Abstract
This chapter estimates an off-policy IRL algorithm to obtain the optimal tracking control of the unknown chaotic systems. The performance index function is first given based on the system tracking error and control error. For solving the Hamilton–Jacobi–Bellman (HJB) equation, an off-policy integral reinforcement learning (IRL) algorithm is proposed. It is proven that the iterative control law makes the tracking error system asymptotically stable, and the iterative value function is convergent. Simulation study demonstrates the effectiveness of the developed tracking control method.
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Wei, Q., Song, R., Li, B., Lin, X. (2018). Off-Policy IRL Optimal Tracking Control for Continuous-Time Chaotic Systems. In: Self-Learning Optimal Control of Nonlinear Systems. Studies in Systems, Decision and Control, vol 103. Springer, Singapore. https://doi.org/10.1007/978-981-10-4080-1_9
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DOI: https://doi.org/10.1007/978-981-10-4080-1_9
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