Abstract
This article proposes a novel optimal tracking control scheme for permanent magnet synchronous motors (PMSMs) with partially unknown dynamics, saturation voltages, and disturbances in both speed and current dynamics. The strict-feedback nonlinear system is employed to present the PMSM model. Augmented feedforward control inputs are proposed to transform a speed and current tracking control problem of conventional cascade structures to an optimal control problem of a new structure. Consequently, the saturated adaptive optimal control law is designed for the problem. The optimal solution of Hamilton–Jacobi–Issac equation, which provides the value to the control law, is approximated by a simple online approximator. An integral reinforcement learning technique is used to tune the approximator without observing unknown dynamics. It is proven that the optimal value function, the control law, and the worst disturbance law converge to the near-optimal values. The simulation and experiment on a PMSM prototype model of a load drive application with a digital signal processing board TMS320F28379D of Texas Instrument are conducted to justify the effectiveness of the proposed scheme.
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