Abstract
This article presents an adaptive integral backstepping controller (AIBC) for permanent magnet synchronous motors (PMSMs) with adaptive weight particle swarm optimization (AWPSO) parameters optimization. The integral terms of dq axis current following errors are introduced into the control law, and by constructing an appropriate Lyapunov function, the adaptive law with the differential term and the control law with the integral terms of the current error are derived to weaken the influence of internal parameters perturbation on current control. The AWPSO algorithm is used to optimize the parameters of the AIBC. Based on the analysis of single-objective optimization and multi-objective realization process, a method for transforming multi-objective optimization with convex Prato frontier into single-objective optimization is presented. By this method, a form of fitness function suitable for parameters optimization of backstepping controller is determined, and according to the theoretical derivation and large number of simulation results, the corresponding parameters of the optimization algorithm are set. By randomly adjusting the inertia weight and changing the acceleration factor, the algorithm can accelerate the convergence speed and solve the problem of parameters optimization of the AIBC. The feasibility and effectiveness of the proposed controller for PMSM are verified by simulation and experimental studies.
Highlights
Permanent magnet synchronous motors (PMSMs) have been widely applied in various fields due to their simple structure, high power density and reliable operation [1,2,3,4]
With the development of control theory, some nonlinear control methods have been applied to PMSM, such as sliding mode control (SMC) [7,8,9,10], feedback linearization control (FLC) [11,12,13], auto disturbance rejection control (ADRC) [14,15,16,17], and backstepping control (BC) [18,19,20]
In view of the above two major problems, the main contribution of this article is to present an adaptive integral backstepping controller (AIBC) for PMSM with adaptive weight particle swarm optimization (AWPSO) parameters optimization, which effectively suppress the influence of load torque disturbance and inductance uncertainties of the dq axis on the system, and the parameters tuning problem of the controller is effectively solved
Summary
Permanent magnet synchronous motors (PMSMs) have been widely applied in various fields due to their simple structure, high power density and reliable operation [1,2,3,4]. In [33], adaptive control and backstepping control are combined and applied to speed tracking system of PMSMs with uncertain parameters This method aims at the real-time estimation of resistance and load in the control system and achieves disturbance suppression to a certain extent. In view of the above two major problems, the main contribution of this article is to present an adaptive integral backstepping controller (AIBC) for PMSM with adaptive weight particle swarm optimization (AWPSO) parameters optimization, which effectively suppress the influence of load torque disturbance and inductance uncertainties of the dq axis on the system, and the parameters tuning problem of the controller is effectively solved. The feasibility and effectiveness of the proposed controller for PMSM are verified by simulation and experimental studies
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