Intelligent Adaptive Dynamic Surface Control System with Recurrent Wavelet Elman Neural Networks for DSP-Based Induction Motor Servo Drives

Intelligent Adaptive Dynamic Surface Control System with Recurrent Wavelet Elman Neural Networks for DSP-Based Induction Motor Servo Drives

Abstract

In this paper, an intelligent adaptive dynamic surface control system (IADSCS) with recurrent wavelet Elman neural network (RWENN) for induction motor (IM) servo drive is proposed. The IADSCS comprises a dynamic surface controller (DSC), a recurrent wavelet Elman neural network (RWENN) uncertainty observer and a robust controller. First, a computed torque controller (CTC) is designed to stabilize the IM servo drive. Then, a nonlinear disturbance observer (NDO) is designed to estimate the nonlinear lumped parameter uncertainties existed in the CTC law. However, the IM servo drive performance is degraded by the NDO error due to the parameter uncertainties. To improve the robustness of the IM servo drive due to external load disturbances and parameter uncertainties, an IADSCS is designed to achieve this purpose. In the IADSCS, the DSC is used to overcome the explosion of the complexity in the backstepping design technique and the RWENN identifier is used to approximate the lumped parameter uncertainties and compounded disturbances. In addition, the robust controller is designed to recover the approximation error of the RWENN. The stability of the closed-loop system is guaranteed by the Lyapunov stability theory. All control algorithms are implemented using dSPACE1104 DSP-based control computer. The simulation and experimental results show the superiority of the proposed IADSCS in external load disturbance suppression and the robustness against parameter uncertainties.

Index Terms

Computed torque control, dynamic surface control, IM drive, Lyapunov stability, recurrent wavelet Elman neural network, nonlinear disturbance observer, uncertainties.

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published in: 2018-10-18 00:27:05

Robust Adaptive Dynamic Surface Control Using Recurrent Cerebellar Model Articulation Controller-Based Function Link Neural Network for Two-Axis Motion Control Systems

Robust Adaptive Dynamic Surface Control Using Recurrent Cerebellar Model Articulation Controller-Based Function Link Neural Network for Two-Axis Motion Control Systems

This paper proposes a robust adaptive dynamic surface control system (RADSCS) using recurrent cerebellar model articulation controller-based function link neural network (RCMACFLNN) for uncertain two-axis motion control system driven by two permanent-magnet synchronous motors (PMSMs) servo drives. The proposed control scheme incorporates a dynamic surface controller (DSC), a RCMACFLNN uncertainty observer and a robust controller. First, an optimal computed torque controller (OCTC) is deigned to stabilize the two-axis motion control system. In addition, the OCTC is utilized to approximate the CTC law and minimizes a quadratic performance index based on the Hamilton-Jacobi-Bellman (HJB) optimization scheme. However, the control performance may be destroyed due to parameter uncertainties exist in the OCTC law for the reason that the linear optimal control has an inherent robustness against a certain range of model uncertainties. Therefore, a RADSCS is designed to improve the robustness of the control system. In the RADSCS, the DSC is used to overcome the explosion of the complexity in the backstepping design. As well, the RCMACFLNN uncertainty observer is designed to adaptively estimate the nonlinear parameter uncertainty terms online, whereas the robust controller is designed to recover the residual of the approximation error of the RCMACFLNN. The online adaptive control laws are derived using the Lyapunov stability analysis. From the experimental results, the motions at X-axis and Y-axis are controlled separately and the dynamic behaviors of the proposed RADSCS can achieve robust tracking performance against parameter uncertainties.

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published in: 2018-10-14 14:22:24

Intelligent mixed H2/H∞ adaptive tracking control system design using self-organizing recurrent fuzzy-wavelet-neural-network for uncertain two-axis motion control system

Intelligent mixed H2/H∞ adaptive tracking control system design using self-organizing recurrent fuzzy-wavelet-neural-network for uncertain two-axis motion control system

Abstract

In this paper, an intelligent adaptive tracking control system (IATCS) based on the mixed H2/H∞ approachunder uncertain plant parameters and external disturbances for achieving high precision performance of a two-axis motion control system is proposed. The two-axis motion control system is an X–Y table driven by two permanent-magnet linear synchronous motors (PMLSMs) servo drives. The proposed control scheme incorporates a mixed H2/H∞ controller, a self-organizing recurrent fuzzy-wavelet-neural-network controller (SORFWNNC) and a robust controller. The combinations of these control methods would insure the stability, robustness, optimality, overcome the uncertainties, and performance properties of the two-axis motion control system. The SORFWNNC is used as the main tracking controller to adaptively estimate an unknown nonlinear dynamic function that includes the lumped parameter uncertainties, external disturbances, cross-coupled interference and frictional force. Moreover, the structure and the parameter learning phases of the SORFWNNC are performed concurrently and online. Furthermore, a robust controller is designed to deal with the uncertainties, including the approximation error, optimal parameter vectors and higher order terms in Taylor series. Besides, the mixed H2/H∞controller is designed such that the quadratic cost function is minimized and the worst case effect of the unknown nonlinear dynamic function on the tracking error must be attenuated below a desired attenuation level. The mixed H2/H∞control design has the advantage of both H2optimal control performance and H∞ robust control performance. The sufficient conditions are developed for the adaptive mixed H2/H∞tracking problem in terms of a pair of coupled algebraic equations instead of coupled nonlinear differential equations. The coupled algebraic equations can be solved analytically. The online adaptive control laws are derived based on Lyapunov theorem and the mixed H2/H∞tracking performance so that the stability of the proposed IATCS can be guaranteed. Furthermore, the control algorithms are implemented in a DSP-based control computer. From the experimental results, the motions...

published in: 2016-02-08 10:01:52