IEEE Transactions on Systems Man and Cybernetics Systems · 2018 · 62 citations · 45 references
EngineeringComputer ScienceMachine LearningOptimizationProcess ControlAdaptive ControlControl SystemsControl TheoryController DesignNonlinear ControlOptimal ControlAdaptive Control Of Nonlinear SystemsAdaptive Neural ControlAsymptotic StabilityConstrained Nonlinear ControlEstimation ErrorIterative LearningNeural Network ControlNonlinear Systems
In this paper, an H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> optimal tracking controller for completely unknown discrete-time nonlinear systems with control constraints is obtained by using an iterative adaptive learning algorithm. An augmented system is established by integrating the tracking error system and the reference trajectory. As an identifier of the unknown systems, a neural network (NN) is introduced with asymptotic stability of the estimation error. An action-disturbance-critic NN structure is proposed to implement the iterative dual heuristic programming algorithm with convergence guarantee of the costate function and the control policy. Simulation results and comparisons are provided to illustrate the superior performance of the designed optimal tracking controller.
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