Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities

Lewis, Frank L.; Šelmić, Rastko R.; Campos, Javier

doi:10.1137/1.9780898717563

Cited by 223 publications

(173 citation statements)

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“…The control objective is to design a backstepping control system for the output Y of the system shown in (18) to asymptotically track the reference trajectory…”

Section: A Backsteppingcontrol System Design Using Adaptive Modimentioning

confidence: 99%

“…In summary, the on-line tuning algorithm of the modified recurrent Laguerre OPNN is based on the adaptation laws (47) and (48) for the connective weight adjustment and recurrent weight adjustment with two optimal learning rates in (50) and (62), respectively. Moreover, the modified recurrent Legendre OPNN weight estimation errors are fundamentally bounded [18]. As long as the modified recurrent Laguerre OPNN weight estimation errors are bounded, the control signal is bounded.…”

Section: A Backsteppingcontrol System Design Using Adaptive Modimentioning

confidence: 99%

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

Lin¹

2016

Journal of Power Electronics

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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.

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“…The control objective is to design a backstepping control system for the output Y of the system shown in (18) to asymptotically track the reference trajectory…”

Section: A Backsteppingcontrol System Design Using Adaptive Modimentioning

confidence: 99%

Section: A Backsteppingcontrol System Design Using Adaptive Modimentioning

confidence: 99%

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

Lin¹

2016

Journal of Power Electronics

View full text Add to dashboard Cite

show abstract

“…of each rule is calculated by evaluating the expression of belonging at the antecedent of the rule. This action is performed by the conversion, firstly, of the gray scale values of the input pixels in the fuzzy membership values through an initialization of the input membership Function, then the application of operator "and" to [1] Membership values. The operator "and" corresponds to a multiplication of different membership values of the entries pixels.…”

Section: A Structure Of the Realised Nf Edge Detectormentioning

confidence: 99%

“…[1] The systems based on neural networks and fuzzy logic are Good approximations function from sample data. They do not Need mathematical model.…”

Section: Introductionmentioning

confidence: 99%