CN102637011A - Robust control method for directly driving numerical control platform based on coordinate transformation and parameter adjustment - Google Patents

Abstract

A robust control method based on coordinate transformation and parameter adjustment to directly drive the CNC platform. First, determine the initial phase of the motor mover; Adjust the position; finally execute the robust control algorithm, output the control amount, and drive the CNC platform. The control system adopted by the method of the present invention includes a voltage adjustment circuit, a rectification filter unit, an IPM inverter unit, a DSP, a Hall sensor, a grating scale, a current sampling circuit, a position sampling circuit, and an IPM isolation drive protection circuit. The invention proposes a robust contour controller for the direct drive numerical control platform. Based on a complete coordinate system transformation and parameter adjustment function, which can be applied to any smooth profile curve, it provides robust control system stability against plant modeling errors and disturbances.

Description

Robust control method for direct drive NC platform based on coordinate transformation and parameter adjustment

technical field

The invention belongs to the technical field of numerical control, and relates to a robust control method for directly driving a numerical control platform based on coordinate transformation and parameter adjustment.

Background technique

CNC machine tools are developing in the direction of precision, high speed, compound, intelligence and environmental protection. Precision and high-speed machining put forward higher requirements for transmission and its control, higher dynamic characteristics and control accuracy, higher feed speed and acceleration, lower vibration noise and smaller wear. The direct drive feed system utilizes the characteristic that the linear motor can directly generate linear thrust, and the intermediate transmission links such as the screw nut mechanism are no longer needed in the system composition. The application of linear motor and its drive control technology in the feed drive of machine tools has brought about major changes in the transmission structure of machine tools and made a new leap in the performance of machine tools.

The linear servo system of the CNC machine tool adopts the direct drive method, which eliminates a series of adverse effects caused by the mechanical motion transformation mechanism. Therefore, it has very obvious advantages in the high-precision, fast-response micro-feed servo system. But this also increases the difficulty of control.

Machine tool control systems are generally designed with single-axis feed drive shafts to reduce tracking errors. However, with respect to machining, the quadrature error component of the drive axis is more important to the designed profile than the tracking error for a single-axis feed. Various control methods for reducing contour errors have been proposed so far. Since the profile and tracking error of each feed axis are used to calculate the control input in most methods, there may be a degradation in profile tracking performance. At the same time, it is difficult to adjust the controller parameters considering the profile and tracking error of the drive axis at each feed.

The contour control method of the dual-axis feed drive system based on coordinate transformation converts the tracking error of each drive axis into an orthogonal error component and a tangent error component for the ideal contour curve. Since this method obtains two separate SIMO systems for the orthogonal and tangential directions, the controllers for each direction can be designed independently. However, this simplifies the controller parameter tuning, and the control system stability can only be guaranteed when the velocity dynamics of the dual feed axes and ideal ramp trajectories are matched. A controller that decomposes the contour error into positive and tangential error components for ideal contour trajectories and obtains a non-gouging process for machine tool operations, whereby a working coordinate system is defined at the specified The position and dynamics of the control system are rewritten according to this coordinate system. The working coordinate system axes and orthogonal error components corresponding to the orthogonal and tangential directions of the ideal profile curve are considered as an approximation of the actual profile curve. Although this approach provides control system stability for any profile curve, the difference between the actual profile error and the quadrature error components can lead to a significant profile error.

Contents of the invention

Aiming at the practical problems existing in the existing control technology, the present invention provides a robust control method based on coordinate transformation and parameter adjustment to directly drive the numerical control platform, and a contour control system design based on complete coordinate system transformation and parameter adjustment. Modulo errors and disturbances provide robust control system stability.

In order to compensate the contour error caused by the difference between the actual contour error and the quadrature error component, a contour index method in which the quadrature error component is replaced by a newly defined error signal, a robust contour based on pushback sliding mode control is proposed method. However, new definitions of the upper bounds for the second derivatives of the error signal and disturbance derivatives, including friction, are required in designing robust controllers.

The method of the present invention includes coordinate system conversion and reference adjustment method and robust controller design, the adjustment of control gain reduces the orthogonal error component of the ideal curve more than the tangential error component, and the reference adjustment method makes the error convergence speed of the new coordinate system are adjusted independently, the reference signal is adjusted as shown in Figure 2. The robust controller considers disturbances and plant modeling errors, and only nominal values of plant parameters and upper bounds of disturbance magnitudes are required.

The control system adopted by the method of the present invention includes a voltage adjustment circuit, a rectification filter unit, an IPM inverter unit, a DSP, a Hall sensor, a grating scale, a current sampling circuit, a position sampling circuit, and an IPM isolation drive protection circuit.

