[go: up one dir, main page]

Desaraju et al., 2017 - Google Patents

Leveraging experience for computationally efficient adaptive nonlinear model predictive control

Desaraju et al., 2017

View PDF
Document ID
538052621123739280
Author
Desaraju V
Michael N
Publication year
Publication venue
2017 IEEE International Conference on Robotics and Automation (ICRA)

External Links

Snippet

This work presents Experience-driven Predictive Control (EPC) as a fast technique for solving nonlinear model predictive control (NMPC) problems with uncertain system dynamics. EPC leverages an affine dynamics model that is updated online via Locally …
Continue reading at www.cs.cmu.edu (PDF) (other versions)

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
    • G05B13/04—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
    • 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
    • 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
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/39—Robotics, robotics to robotics hand
    • 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
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
    • 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
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
    • 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
    • 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
    • G05B15/00—Systems controlled by a computer
    • G05B15/02—Systems controlled by a computer electric
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models

Similar Documents

Publication Publication Date Title
Rosolia et al. Learning model predictive control for iterative tasks. a data-driven control framework
Chang et al. Stabilizing neural control using self-learned almost lyapunov critics
Carron et al. Data-driven model predictive control for trajectory tracking with a robotic arm
Biglarbegian et al. On the stability of interval type-2 TSK fuzzy logic control systems
Yang et al. Adaptive fuzzy robust tracking controller design via small gain approach and its application
Banerjee Autonomous acquisition of behavior trees for robot control
Wei et al. Safe control with neural network dynamic models
Rosolia et al. Robust learning model-predictive control for linear systems performing iterative tasks
Cheng et al. Improving the Robustness of Reinforcement Learning Policies With ${\mathcal {L} _ {1}} $ Adaptive Control
Desaraju et al. Leveraging experience for computationally efficient adaptive nonlinear model predictive control
US12038727B2 (en) System for performing a task according to a reference trajectory
Westenbroek et al. Feedback linearization for unknown systems via reinforcement learning
Azarbahram et al. Event-triggered tracking formation of networked nonlinear intelligent transportation systems surrounded by random disturbances
Rosolia et al. Minimum time learning model predictive control
Morales et al. LAMDA control approaches applied to trajectory tracking for mobile robots
Friedrich et al. A robust stability approach to robot reinforcement learning based on a parameterization of stabilizing controllers
Beirigo et al. Online TD (A) for discrete-time Markov jump linear systems
Li et al. Moving Obstacle Collision Avoidance via Chance-Constrained MPC with CBF
Desaraju et al. Experience-driven predictive control
Desaraju et al. Fast nonlinear model predictive control via partial enumeration
Yel et al. Fast run-time monitoring, replanning, and recovery for safe autonomous system operations
Hussain et al. Underactuated nonlinear adaptive control approach using U-model for multivariable underwater glider control parameters
Benosman Learning-based adaptive control for nonlinear systems
Duţescu et al. Model predictive control of a nonlinear laboratory twin rotor aero-dynamical system
del Río et al. Adaptive Koopman Model Predictive Control of Simple Serial Robots