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摘要
This paper focuses on the optimal tracking control problem for a class of discrete-time stochastic systems with additive and multiplicative noises. By constructing an augmented system composed of the original system and the reference generator, the cost function of the stochastic linear quadratic tracking control (SLQT) is transformed into a quadratic function in terms of the augmented state. A Bellman equation and an augmented stochastic algebraic Riccati equation (SARE) for solving the SLQT are then derived. For the case of both the system and reference generator dynamics are completely unknown, a Q-learning algorithm is proposed to solve the augmented SARE, and its convergence is proved rigorously. Moreover, the online model-free control algorithm implements with the batch least square method (BLS). The effectiveness of the proposed control scheme is verified by the single-phase voltage-source UPS inverter. ? 2023 South China University of Technology.
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