The AC voltage is output to the input end of the rectification filter unit, the output end of the rectification filter unit is connected to the IPM inverter unit, the IPM is connected to the motor, the motor body is equipped with a grating scale, the grating scale is connected to the input end of the position sampling circuit, and the Hall sensor collects the motor current The signal is output to the current sampling circuit, the output terminal of the current sampling circuit and the output terminal of the position sampling circuit are connected to the DSP, the output signal of the DSP is sent to the input terminal of the voltage adjustment circuit and the IPM isolation drive protection circuit, and the voltage adjustment circuit adjusts the AC voltage. The isolation drive protection circuit is connected to the IPM inverter unit. The speed and position signals are detected by an incremental photoelectric encoder with a resolution of 400 lines. It generates pulse signals A and B, which are sent to the event capture port of the DSP, and the rotor speed is obtained by using the counting function of the capture port unit. The position is obtained from the Z signal.

The robust control method of the direct drive numerical control platform based on coordinate transformation and parameter adjustment of the present invention, the specific steps are as follows:

Step 1: Determine the initial phase of the motor mover;

The position, speed and current information of the motor mover are collected respectively through the mover position sampling circuit and the current sampling circuit.

Step 2: Carry out coordinate transformation on the contour error and adjust parameters to obtain the position error, that is, calculate the position deviation and judge whether to perform position adjustment. If yes, proceed to step 3; otherwise, perform current adjustment;

Step 2.1: Coordinate Transformation for Contour Errors

Assumptions:

和

可得；(a1) Ideal trajectory r _i and its derivative

and

Available;

和可得；(a2) The inclination θ of the local coordinate system ∑ _l and its derivative

and Available;

(a3) the feed drive system position x and its derivatives are measurable;

(a4) For 2 device parameter values, all are available;

According to the above assumptions, the following control is determined, including the input voltage v of the drive shaft motor, matrix H, E and I _e :

$\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; diag</span>}<span\; class="notranslate">\; \{</span>\frac{{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\frac{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}}<span\; class="notranslate">\; \}</span><span\; class="notranslate">\; ,</span>$

$\mathrm{<span\; class="notranslate">\; E.</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; diag</span>}<span\; class="notranslate">\; \{</span>\frac{{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\frac{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}}<span\; class="notranslate">\; \}</span><span\; class="notranslate">\; ,</span>$

${\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; =</span>\left[\begin{array}{cc}\mathrm{<span\; class="notranslate">\; 0</span>}& \mathrm{<span\; class="notranslate">\; 1</span>}\\ <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; 0</span>}\end{array}\right]<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

Where m _i (>0), _ci (≥0) and f _i are the mass, viscous friction coefficient and driving force of the drive axis i, respectively, where K _v and K _p are the velocity and position feedback gain matrices. They are assumed to be diagonal matrices with only positive elements. The following equation holds:

Since matrices H and R are non-singular, the following relation needs to be satisfied for realizing equation (2):

From the above equations, it is obtained that e _l approaches 0 as t approaches infinity by properly assigning the feedback gain matrices K _v and K _p in equation (3). In addition, the controller can adjust the error convergence speed for each axis of ∑ _l in Fig. 1. If the feedback gain for e _l2 is set larger than that for e _l1 , the contour error can be reduced somewhat faster than the tangent tracking error for the ideal curve. n _i is the inertia of the motor driving the feed drive system; _ki is the torque-voltage conversion rate; pi is the node of the ball screw; ki is the torque-voltage conversion rate.

Step 2.2: Parameter adjustment, as follows:

In order to reduce the tangential error component e _l1 and increase the stability of the control system, the controller is expected to have a small feedback gain, a large tracking error e _l1 may produce a large gap between the quadrature error component e _l2 and the actual contour error e _c difference. In this case, even if e _l2 can be converged to zero contour error e _c is no longer suppressed.

This parameter adjustment method slightly moves the ideal position and the local coordinate system to reduce the difference between the orthogonal error and the actual contour error. Because a tracking error exists along l ₁ , there may be an inconsistency between the error e _l2 and e _c Accept the difference. One solution to reduce this difference is to estimate the closest position from the ideal profile curve to the actual position x, denoted by r _a . However, if the ideal profile curve is complex, it is difficult to accurately calculate the nearest position in real time. Also, if the ideal profile curve is concave with respect to x, several closest positions to the actual position x may exist. Even if the nearest position is estimated, the proposed method for the local coordinate system of the nearest position cannot be applied to the error, because the tracking error along the ideal contour curve is always approximately zero, and tracking along the ideal contour curve is no longer possible. In this way, a local coordinate system is generated whose orientation is similar to that of the closest position and whose origin is close to the ideal position of the ideal profile curve, r _n and ∑ _n . Replace the rotation matrix R and the tracking errors e _w and e _l in equation (1) with the new definition system ∑ _n , resulting in the difference between e _l2 and e _c . Next, the method of generating the series Σ _n will be described.

