Chaos control of the brushless direct current motor using adaptive dynamic surface control based on neural network with the minimum weights

Abstract

This paper investigates chaos control for the brushless DC motor (BLDCM) system by adaptive dynamic surface approach based on neural network with the minimum weights. The BLDCM system contains parameter perturbation, chaotic behavior, and uncertainty. With the help of radial basis function (RBF) neural network to approximate the unknown nonlinear functions, the adaptive law is established to overcome uncertainty of the control gain. By introducing the RBF neural network and adaptive technology into the dynamic surface control design, a robust chaos control scheme is developed. It is proved that the proposed control approach can guarantee that all signals in the closed-loop system are globally uniformly bounded, and the tracking error converges to a small neighborhood of the origin. Simulation results are provided to show that the proposed approach works well in suppressing chaos and parameter perturbation.

Global exponential stabilization for chaotic brushless DC motor with simpler controllers

“…And many of them have proposed different control methods to stabilize BLDCM. Luo et al (2015) used adaptive dynamic surface control based on neural network with the minimum weights to control chaos in BLDCM systems. Wang et al (2016b) controlled chaos in BLDCM system based on finite-time stability theory.…”

Nonlinear behavior for periodically excited brushless motor

“…The simulation results correlated well with the experimental results which indicated that the model was significant. Luo et al (2014, 2015) studied the application of the combination of dynamic surface control technology, radial basis function neural network and adaptive method, in the control of chaos for the BLDCM system. The same model with Hemati was applied, and the tangent barrier Lyapunov function was used for a time-delay nonlinear system.…”

“…For systems with strong nonlinearity, a generalized harmonic balanced method was developed and the effectiveness of such a method was verified with the Duffy oscillator (Luo and Huang, 2013), nonlinear Jeffcott oscillator (Huang and Luo, 2015), etc. Further, a semi-analytic methodology called discrete implicit maps was introduced (Luo et al , 2015) to give the analytic solution of periodic motions for any type of nonlinear system which possesses ordinary differential equations. With such a method, the bifurcation of analytic steady state solution for nonlinear systems such as piston in a high pressure gas cylinder (Wang and Huang, 2016), hardening Duffing oscillator with time-delay (Luo and Xing, 2016) and parametrically excited pendulum (Guo and Luo, 2017).…”

Synchronization of the Fractional-Order Brushless DC Motors Chaotic System

“…The brushless direct-current motors (BLDCM) have many advantages over brushed direct-current motors [1][2][3][4][5][6][7], including more torque per watt, high torque per weight, longer lifetime, lower noise, lower electromagnetic interference, and high reliability. So, the BLDCM has been used diffusely in industrial automation and manufacturing engineering, for example, computer hard drives and CD/DVD players, electric vehicles and hybrid vehicles, motion control, positioning or actuation systems, and micro radio-controlled airplane.…”

“…So, the BLDCM has been used diffusely in industrial automation and manufacturing engineering, for example, computer hard drives and CD/DVD players, electric vehicles and hybrid vehicles, motion control, positioning or actuation systems, and micro radio-controlled airplane. Recently, chaotic behavior in the brushless directcurrent motors (BLDCM) has been reported [1][2][3][4][5][6][7], and many researchers investigated chaos synchronization and chaos control for the BLDCM chaotic system [1][2][3][4][5][6][7]. Chaos is useful in many applications, for example, in image steganography [8,9], authenticated encryption [10], and chaotic communications [11].…”