[go: up one dir, main page]

Choi et al., 2020 - Google Patents

Reinforcement learning for safety-critical control under model uncertainty, using control lyapunov functions and control barrier functions

Choi et al., 2020

View PDF
Document ID
12439371586503751518
Author
Choi J
Castaneda F
Tomlin C
Sreenath K
Publication year
Publication venue
arXiv preprint arXiv:2004.07584

External Links

Snippet

In this paper, the issue of model uncertainty in safety-critical control is addressed with a data- driven approach. For this purpose, we utilize the structure of an input-ouput linearization controller based on a nominal model along with a Control Barrier Function and Control …
Continue reading at arxiv.org (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
    • 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
    • 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
    • G06N3/08—Learning methods
    • 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
    • G05B13/0205—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric not using a model or a simulator of the controlled system
    • G05B13/021—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric not using a model or a simulator of the controlled system in which a variable is automatically adjusted to optimise the performance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50—Computer-aided design
    • G06F17/5009—Computer-aided design using simulation
    • 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
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/04—Inference methods or devices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • G06F17/13—Differential equations

Similar Documents

Publication Publication Date Title
Choi et al. Reinforcement learning for safety-critical control under model uncertainty, using control lyapunov functions and control barrier functions
Liu et al. Reinforcement-learning-based robust controller design for continuous-time uncertain nonlinear systems subject to input constraints
Han et al. Precise positioning of nonsmooth dynamic systems using fuzzy wavelet echo state networks and dynamic surface sliding mode control
He et al. Neural network control of a robotic manipulator with input deadzone and output constraint
Zeinali et al. Adaptive sliding mode control with uncertainty estimator for robot manipulators
Westenbroek et al. Feedback linearization for uncertain systems via reinforcement learning
Izadbakhsh et al. Robust adaptive control of robot manipulators using Bernstein polynomials as universal approximator
Wang et al. Dynamic learning from adaptive neural control for discrete-time strict-feedback systems
Vu et al. Sliding variable-based online adaptive reinforcement learning of uncertain/disturbed nonlinear mechanical systems
Qin et al. Adaptive interval type-2 fuzzy fixed-time control for underwater walking robot with error constraints and actuator faults using prescribed performance terminal sliding-mode surfaces
Tang et al. Adaptive critic design for pure-feedback discrete-time MIMO systems preceded by unknown backlashlike hysteresis
Van Kien et al. Adaptive fuzzy sliding mode control for nonlinear uncertain SISO system optimized by differential evolution algorithm
Dai et al. Transverse function approach to practical stabilisation of underactuated surface vessels with modelling uncertainties and unknown disturbances
Wang et al. Adaptive dynamic programming-based optimal control for nonlinear state constrained systems with input delay
Marvi et al. Reinforcement learning with safety and stability guarantees during exploration for linear systems
Ben Hazem Study of Q-learning and deep Q-network learning control for a rotary inverted pendulum system
Modares et al. Safe reinforcement learning via a model-free safety certifier
Zhu et al. Robust constraint-following control for uncertain mechanical systems with generalized Udwadia-Kalaba approach
Kolaric et al. Local policy optimization for trajectory-centric reinforcement learning
Zhu et al. Cooperative game-theoretic optimization of adaptive robust constraint-following control for fuzzy mechanical systems under inequality constraints
Youssef et al. Reinforcement learning-enhanced adaptive sliding mode control for nonlinear systems
Yang et al. Optimized tracking control using reinforcement learning strategy for a class of nonlinear systems
Bałazy et al. Neural algorithm for optimization of multidimensional object controller parameters
Wan et al. Adaptive neural globally asymptotic tracking control for a class of uncertain nonlinear systems
Bachhuber et al. Neural odes for data-driven automatic self-design of finite-time output feedback control for unknown nonlinear dynamics