Assume that the tracking error along _l1 has a negative value (eg e _l1 is negative), since the controller gain assigned to reduce e _l1 is normally smaller than that assigned to e _l2 . It is also assumed that the distance between the ideal position r and the closest point r _a along the ideal contour curve is approximately equal to the magnitude of the tracking error e _l1 . Furthermore, the ideal velocity along this segment is close to constant. Then, the time t _d required to pass through this period can be estimated as follows:

The origin (r _a ) and inclination (θ _a ) in the coordinate system Σ _a in Fig. 2, which are the closest positions to x on the ideal contour curve, can be estimated as follows:

r _a =r(tt _d ), θ _a =θ(tt _d ) (5)

where r() and θ() indicate functions of time. The modified ideal position r _n is represented in Fig. 2 as:

r _n =r+R _a d _r , d _r =[0,-d _ra2 ] ^T (6)

Where R _a is the rotation matrix from Σ _a to Σ _w in Figure 2, which is a function of θ _a . The control input equation (1) is replaced by:

where e _wn =xr _n . Thus, the following control dynamics are obtained, and the convergence speeds of e _n1 and e _n2 can be adjusted independently:

Step 3: Execute the robust control algorithm, output the control variable v, and drive the CNC platform.

In actual machining, disturbances including nonlinear friction and cutting force as well as equipment modeling errors do exist, therefore, the controller equation should be extended to consider disturbances and equipment modeling errors. Feed drive dynamics with bounded disturbance vector w.

Present the following controller:

$\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; h</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; diag</span>}<span\; class="notranslate">\; \{</span>\frac{{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; m</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; no</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; p</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\frac{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; k</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; p</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}}<span\; class="notranslate">\; \}</span>$

$\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; E.</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; diag</span>}<span\; class="notranslate">\; \{</span>\frac{{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; c</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; d</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; p</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\frac{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; k</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; p</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}}<span\; class="notranslate">\; \}</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 10</span>}<span\; class="notranslate">\; )</span>$

(其中Λ是一个对角矩阵和只有正元素的常量)被采用作为后续稳定性分析的代替

的速度信号。符号δv是实现鲁棒控制的一个输入矢量。通过近似分配等式反馈增益矩阵K_v、K_p和Λ，当t趋近于无穷时能得到e_n趋近于零。此外，沿着图2中∑_n每个轴的误差收敛速度能被独立调整。in Indicates the nominal value of z, the signal

(where Λ is a diagonal matrix and a constant with only positive elements) was adopted as a substitute for subsequent stability analysis

speed signal. The symbol δv is an input vector for robust control. By approximately assigning the equal feedback gain matrices K _v , K _p and Λ, it can be obtained that e _n approaches zero as t approaches infinity. In addition, the error convergence speed along each axis of Σ _n in Fig. 2 can be adjusted independently.

Robust control algorithms ensure control system stability even when plant modeling errors and disturbances exist. Robust control is obtained by the following equation:

$\mathrm{\&delta;v}=-\frac{\mathrm{\&eta;}{R}_a{e}_v}{\mathrm{\&rho;}},$

where ||a|| is the Euclidean norm of a, and ε is a small positive constant.

In order to show the robust stability of the proposed system, a Lyapunov function candidate is applied.

也是一致最终有界。因此，虽然过大的值会降低控制性能，提出的控制器能提高鲁棒稳定性。It is concluded that e _v and e _n are uniformly ultimately bounded. So, from

It is also uniformly ultimately bounded. Therefore, although an excessively large value will degrade the control performance, the proposed controller can improve robust stability.

Beneficial effects: A robust contour controller is proposed for the direct drive CNC platform. This method, based on a complete coordinate transformation and parameter adjustment function, can be applied to any smooth profile curve, providing robust control system stability against plant modeling errors and disturbances. The effectiveness of the robust profile control system accounts for large modeling errors in the generalized inertia of the feed drive system, and future applications develop methods that can achieve sub-micron accuracy and extend to 3-5 axis cutting.

Description of drawings

Fig. 1 is a block diagram of the control system of the embodiment of the present invention;

Fig. 2 is a schematic diagram of parameter adjustment in an embodiment of the present invention;

Fig. 3 is a block diagram of the hardware structure of the control system for realizing the embodiment of the present invention;

4 is a schematic diagram of the main circuit of the motor control system according to the embodiment of the present invention;

Fig. 5 is A, B phase current sampling circuit schematic diagram of the present invention;

6 is a schematic diagram of a grating ruler signal sampling circuit according to an embodiment of the present invention;

7 is a schematic diagram of an IPM hardware drive circuit according to an embodiment of the present invention;

Fig. 8 is a flow chart of the program of the vector control system in the control method of the embodiment of the present invention;

FIG. 9 is a flow chart of a sub-control program for position adjustment interruption processing of the control method according to an embodiment of the present invention.

Detailed ways

The present invention will be further described below in conjunction with the accompanying drawings.

The present invention is applied to an X-Y numerical control stage with a linear drive motor, the position of the stage is connected to a linear encoder of each drive axis, and the sensor resolution of the linear encoder is 0.1 micron. The speed of each drive shaft is calculated from the reverse difference of the position measurement, and the sampling period is 2 ms.

