Neural Adaptive Funnel Dynamic Surface Control with Disturbance-Observer for the PMSM with Time Delays

This paper suggests an adaptive funnel dynamic surface control method with a disturbance observer for the permanent magnet synchronous motor with time delays. An improved prescribed performance function is integrated with a modified funnel variable at the beginning of the controller design to coordinate the permanent magnet synchronous motor with the output constrained into an unconstrained one, which has a faster convergence rate than ordinary barrier Lyapunov functions. Then, the specific controller is devised by the dynamic surface control technique with first-order filters to the unconstrained system. Therein, a disturbance-observer and the radial basis function neural networks are introduced to estimate unmatched disturbances and multiple unknown nonlinearities, respectively. Several Lyapunov-Krasovskii functionals are constructed to make up for time delays, enhancing control performance. The first-order filters are implemented to overcome the complexity explosion caused by general backstepping methods. Additionally, the boundedness and binding ranges of all the signals are ensured through the detailed stability analysis. Ultimately, simulation results and comparison experiments confirm the superiority of the controller designed in this paper.

Automated summary

This paper proposes an adaptive funnel dynamic surface control method for the permanent magnet synchronous motor with time delays. The method integrates an improved prescribed performance function with a modified funnel variable to achieve unconstrained output. It utilizes a disturbance observer and radial basis function neural networks to estimate disturbances and unknown nonlinearities. By constructing Lyapunov-Krasovskii functionals, it compensates for time delays and enhances control performance. Through detailed stability analysis, the boundedness and binding ranges of all signals are ensured.