An Improved Adaptive Tracking Controller of Permanent Magnet Synchronous Motor

Abstract

This paper proposes a new adaptive fuzzy neural control to suppress chaos and also to achieve the speed tracking control in a permanent magnet synchronous motor (PMSM) drive system with unknown parameters and uncertainties. The control scheme consists of fuzzy neural and compensatory controllers. The fuzzy neural controller with online parameter tuning is used to estimate the unknown nonlinear models and construct linearization feedback control law, while the compensatory controller is employed to attenuate the estimation error effects of the fuzzy neural network and ensure the robustness of the controlled system. Moreover, due to improvement in controller design, the singularity problem is surely avoided. Finally, numerical simulations are carried out to demonstrate that the proposed control scheme can successfully remove chaotic oscillations and allow the speed to follow the desired trajectory in a chaotic PMSM despite the existence of unknown models and uncertainties.

Improved Adaptive Sliding Mode Control for a Class of Uncertain Nonlinear Systems Subjected to Input Nonlinearity via Fuzzy Neural Networks

“…Consider the unknown nonlinear system in (1) and suppose that Assumptions 1 and 2 are satisfied. Then the controller (27) with the designed adaptive laws (19) and (20) can guarantee that the system output tracks the desired trajectory successfully and the tracking error converges to zero asymptotically fast.…”

“…The fuzzy logic has characteristics of linguistic information and logic control, while neural networks possess characteristics of learning, parallelism, and fault-tolerance. The combination of fuzzy logic and neural networks, known as fuzzy neural networks, which incorporate the advantages of fuzzy inference and neurolearning, was developed and has presented advanced functions in modeling and controlling nonlinear systems [15][16][17][18][19][20][21][22]. Based on universal approximation theorem, fuzzy logic and neural networks have been developed and incorporated into adaptive control techniques.…”

“…In the direct control method, a fuzzy logic system or neural networks are employed to simulate the action of the ideal controller and the parameters are directly adjusted to meet the control objective [15,19,[23][24][25][26][27][28][29][30][31]. In contrast, the indirect control method uses a fuzzy logic system or neural networks to approximate the unknown nonlinear terms of model dynamics and then synthesizes control laws based on these approximations [15,20,21,[32][33][34][35][36][37][38][39][40]. In the indirect control method [15,21,[32][33][34][35][36][37][38][39][40], the authors considered the single-input single-output (SISO) nonlinear systems in the form, ( ) = (x) + (x) , where x ∈ R is the overall state vector, ∈ R is the control input, and ∈ R is the system output.…”

Output-Feedback Sliding Mode Control for Permanent Magnet Synchronous Motor Servo System Subject to Unmatched Disturbances

“…However, it should be noted that the servo control performance is significantly affected by nonlinearities, uncertainties, and disturbances in PMSM systems, and the traditional linear control strategies including the proportional-integral (PI) controller [5] are unable to provide satisfactory control performance [6]. In order to obtain better performance, many advanced nonlinear control methods have been developed for PMSM servo systems in recent years, such as adaptive control [2,7], robust control [8,9], linearization control [10], disturbance observer-based control [2,11], fuzzy-logic based control [6,12], finite time control [13,14], fractional order control [15], sliding mode control [16,17], and neuro-network based control [5,12]. ese control strategies improve control performance for PMSM servo systems from different aspects.…”

Adaptive MIMO Controller Design for Chaos Synchronization in Coupled Josephson Junctions via Fuzzy Neural Networks

“…Nowadays, fuzzy logic and neural networks are used as the power tools for modelling and controlling highly uncertain, nonlinear and complex systems [12], [13], [14], [15], [16]. In this study, the chaos synchronization of coupled RCLSJ modes is expected.…”