(1) System hardware structure

Based on coordinate transformation and parameter adjustment, the robust control system directly drives the CNC platform, including voltage adjustment circuit, rectification and filtering unit, IPM inverter unit, DSP, Hall sensor, grating scale, current sampling circuit, position sampling circuit, IPM isolation drive protection circuit . The structure of the system is shown in Figure 3. The voltage adjustment circuit adopts the reverse voltage regulation module EUV-25A-II, which can realize 0-220V isolation voltage regulation. The rectification filter unit adopts bridge type uncontrollable rectification, large capacitor filter, and appropriate resistance-capacity absorption circuit, which can obtain the constant DC voltage required for IPM work. The IPM uses Fuji 6MBP50RA060 intelligent power module, with a withstand voltage of 600V, a maximum current of 50A, and a maximum operating frequency of 20kHz. The IPM is powered by four independent 15V drive power supplies. The main power input terminals (P, N), output terminals (U, V, W), and the main terminals are fixed with their own screws, which can realize current transmission. P and N are the input terminals of the main power supply after rectification, transformation and smoothing of the inverter. P is the positive terminal and N is the negative terminal. The three-phase alternating current output by the inverter is connected to the motor through the output terminals U, V, and W. The schematic diagram of the main circuit of the control system is shown in Figure 4.

DSP selects TMS320F2812 processor, and its supporting development board includes target read-only memory, analog interface, eCAN interface, serial boot ROM, user indicator light, reset circuit, asynchronous serial port that can be configured as RS232/RS422/RS485, and SPI synchronous serial port And off-chip 256K*16-bit RAM.

The current sampling in the control system adopts the Hall current sensor LT58-S7 of LEM Company. The A and B phase currents are detected by two Hall current sensors to obtain current signals, which are converted into 0-3.3V voltage signals through the current sampling circuit, and finally converted into binary numbers with 12-bit precision by the A/D conversion module of TMS320LF2812 , and stored in the value register. A, B-phase current sampling circuit shown in Figure 5. The adjustable resistor VR2 adjusts the signal amplitude, and the adjustable resistor VR1 adjusts the signal offset. By adjusting these two resistors, the signal can be adjusted to 0~3.3V, and then sent to the AD0 and AD1 pins of the DSP. . The regulator tube in the figure is to prevent the signal sent to the DSP from exceeding 3.3V, causing the DSP to be damaged by high voltage. The operational amplifier adopts OP27, the power supply is connected to positive and negative 15V voltage, and the decoupling capacitor is indirect between the voltage and the ground. The input terminal of the circuit is connected with a capacitor filter to remove high-frequency signal interference and improve sampling accuracy.

引脚，以确保IPM发生故障时DSP及时将所有事件管理器输出脚置高阻态。The A-phase and B-phase pulse signals output by the grating ruler should be isolated by a fast optocoupler 6N137, and then the signal level will be converted from 5V to 3.3V by a voltage divider circuit, and finally connected to the two-way orthogonal encoding pulse interface of DSP QEP1 and QEP2. The circuit principle is shown in Figure 6. Figure 7 shows the schematic diagram of the six-way isolation drive circuit. It should be pointed out that the IPM fault protection signal is aimed at non-repetitive transient faults, which are realized in this system through the following measures: the fault output signal of IPM is connected to the DSP through optical coupling

pins, to ensure that the DSP puts all event manager output pins in a high-impedance state in time when the IPM fails.

(2) The robust control method of the direct drive numerical control platform based on coordinate transformation and parameter adjustment of the present invention, the specific steps are as follows:

Step 1: Determine the initial phase of the motor mover;

The position, speed and current information of the motor mover are collected respectively through the mover position sampling circuit and the current sampling circuit.

Step 2: Carry out coordinate transformation on the contour error and adjust parameters to obtain the position error, that is, calculate the position deviation and judge whether to perform position adjustment. If yes, proceed to step 3; otherwise, perform current adjustment;

Step 2.1: Coordinate Transformation for Contour Errors

Assumptions:

和

可得；(a1) Ideal trajectory r _i and its derivative

and

Available;

和

可得；(a2) The inclination θ of the local coordinate system ∑ _l and its derivative

and

Available;

(a3) the feed drive system position x and its derivatives are measurable;

(a4) For 2 device parameter values, all are available;

According to the above assumptions, the following control is determined, including the input voltage v of the drive shaft motor, matrix H, E and I _e :

$\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; diag</span>}<span\; class="notranslate">\; \{</span>\frac{{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\frac{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}}<span\; class="notranslate">\; \}</span><span\; class="notranslate">\; ,</span>$

$\mathrm{<span\; class="notranslate">\; E.</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; diag</span>}<span\; class="notranslate">\; \{</span>\frac{{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\frac{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}}<span\; class="notranslate">\; \}</span><span\; class="notranslate">\; ,</span>$

${\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; =</span>\left[\begin{array}{cc}\mathrm{<span\; class="notranslate">\; 0</span>}& \mathrm{<span\; class="notranslate">\; 1</span>}\\ <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; 0</span>}\end{array}\right]<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

Where m _i (>0), _ci (≥0) and f _i are the mass, viscous friction coefficient and driving force of the drive axis i, respectively, where K _v and K _p are the velocity and position feedback gain matrices. They are assumed to be diagonal matrices with only positive elements. The following equation holds:

Since matrices H and R are non-singular, the following relation needs to be satisfied for realizing equation (2):

From the above equations, it is obtained that e _l approaches 0 as t approaches infinity by properly assigning the feedback gain matrices K _v and K _p in equation (3). In addition, the controller can adjust the error convergence speed for each axis of ∑ _l in Fig. 1. If the feedback gain for e _l2 is set larger than that for e _l1 , the contour error can be reduced somewhat faster than the tangent tracking error for the ideal curve. n _i is the inertia of the motor driving the feed drive system; _ki is the torque-voltage conversion rate; pi is the node of the ball screw; ki is the torque-voltage conversion rate.

