Mon et al., 2014 - Google Patents
Double inverted pendulum decoupling control by adaptive terminal sliding-mode recurrent fuzzy neural networkMon et al., 2014
- Document ID
- 11178245200322269002
- Author
- Mon Y
- Lin C
- Publication year
- Publication venue
- Journal of Intelligent & Fuzzy Systems
External Links
Snippet
An adaptive terminal sliding-mode recurrent fuzzy neural network (ATSRFNN) control system is developed to control a coupled double inverted pendulum system. The proposed ATSRFNN control system is composed of a recurrent fuzzy neural network (RFNN) controller …
- 230000001537 neural 0 title abstract description 26
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/04—Architectures, e.g. interconnection topology
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B17/00—Systems involving the use of models or simulators of said systems
- G05B17/02—Systems involving the use of models or simulators of said systems electric
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B11/00—Automatic controllers
- G05B11/01—Automatic controllers electric
- G05B11/32—Automatic controllers electric with inputs from more than one sensing element; with outputs to more than one correcting element
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Dai et al. | Robust model predictive tracking control for robot manipulators with disturbances | |
| Chang et al. | Stabilizing neural control using self-learned almost lyapunov critics | |
| Wai et al. | Robust neural-fuzzy-network control for robot manipulator including actuator dynamics | |
| Chen et al. | Recurrent neural network-based robust nonsingular sliding mode control with input saturation for a non-holonomic spherical robot | |
| Li et al. | Adaptive fuzzy logic control of dynamic balance and motion for wheeled inverted pendulums | |
| Hsu et al. | Supervisory adaptive dynamic RBF-based neural-fuzzy control system design for unknown nonlinear systems | |
| Wu et al. | Safety-critical control of a 3d quadrotor with range-limited sensing | |
| Asad et al. | Backstepping-based recurrent type-2 fuzzy sliding mode control for MIMO systems (MEMS triaxial gyroscope case study) | |
| Guechi et al. | PDC control design for non-holonomic wheeled mobile robots with delayed outputs | |
| Roy et al. | Grey wolf optimization-based second order sliding mode control for inchworm robot | |
| Wang et al. | Neural learning control of flexible joint manipulator with predefined tracking performance and application to baxter robot | |
| Jung | Stability analysis of reference compensation technique for controlling robot manipulators by neural network | |
| Yen et al. | Adaptive neural network based tracking control for electrically driven flexible-joint robots without velocity measurements | |
| Mon et al. | Double inverted pendulum decoupling control by adaptive terminal sliding-mode recurrent fuzzy neural network | |
| Miao et al. | Robust dynamic surface control of flexible joint robots using recurrent neural networks | |
| Ngo et al. | Robust adaptive self-organizing wavelet fuzzy CMAC tracking control for de-icing robot manipulator | |
| Tsai et al. | Decentralized cooperative transportation with obstacle avoidance using fuzzy wavelet neural networks for uncertain networked omnidirectional multi-robots | |
| Lin et al. | Hybrid adaptive fuzzy controllers with application to robotic systems | |
| Li et al. | Weighted Multiple‐Model Neural Network Adaptive Control for Robotic Manipulators With Jumping Parameters | |
| Hussain et al. | Underactuated nonlinear adaptive control approach using U-model for multivariable underwater glider control parameters | |
| Sun et al. | Adaptive tracking control of mobile manipulators with affine constraints and under-actuated joints | |
| Chi et al. | A new neural network-based adaptive ILC for nonlinear discrete-time systems with dead zone scheme | |
| Fei et al. | Adaptive global fast terminal sliding mode control of MEMS gyroscope using fuzzy-neural-network | |
| Purwar et al. | Neuro sliding mode control of robotic manipulators | |
| Lin | Fuzzy-Basis-Function-Network-Based $ H_\infty $ Tracking Control for Robotic Manipulators Using Only Position Feedback |