Neural Adaptive Dynamic Surface Control of PMSMs with Input Saturation and output constraint

This paper discusses an adaptive neural tracking control of permanent magnet synchronous motors subject to input saturation and output constraint. The difficulty is to consider output constraints and input saturation. Firstly, the adaptive dynamic surface control design process is systematized by embedding many existing tools into the classical backstepping framework. Then, a nonlinear transformation function is proposed to transform the output constrained system into an unconstrained system. Furthermore, using radial basis function neural networks to Process the unknown terms, the Gaussian error function is utilized to describe the continuously differentiable asymmetric saturation nonlinearity. It turns out that all signals in the proposed scheme are bounded. The simulation results are provided to further show the feasibility of the proposed method.