[go: up one dir, main page]

Wang et al., 2025 - Google Patents

A Value Iteration Algorithm for Stochastic Linear Quadratic Regulator

Wang et al., 2025

Document ID
1992699072167216112
Author
Wang H
Liu Y
Liu X
Publication year
Publication venue
Journal of Optimization Theory and Applications

External Links

Snippet

In this paper, we propose a novel value iteration algorithm for online adaptive optimal control of discrete-time stochastic linear quadratic regulator (LQR) problems. The algorithm iteratively solves the algebraic Riccati equation (ARE) using online information of states and …
Continue reading at link.springer.com (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
    • G05B13/0205—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
    • G05B13/024—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 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
    • 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
    • G05B13/048—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 using a predictor
    • 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
    • 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
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • 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
    • 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
    • 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
    • G06N7/00—Computer systems based on specific mathematical models

Similar Documents

Publication Publication Date Title
Pham et al. Neural networks-based backward scheme for fully nonlinear PDEs
Zhao et al. Adaptive neural decentralised control for switched interconnected nonlinear systems with backlash-like hysteresis and output constraints
Yaghmaie et al. Linear quadratic control using model-free reinforcement learning
Shalaby et al. Fractional order modeling and control for under-actuated inverted pendulum
Jiang et al. Robust adaptive dynamic programming for linear and nonlinear systems: An overview
Han et al. Recurrent neural networks for stochastic control problems with delay
Cao et al. Gaussian process model predictive control of unknown non‐linear systems
Wu et al. Multiobjective $ H_ {2}/H_ {\infty} $ Control Design of the Nonlinear Mean-Field Stochastic Jump-Diffusion Systems via Fuzzy Approach
Yin et al. Joint stochastic distribution tracking control for multivariate descriptor systems with non-Gaussian variables
Talebi et al. On regularizability and its application to online control of unstable LTI systems
Pang et al. Robust reinforcement learning for stochastic linear quadratic control with multiplicative noise
Qian et al. Hybrid identification method for fractional-order nonlinear systems based on the multi-innovation principle
Liu et al. Neural network state learning based adaptive terminal ILC for tracking iteration-varying target points
Jiang et al. Adaptive linear quadratic control for stochastic discrete-time linear systems with unmeasurable multiplicative and additive noises
Wang et al. A Value Iteration Algorithm for Stochastic Linear Quadratic Regulator
Zhu et al. Cooperative game-theoretic optimization of adaptive robust constraint-following control for fuzzy mechanical systems under inequality constraints
Zhuang et al. Robust control design for zero-sum differential games problem based on off-policy reinforcement learning technique
Zhao Discovering phase field models from image data with the pseudo-spectral physics informed neural networks
Zhang et al. Adaptive iterative learning control of non-uniform trajectory tracking for strict feedback nonlinear time-varying systems
Dai A fast equivalent scheme for robust constrained-input min–max finite horizon model predictive control via model-free critic-only Q-learning
Buldaev Operator forms of the maximum principle and iterative algorithms in optimal control problems
Chen et al. Optimal tracking control of mechatronic servo system using integral reinforcement learning
Janakiraman et al. A Lyapunov based stable online learning algorithm for nonlinear dynamical systems using extreme learning machines
Islam et al. Robust functional observer for stabilising uncertain fuzzy systems with time-delay
Li et al. Disentangling linear quadratic control with untrusted ML predictions