Full state constraints and command filtering-based adaptive fuzzy control for permanent magnet synchronous motor stochastic systems

In this article, an adaptive fuzzy control scheme based on command filtering is proposed for the position tracking control of permanent magnet synchronous motor (PMSM) stochastic system with full state constraints. Firstly, fuzzy logic systems are employed to approximate unknown stochastic nonlinear functions in PMSM stochastic system. Then, the barrier Lyapunov functions are constructed to ensure that all states of the system do not violate its constrained boundary. In addition, the problem of explosion of complexity in traditional backstepping design is solved by using the command filtering technique and the error compensation mechanism is introduced to reduce filtering errors. At last, the effectiveness of the scheme is illustrated by simulation results. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

An adaptive fuzzy control scheme based on command filtering is proposed for the position tracking control of PMSM stochastic system with full state constraints. Fuzzy logic systems are employed to approximate unknown stochastic nonlinear functions, while barrier Lyapunov functions are constructed to ensure that all states of the system do not violate its constrained boundary. The scheme solves the problem of complexity in traditional design and introduces error compensation mechanism to reduce filtering errors.