IEEE Trans Neural Netw Learn Syst - Reinforcement learning design-based adaptive tracking control with less learning parameters for nonlinear discrete-time MIMO systems.

Tópicos

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Resumo

Based on the neural network (NN) approximator, an online reinforcement learning algorithm is proposed for a class of affine multiple input and multiple output (MIMO) nonlinear discrete-time systems with unknown functions and disturbances. In the design procedure, two networks are provided where one is an action network to generate an optimal control signal and the other is a critic network to approximate the cost function. An optimal control signal and adaptation laws can be generated based on two NNs. In the previous approaches, the weights of critic and action networks are updated based on the gradient descent rule and the estimations of optimal weight vectors are directly adjusted in the design. Consequently, compared with the existing results, the main contributions of this paper are: 1) only two parameters are needed to be adjusted, and thus the number of the adaptation laws is smaller than the previous results and 2) the updating parameters do not depend on the number of the subsystems for MIMO systems and the tuning rules are replaced by adjusting the norms on optimal weight vectors in both action and critic networks. It is proven that the tracking errors, the adaptation laws, and the control inputs are uniformly bounded using Lyapunov analysis method. The simulation examples are employed to illustrate the effectiveness of the proposed algorithm.

Resumo Limpo

base neural network nn approxim onlin reinforc learn algorithm propos class affin multipl input multipl output mimo nonlinear discretetim system unknown function disturb design procedur two network provid one action network generat optim control signal critic network approxim cost function optim control signal adapt law can generat base two nns previous approach weight critic action network updat base gradient descent rule estim optim weight vector direct adjust design consequ compar exist result main contribut paper two paramet need adjust thus number adapt law smaller previous result updat paramet depend number subsystem mimo system tune rule replac adjust norm optim weight vector action critic network proven track error adapt law control input uniform bound use lyapunov analysi method simul exampl employ illustr effect propos algorithm

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