Step 2.2: Parameter adjustment, as follows:

In order to reduce the tangential error component e _l1 and increase the stability of the control system, the controller is expected to have a small feedback gain, a large tracking error e _l1 may produce a large gap between the quadrature error component e _l2 and the actual contour error e _c difference. In this case, even if e _l2 can be converged to zero contour error e _c is no longer suppressed.

This parameter adjustment method slightly moves the ideal position and the local coordinate system to reduce the difference between the orthogonal error and the actual contour error. Because a tracking error exists along l ₁ , there may be an inconsistency between the error e _l2 and e _c Accept the difference. One solution to reduce this difference is to estimate the closest position from the ideal profile curve to the actual position x, denoted by r _a . However, if the ideal profile curve is complex, it is difficult to accurately calculate the nearest position in real time. Also, if the ideal profile curve is concave with respect to x, several closest positions to the actual position x may exist. Even if the nearest position is estimated, the proposed method for the local coordinate system of the nearest position cannot be applied to the error, because the tracking error along the ideal contour curve is always approximately zero, and tracking along the ideal contour curve is no longer possible. In this way, a local coordinate system is generated whose orientation is similar to that of the closest position and whose origin is close to the ideal position of the ideal profile curve, r _n and ∑ _n . Replace the rotation matrix R and the tracking errors e _w and e _l in equation (1) with the new definition system ∑ _n , resulting in the difference between e _l2 and e _c . Next, the method of generating the series Σ _n will be described.

Assume that the tracking error along _l1 has a negative value (eg e _l1 is negative), since the controller gain assigned to reduce e _l1 is normally smaller than that assigned to e _l2 . It is also assumed that the distance between the ideal position r and the closest point r _a along the ideal contour curve is approximately equal to the magnitude of the tracking error e _l1 . Furthermore, the ideal velocity along this segment is close to constant. Then, the time t _d required to pass through this period can be estimated as follows:

The origin (r _a ) and inclination (θ _a ) in the coordinate system Σ _a in Fig. 2, which are the closest positions to x on the ideal contour curve, can be estimated as follows:

r _a =r(tt _d ), θ _a =θ(tt _d ) (5)

where r() and θ() indicate functions of time. The modified ideal position r _n is represented in Fig. 2 as:

r _n =r+R _a d _r , d _r =[0,-d _ra2 ] ^T (6)

Where R _a is the rotation matrix from Σ _a to Σ _w in Figure 2, which is a function of θ _a . The control input equation (1) is replaced by:

where e _wn =xr _n . Thus, the following control dynamics are obtained, and the convergence speeds of e _n1 and e _n2 can be adjusted independently:

Step 3: Execute the robust control algorithm, output the control variable v, and drive the CNC platform.

In actual machining, disturbances including nonlinear friction and cutting force as well as equipment modeling errors do exist, therefore, the controller equation should be extended to consider disturbances and equipment modeling errors. Feed drive dynamics with bounded disturbance vector w.

Present the following controller:

$\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; h</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; diag</span>}<span\; class="notranslate">\; \{</span>\frac{{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; m</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; no</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; p</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\frac{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; k</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; p</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}}<span\; class="notranslate">\; \}</span>$

$\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; E.</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; diag</span>}<span\; class="notranslate">\; \{</span>\frac{{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; c</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; d</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; p</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\frac{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; k</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; p</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}}<span\; class="notranslate">\; \}</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 10</span>}<span\; class="notranslate">\; )</span>$

表示z的标称值，信号

(其中Λ是一个对角矩阵和只有正元素的常量)被采用作为后续稳定性分析的代替

的速度信号。符号δv是实现鲁棒控制的一个输入矢量。通过近似分配等式反馈增益矩阵K_v、K_p和Λ，当t趋近于无穷时能得到e_n趋近于零。此外，沿着图2中∑_n每个轴的误差收敛速度能被独立调整。in

Indicates the nominal value of z, the signal

(where Λ is a diagonal matrix and a constant with only positive elements) was adopted as a substitute for subsequent stability analysis

speed signal. The symbol δv is an input vector for robust control. By approximately assigning the equal feedback gain matrices K _v , K _p and Λ, it can be obtained that e _n approaches zero as t approaches infinity. In addition, the error convergence speed along each axis of Σ _n in Fig. 2 can be adjusted independently.

Robust control algorithms ensure control system stability even when plant modeling errors and disturbances exist. Robust control is obtained by the following equation:

$\mathrm{\&delta;\; v}=-\frac{\mathrm{\&eta;}{R}_a{e}_v}{\mathrm{\&rho;}},$

where ||a|| is the Euclidean norm of a, and ε is a small positive constant.

