A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 1 Issue 4
Oct.  2014

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Qiming Zhao, Hao Xu and Sarangapani Jagannathan, "Near Optimal Output Feedback Control of Nonlinear Discrete-time Systems Based on Reinforcement Neural Network Learning," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 4, pp. 372-384, 2014.
Citation: Qiming Zhao, Hao Xu and Sarangapani Jagannathan, "Near Optimal Output Feedback Control of Nonlinear Discrete-time Systems Based on Reinforcement Neural Network Learning," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 4, pp. 372-384, 2014.

Near Optimal Output Feedback Control of Nonlinear Discrete-time Systems Based on Reinforcement Neural Network Learning

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This work was supported by National Science Foundation (NSF), Division of Electrical, Communications and Cyber Systems (ECCS) (1128281) and Missouri S&T University Intelligent System Center.

  • In this paper, the output feedback based finitehorizon near optimal regulation of nonlinear affine discretetime systems with unknown system dynamics is considered by using neural networks (NNs) to approximate Hamilton-Jacobi-Bellman (HJB) equation solution. First, a NN-based Luenberger observer is proposed to reconstruct both the system states and the control coefficient matrix. Next, reinforcement learning methodology with actor-critic structure is utilized to approximate the time-varying solution, referred to as the value function, of the HJB equation by using a NN. To properly satisfy the terminal constraint, a new error term is defined and incorporated in the NN update law so that the terminal constraint error is also minimized over time. The NN with constant weights and timedependent activation function is employed to approximate the time-varying value function which is subsequently utilized to generate the finite-horizon near optimal control policy due to NN reconstruction errors. The proposed scheme functions in a forward-in-time manner without offline training phase. Lyapunov analysis is used to investigate the stability of the overall closedloop system. Simulation results are given to show the effectiveness and feasibility of the proposed method.

     

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