Adaptive neural network control for fractional-order PMSM with time delay based on command filtered backstepping

Abstract

In this paper, an adaptive neural network (NN) control based on command filtered backstepping approach is presented for fractional-order permanent magnet synchronous motor (PMSM) with parameter uncertainties and unknown time delays. For the convenience of controller design, the state trajectories and phase portrait of the system are investigated to analyze the dynamics of the fractional-order PMSM. The unknown parameters and load torque disturbance in the system dynamics are approximated by using NNs, and the number of adaptive laws for the weight vector is curtailed to just one. To ensure orderly decay of the desired error trajectory, a model reference technique is also introduced to backstepping approach. The command filter technique, which can solve the “explosion of complexity” issue of backstepping, is extended to fractional-order nonlinear systems, and the error compensation mechanism is designed to overcome the shortcoming of the classical dynamics surface filter. The effects of time delays uncertainties are suppressed by employing proper Lyapunov-Krasovskii functions. From the Lyapunov stability theory, the design of the controller ensures all signals in the fractional-order PMSM system remain bounded, while the output error converges to a small region of the origin. Numerical simulations are given to show the correctness and effectiveness of the new design technique.

Finite-time adaptive neural network control for fractional-order chaotic PMSM via command filtered backstepping

“…For the feedback linearization approach, the accurate mathematical model is required. In [12,18,20,[32][33][34], the adaptive NN scheme shows a distinct advantage in dealing with nonlinear systems with uncertain function. Therefore, the adaptive NN technique is adopted to address the unknown nonlinear functions in this paper.…”

“…In this paper, we employ the sign functions as in (16) such that the finite-time convergent of the error compensation system can be guaranteed. Second, the conventional control laws constructed in [2,12,20,22,29] can only guarantee the tracking errors are bounded. However, the controllers (26), (32), (37) and (42) provided in this paper can guarantee that the tracking errors are finite-time stable, which can ensure the fractional-order PMSM system with fast convergence performance.…”

“…However, our method can solve the finite-time control issue for fractional-order PMSM. Fourth, the adaptive NN technique via backstepping developed in [12,33,34] does not take into account the approximation errors. In this paper, the adaptive law C 0 D α tε i = r i |s i | in (48) is constructed to compensate the approximation errors, which can ensure the closed-loop system with high quality tracking performance.…”

“…The permanent magnet synchronous motor (PMSM) has received wide acceptance for industry applications, owing to the superiorities of reliable operation, simple structure and high power density [11,12]. The PMSM is a type of synchronous motor in which the field excitation generated in the stator windings is created by the rare-earth permanent magnet of the rotor.…”

Command Filtering-Based Neural Network Control for Fractional-Order PMSM With Input Saturation

“…For example, a synchronization criteria combined with an impulsive gain is developed in [6] to address the global synchronization problem for fractional-order complex networks. In [7], an adaptive control scheme via backstepping is proposed for fractional-order time delayed systems with uncertain parameters. Compared with integer-order system, the motion states of the fractional-order one may be changed under the same conditions [8].…”