In order to show the robust stability of the proposed system, a Lyapunov function candidate is applied.

也是一致最终有界。因此，虽然过大的值会降低控制性能，提出的控制器能提高鲁棒稳定性。It is concluded that e _v and e _n are uniformly ultimately bounded. So, from

It is also uniformly ultimately bounded. Therefore, although an excessively large value will degrade the control performance, the proposed controller can improve robust stability.

(3) System software implementation

The program flowchart of the vector control system in the method of the present invention is shown in FIG. 4 . The main program of the software includes system initialization; opening INT1, INT2 interrupt; allowing timer interrupt; timer interrupt processing subroutine. The initialization procedure includes closing all interrupts, DSP system initialization, variable initialization, event manager initialization, AD initialization and quadrature encoding pulse QEP initialization. The interrupt service subroutine includes the protection interrupt subroutine and the T1 underflow interrupt service subroutine. Other parts such as mover initial positioning, PID adjustment, vector conversion, etc. are all executed in the timer TI underflow interrupt processing subroutine.

一旦有异常情况发生，DSP会进入保护中断子程序，首先禁止所有中断，然后封锁PWM输出使得电机马上停转，起到保护电机和IPM的作用。The protection interrupt response generated by the IPM protection signal is an external interrupt, and the INT1 interrupt priority is higher than that of the timer T1. IPM will automatically send a protection signal in abnormal conditions such as overcurrent and overvoltage, and this signal is converted and connected to the power drive protection pin of DSP

Once an abnormal situation occurs, the DSP will enter the protection interrupt subroutine, first prohibit all interrupts, and then block the PWM output to stop the motor immediately, protecting the motor and IPM.

To start the vector control system smoothly, it is necessary to know the initial position of the mover. Using the software, a DC current with a constant amplitude can be passed through the mover of the motor to make the stator generate a constant magnetic field. This magnetic field interacts with the constant magnetic field of the rotor. The motor mover moves to the position where the two flux linkages coincide. The initial positioning of the mover, the reading of AD sampling values, the calculation of the position of the motor mover, coordinate transformation, PID adjustment, and the generation of SVPWM waveform comparison values are all completed in the T1 underflow interrupt service subroutine.

Complete all vector control algorithms in the T1 underflow interrupt subroutine. The process is shown in Figure 9. After entering the interrupt, first judge whether the mover has completed the initial positioning, if the initial positioning has been completed, the program first starts AD conversion, and collects the current value sent back by the hardware into the DSP. The collected data is firstly stored in the respective result registers (RESUTLx, x=0, 1), read out the A-phase and B-phase current values from the result registers RESULT0 and RESULT1 and transform them into Q15 format to obtain the C-phase current, and then Coordinate transformation is performed on the three-phase current to obtain the two-phase current in the static coordinate system.

The timer T2 adopts the continuous counting method, and counts the pulses of the photoelectric encoder through the QEP1 and QEP2 two-phase quadrature encoding pulse circuits, and the actual position of the motor can be obtained through calculation. The current movement direction of the mover can be judged by reading the counting direction bit. The actual measured position and the given position are taken as the input of the position regulator, and the output of the position regulator is taken as the given amount of the q-axis current. The resolution of the encoder is 1 micron, and since the pole distance of the stator of the linear motor is 32mm, the period register of the timer T2 is set to 32000. Whenever the movement distance of the mover reaches a pole distance, the timer T2 interrupt flag is set, and T2 restarts counting from 0 at the same time. After reading the new count value, the timer T2 interrupt flag bit needs to be cleared. The maximum speed achieved in the experiment is small. When the calculated difference between the count value of this cycle and the count value of the previous cycle is greater than 5000, it means that the counter has overflowed or underflowed once, and the actual position of the motor is calculated accordingly. The requirements of the position loop on the response speed are far lower than the requirements of the current loop on the response speed. Therefore, it is stipulated that the AC and D axis currents must be adjusted every time the T1 underflow interruption is interrupted, and the position PI adjustment is only performed every 10 interruptions.

Vector control directly completes the adjustment of the q-axis current of the mover through coordinate transformation, and changes the thrust of the mover. The process is shown in Figure 8. Through coordinate transformation and space vector calculation, and according to the two vectors and the zero vector in the six basic voltage vectors determined by the switching voltage vector table, the values of the three comparison registers CMPR1, CMPR2 and CMPR3 are calculated accordingly. Assign it to the corresponding register to get the desired SVPWM wave, and drive the IPM to control the operation of the linear motor.

