Background:
This article studies the issue of adaptive neural dynamic surface control for
the chaotic permanent magnet synchronous motor system with constrained output, external disturbances
and parameter perturbations.
Methods:
Firstly, a virtual controller and two practical controllers are created based on the backstepping
framework. In the process of creating controllers, adaptive technique and radial basis function
neural networks are used to handle unknown parameters and nonlinearities, respectively. The
nonlinear damping items are applied to overcome external disturbances. The barrier Lyapunov function
is used to prevent the violation of system output constraint. Meanwhile, the first-order filter to
eliminate the “explosion of complexity” of traditional back stepping has been introduced. Then, it is
proved that all the closed-loop signals are uniform ultimate asymptotic stability and the tracking
error converges to a small set of origin.
Results:
The effectiveness and robustness of the developed approach are illustrated by numerical
simulations.
Conclusion:
The raised control scheme is a useful tool for enhancing the performance of the chaotic
PMSM system with external disturbances, constrained output and parameter perturbations.
Output Feedback Adaptive Dynamic Surface Control of Permanent Magnet Synchronous Motor with Uncertain Time Delays via RBFNN
