[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Modares et al., 2014 - Google Patents

Online solution of nonquadratic two‐player zero‐sum games arising in the H∞ control of constrained input systems

Modares et al., 2014

Document ID
12937442313634962858
Author
Modares H
Lewis F
Sistani M
Publication year
Publication venue
International Journal of Adaptive Control and Signal Processing

External Links

Snippet

In this paper, we present an online learning algorithm to find the solution to the H∞ control problem of continuous‐time systems with input constraints. A suitable nonquadratic functional is utilized to encode the input constraints into the H∞ control problem, and the …
Continue reading at onlinelibrary.wiley.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
    • G05B13/0265—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
    • 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
    • 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
    • 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
    • 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
    • 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

Similar Documents

Publication Publication Date Title
Modares et al. Online solution of nonquadratic two‐player zero‐sum games arising in the H∞ control of constrained input systems
Vamvoudakis et al. Online solution of nonlinear two‐player zero‐sum games using synchronous policy iteration
Vamvoudakis et al. Online adaptive algorithm for optimal control with integral reinforcement learning
Polyakov et al. Robust stabilization of MIMO systems in finite/fixed time
Yang Wasserstein distributionally robust stochastic control: A data-driven approach
Wen et al. Optimized backstepping for tracking control of strict-feedback systems
Yang et al. Neural‐network‐based online optimal control for uncertain non‐linear continuous‐time systems with control constraints
Lopez‐Ramirez et al. Fixed‐time output stabilization and fixed‐time estimation of a chain of integrators
Fan et al. Adaptive fault‐tolerant control for affine non‐linear systems based on approximate dynamic programming
Aslam L 2–L∞ control for delayed singular markov switch system with nonlinear actuator faults
Wang et al. Dynamic learning from adaptive neural control for discrete-time strict-feedback systems
Yang et al. Dynamic intermittent Q‐learning–based model‐free suboptimal co‐design of‐stabilization
Adib Yaghmaie et al. Output regulation of heterogeneous linear multi‐agent systems with differential graphical game
Xu et al. Finite-horizon near-optimal output feedback neural network control of quantized nonlinear discrete-time systems with input constraint
Yasini et al. Online concurrent reinforcement learning algorithm to solve two‐player zero‐sum games for partially unknown nonlinear continuous‐time systems
Li et al. Output‐feedback‐based sliding mode control for networked control systems subject to packet loss and quantization
Liu et al. Robust reliable control for discrete‐time‐delay systems with stochastic nonlinearities and multiplicative noises
Du et al. Sensor fault estimation and accommodation for discrete‐time switched linear systems
Zhang et al. Non‐fragile robust control for networked control systems with long time‐varying delay, randomly occurring nonlinearity, and randomly occurring controller gain fluctuation
Lin et al. Online identifier–actor–critic algorithm for optimal control of nonlinear systems
Zhang et al. Adaptive MPC for constrained systems with parameter uncertainty and additive disturbance
Liu et al. Second‐order sliding mode controller design subject to mismatched unbounded perturbations
Ying‐Chun et al. Heuristic dynamic programming using echo state network for multivariable tracking control of wastewater treatment process
Liu et al. Infinite time linear quadratic Stackelberg game problem for unknown stochastic discrete‐time systems via adaptive dynamic programming approach
Kinoshita et al. Design of neural network PID controller based on E‐FRIT