The inventive method is finally realized by the control program embedded in the DSP processor, and its control process is carried out in the following steps:

Step 1 system initialization;

Step 2 allows TN1, TN2 interrupt;

Step 3 starts T1 underflow interrupt;

Step 4 program data initialization;

Step 5 enable total interruption;

Step 6 interrupt waiting;

Step 7TN1 interrupt processing sub-control program;

Step 8 ends;

Wherein in the step 6, the position adjustment interrupt processing sub-control program follows the steps below:

Step 6-1 position adjustment interruption sub-control program;

Step 6-2 read the encoder value;

Step 6-3 judges the angle;

Step 6-4 calculates the distance traveled;

Step 6-5 executes the position controller;

Steps 6-6 execute the robust controller to compensate for external disturbances;

Steps 6-7 calculate and output the current command;

Step 6-8 interrupt return;

Wherein in the step 7, the T1 interrupt processing sub-control program follows the steps below:

Step 7-1T1 interrupt sub-control program;

Step 7-2 protect the site;

Step 7-3 judges whether the initial positioning has been performed; if yes, enter step 4, otherwise enter step 10;

Step 7-4 current sampling, CLARK transformation, PARK transformation;

Step 7-5 judges whether position adjustment is needed; otherwise, enter step 9;

Step 7-6 position adjustment interrupt processing sub-control program;

Step 7-7 Position Control Deviation Calculation

Step 7-8 Control volume output adjustment

Steps 7-9 dq-axis current regulation;

Steps 7-10PARK inverse transformation;

Steps 7-11 calculate CMPPx and PWM output;

Step 7-12 position sampling;

Steps 7-13 initial positioning procedure;

Steps 7-14 restore the scene;

Steps 7-15 interrupt return.

Claims (4)

1. one kind directly drives the digital control platform robust control method based on coordinate transform and parameter adjustment, and it is characterized in that: concrete steps are following:

Step 1: the initial phase of confirming electric mover;

Gather electric mover position, speed and current information respectively through rotor position sample circuit and current sampling circuit;

Step 2: profile errors is carried out coordinate transform, and the line parameter of going forward side by side adjustment obtains site error, promptly carries out position deviation and calculates, and judges whether to carry out position adjustments, is then carry out step 3, otherwise carries out Current Regulation;

Step 2.1: profile errors is carried out coordinate transform

Suppose:

(a1) ideal trajectory r _iWith its derivative

With

Can get;

(a2) local coordinate system ∑ _lInclination angle theta and its derivative

With

Can get;

(a3) feed drive system position x and its derivative are measurable;

(a4) for 2 device parameter values, all can get;

Confirm following control according to above-mentioned hypothesis, comprise input voltage v, matrix H, E and the I of driving shaft motor _e:

$H=\mathrm{diag}\left\{\frac{m_i+n_i{\left(\frac{2\mathrm{\&pi;}}{p_i}\right)}^2}{\frac{2\mathrm{\&pi;}}{p_i}}\right\},$

$E=\mathrm{diag}\left\{\frac{c_i+d_i{\left(\frac{2\mathrm{\&pi;}}{p_i}\right)}^2}{\frac{2\mathrm{\&pi;}{k}_i}{p_i}}\right\},$

$I_e=\left[\begin{array}{cc}0& 1\\ -1& 0\end{array}\right]---\left(1\right)$

M wherein _i(＞0), c _i(>=0) and f _iBe respectively driving shaft i table quality, viscous friction coefficient and driving force, wherein K _vAnd K _pBe speed and position feedback gain matrix; They are had only the diagonal matrix of positive element by hypothesis, and following equality is set up:

Because matrix H and R are nonsingular, following relation of plane is for realizing that equality (2) need be satisfied:

From top equality, through feedback gain matrix K in the appropriate allocation equality (3) _vAnd K _pReach a conclusion, when t levels off to when infinite e _lLevel off to and 0 can get, if for e _L2Feedback gain is set up greater than e _L1, for ideal curve, profile errors can be reduced sooner than tangent line tracking error, n _iFor driving the motor inertia of feed drive system; k _iBe torque-voltage transitions rate; p _iIt is the joint of ball-screw; k _iBe torque-voltage transitions rate;

Step 2.2: parameter adjustment, specific as follows:

Move ideal position and local coordinate system, reduce the different of quadrature error and TP error, because a tracking error is along l ₁Exist, in error e _L2And e _cBetween possibly have a unacceptable difference, estimate nearest position at desirable contour curve to physical location x for reducing this difference, use r _aExpression; A local coordinate system is produced, and its direction is similar with the coordinate system of proximal most position, and its initial point is near the ideal position of desirable contour curve, r _nAnd ∑ _nWith redetermination is ∑ _nReplace rotation matrix R and tracking error e in the equality (1) _wAnd e _l, produce e _L2And e _cDifference, to produce be ∑ in explanation below _nMethod:

Suppose along l ₁Tracking error a negative value is arranged because for reducing e _L1The controller gain that distributes is normal than being e _L2That distributes is smaller, also supposes ideal position r and along desirable contour curve closest approach r _aBetween distance and tracking error e _L1Big or small approximately equivalent, in addition, along the ideal velocity of this section near constant, so, require time t through this section _dCan be estimated by following:

The coordinate system ∑ _aMiddle initial point r _aAnd inclination angle theta _a, be in the proximal most position of desirable contour curve to x, can be estimated by following:

r _a＝r(t-t _d)，θ _a＝θ(t-t _d) (5)

Wherein r () and θ () show the function of time;

The ideal position r that revises _nBe expressed as:

r _n＝r+R _ad _r，d _r＝[0，-d _ra2] ^T (6)

