A Backstepping Control of LSM Drive Systems Using Adaptive Modified Recurrent Laguerre OPNNUO

Abstract

The good control performance of permanent magnet linear synchronous motor (LSM) drive systems is difficult to achieve using linear controllers because of uncertainty effects, such as fictitious forces. A backstepping control system using adaptive modified recurrent Laguerre orthogonal polynomial neural network uncertainty observer (OPNNUO) is proposed to increase the robustness of LSM drive systems. First, a field-oriented mechanism is applied to formulate a dynamic equation for an LSM drive system. Second, a backstepping approach is proposed to control the motion of the LSM drive system. With the proposed backstepping control system, the mover position of the LSM drive achieves good transient control performance and robustness. As the LSM drive system is prone to nonlinear and time-varying uncertainties, an adaptive modified recurrent Laguerre OPNNUO is proposed to estimate lumped uncertainties and thereby enhance the robustness of the LSM drive system. The on-line parameter training methodology of the modified recurrent Laguerre OPNN is based on the Lyapunov stability theorem. Furthermore, two optimal learning rates of the modified recurrent Laguerre OPNN are derived to accelerate parameter convergence. Finally, the effectiveness of the proposed control system is verified by experimental results.

Retracted: Adaptive nonlinear backstepping control using mended recurrent Romanovski polynomials neural network and mended particle swarm optimization for switched reluctance motor drive system

“…Each backstepping stage results in a new pseudo-control design, expressed in terms of the pseudo control design from preceding design stage. When the procedure terminates, feedback design for the true control input results, which achieves the original design objective by virtue of a final Lyapunov function formed by summing up the Lyapunov functions associated with each individual design stage (Bartolini et al, 2000; Kanellakopoulos et al, 1991; Lin, 2016, 2017b). Some of these methods use off-line data collected from the machine during the static conditions, which change during motor operation due to changes in the motor parameters.…”

Switched reluctance motor circuit drive system using adaptive nonlinear backstepping control with mended recurrent Romanovski polynomials neural network and mended particle swarm optimization

“…The parameters of the proposed adaptive nonlinear backstepping control system using mended recurrent Romanovski polynomials neural network with adaptive law and error estimated law as control system D are given as d 1 = 2.2, d 2 = 1.7, d 3 = 2.3, γ = 0.1, and ε = 0.5 through some heuristic knowledge because of periodic step command from 0 to 6.28 rad in the nominal case for the position tracking in order to achieve good transient and steady‐state control performance. Furthermore, to show the effectiveness of the control system with a small number of neurons, the mended recurrent Romanovski polynomials neural network has 2, 4, and 1 neurons in the input layer, the hidden layer, and the output layer, respectively.…”

“…Each backstepping stage results in a new pseudo‐control design, expressed in terms of the pseudo‐control design from preceding design stage. When the procedure terminates, feedback design for the true control input results, which achieves the original design objective by virtue of a final Lyapunov function formed by summing up the Lyapunov functions associated with each individual design stage . Some of these methods use off‐line data collected from the machine during the static conditions, which change during motor operation due to changes in the motor parameters.…”

Micrometer Backstepping Control System for Linear Motion Single Axis Robot Machine Drive

“…The design of tracking and adjustment strategies can provide a systematic skeleton. Moreover, to extend to the estimation of unknown parameters of the system, the adaptive backstepping methods [6,7] were put forward to estimate some unknown parameters of the system. Furthermore, some adaptive backstepping controllers were used for some linear machines [8,9] to estimate uncertainty.…”