R wherein _aIt is ∑ _aTo ∑ _wRotation matrix, be θ _aA function, control input equality (1) is replaced by following formula:

E wherein _Wn=x-r _n

So, the control power below obtaining, e _N1And e _N2Speed of convergence can be adjusted independently:

Step 3: carry out the robust control algorithm, output controlled quentity controlled variable v drives digital control platform;

In the actual processing, comprise that the interference of non-linear friction and cutting force and equipment modeling mistake exists really, therefore, the controller equality should be expanded and consider to disturb and the equipment modeling error, and the feeding that contains BOUNDED DISTURBANCES vector w drives dynamics,

Controller below proposing:

$\hat{H}=\mathrm{diag}\left\{\frac{{\hat{m}}_i+{\hat{n}}_i{\left(\frac{2\mathrm{\&pi;}}{{\hat{p}}_i}\right)}^2}{\frac{2\mathrm{\&pi;}{\hat{k}}_i}{{\hat{p}}_i}}\right\}$

$\hat{E}=\mathrm{diag}\left\{\frac{{\hat{c}}_i+{\hat{d}}_i{\left(\frac{2\mathrm{\&pi;}}{{\hat{p}}_i}\right)}^2}{\frac{2\mathrm{\&pi;}{\hat{k}}_i}{{\hat{p}}_i}}\right\}---\left(10\right)$

Wherein

The nominal value of expression z, signal Λ is a diagonal matrix and the constant that has only positive element, signal e in the formula _vBe used replacement as follow-up stability analysis

Rate signal, symbol δ v is an input vector realizing robust control;

Through approximate distribution equations feedback gain matrix K _v, K _pAnd Λ, when leveling off to, t can obtain e when infinite _nLevel off to zero; In addition, along ∑ _nThe error convergence speed of each can be by independent adjustment;

Robust control obtains following equality:

$\mathrm{\&delta;v}=-\frac{\mathrm{\&eta;}{R}_a{e}_v}{\mathrm{\&rho;}},$

Wherein || a|| is the Euclid norm of a, and ε is little positive constant;

In order to show the robust stability of proposition system, Leah Pu Nuofu function the candidate be employed;

E reaches a conclusion _vAnd e _nBe consistent final bounded, so, from Also be consistent final bounded.

2. according to claim 1ly directly drive the digital control platform robust control method based on coordinate transform and parameter adjustment, it is characterized in that: this method is realized by the control program that embeds among the DSP, carries out according to the following steps:

Step 1 system initialization;

Step 2 allows TN1, TN2 to interrupt;

Step 3 starts the T1 underflow and interrupts;

The initialization of step 4 routine data;

Step 5 is opened total interruption;

Step 6 interrupt latency;

The sub-control program of step 7TN1 Interrupt Process;

Step 8 finishes;

The sub-control program of position adjustments Interrupt Process is according to the following steps in the said step 6:

Step 6-1 position adjustments is interrupted sub-control program;

Step 6-2 reads encoder values;

Step 6-3 judges angle;

Step 6-4 calculates and has walked distance;

Step 6-5 executing location controller;

Step 6-6 carries out robust controller compensation external disturbance;

Step 6-7 calculates current order and output;

Step 6-8 interrupts returning.

3. according to claim 2ly directly drive the digital control platform robust control method based on coordinate transform and parameter adjustment, it is characterized in that: the sub-control program of T1 Interrupt Process is according to the following steps in the said step 7:

Step 7-1T1 interrupts sub-control program;

Step 7-2 keeps the scene intact;

Step 7-3 judges whether initial alignment; Be to get into step 4, otherwise get into step 10;

Step 7-4 current sample, CLARK conversion, PARK conversion;

Step 7-5 need to judge whether position adjustments; Otherwise get into step 9;

The sub-control program of step 7-6 position adjustments Interrupt Process;

Step 7-7 position control deviation calculation

The output of step 7-8 controlled quentity controlled variable is regulated

Step 7-9dq shaft current is regulated;

Step 7-10PARK inverse transformation;

Step 7-11 calculates CMPPx and PWM output;

Step 7-12 position sampling;

Step 7-13 initial alignment program;

It is on-the-spot that step 7-14 recovers;

Step 15 interrupts returning.

4. as claimed in claim 1ly directly drive the control system that the digital control platform robust control method is adopted, it is characterized in that: comprise voltage-regulating circuit, rectification filtering unit, IPM inversion unit, DSP, Hall element, grating chi, current sampling circuit, position sampling circuit, IPM isolation drive holding circuit based on coordinate transform and parameter adjustment;

Alternating voltage exports the rectification filtering unit input end to, and the rectification filtering unit output terminal inserts the IPM inversion unit, and IPM links to each other with motor; The motor fuselage is equipped with the grating chi; Grating chi link position sample circuit input end, Hall element is gathered motor current signal, exports current sampling circuit to; Current sampling circuit output terminal and position sampling circuit output end all insert DSP; DSP outputs signal to voltage-regulating circuit input end and IPM isolation drive holding circuit, and voltage-regulating circuit is adjusted alternating voltage, and IPM isolation drive holding circuit inserts the IPM inversion unit.

Images