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

Lin, Chih‐Hong

doi:10.1177/0142331219851920

Cited by 9 publications

(9 citation statements)

References 41 publications

(81 reference statements)

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“…With the development of control theory, some nonlinear control methods have been applied to switched reluctance motors to improve the control performance [21,22]. Fuzzy control has certain robustness to time-varying load and is suitable for controlling nonlinear, time-varying, lagging, and incomplete model systems [23,24].…”

Section: Introductionmentioning

confidence: 99%

Torque Ripple Suppression of Switched Reluctance Motor Based on Fuzzy Indirect Instant Torque Control

2022

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High torque ripple limits the industrial application of switched reluctance motor (SRM) due to the deep magnetic saturation. This paper proposes a fuzzy indirect instant torque control (IITC) to suppress torque ripple. First, a fuzzy controller is developed to generate a compensation current according to the torque error. The input factor is then proposed and combined with artificial experience. Moreover, considering the relationship between current and electromagnetic torque, the output factor is appropriately designed according to the linearized inductance derivative. Finally, based on a three-phase 12/8 SRM, simulation and experimental results are provided to demonstrate the effectiveness of the proposed fuzzy IITC at torque ripple suppression. Compared with traditional proportion differentiation (PD) IITC, the fuzzy IITC proves its superiority in reducing torque ripple.

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Section: Introductionmentioning

confidence: 99%

Torque Ripple Suppression of Switched Reluctance Motor Based on Fuzzy Indirect Instant Torque Control

2022

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“…Particularly, neural networks (NNs) and fuzzy logic systems as general approximators together with backstepping technique have been widely studied for the adaptive control of uncertain nonlinear systems. 4–6 It should be mentioned that the traditional backstepping suffers from the complexity explosion problem, which arises from the derivatives of virtual controllers. In order to circumvent the complexity explosion problem, the tracking differentiator 7 and the dynamic surface control technique 8 have been put forward.…”

Section: Introductionmentioning

confidence: 99%

Command filtered adaptive neural tracking control of uncertain nonlinear time-delay systems with asymmetric time-varying full state constraints and actuator saturation

Fang

2020

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article investigates the command filtered adaptive neural tracking control for uncertain nonlinear time-delay systems subject to asymmetric time-varying full state constraints and actuator saturation. To stabilize such a class of systems, the radial basis function neural networks and the backstepping technique are used to structure an adaptive controller. The command filter is utilized to overcome the complexity explosion problem in backstepping. By employing the Lyapunov–Krasovskii functionals, the effect of time-delay is eliminated. The asymmetric time-varying barrier Lyapunov functions are designed to ensure full state constraint satisfaction. Moreover, the hyperbolic tangent function and an instrumental variable are introduced to deal with actuator saturation. All signals in the closed-loop system are proved to be bounded and the tracking error converges to a small neighborhood of the origin. Finally, two examples are provided to illustrate the effectiveness of the proposed method.

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“…Adaptive control strategies can be used to solve the uncertainty problems of nonlinear systems, 26 and have been applied to ordinary motor control. 26–28 At present, due to the high complexity of bearingless motor system model, the corresponding adaptive research results are few. Yang et al 29 studied the adaptive exponential sliding mode control strategy of a BL-IM system based on disturbance observer; Bu et al 30 studied an adaptive feedforward vibration compensation strategy of a BL-IM system based on analytical inverse system decoupling, but the effect of unmodeled dynamics which may exist is not considered.…”

Section: Introductionmentioning

confidence: 99%

Neural network inverse system decoupling fuzzy self-tuning proportional–derivative control strategy of a bearingless induction motor

Zhang

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part I:

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In view of the problems that the analytical inverse system decoupling method of bearingless induction motor is sensitive to the change of motor parameters and is greatly affected by unmodeled dynamics, and that of traditional proportional–derivative controller lacking the self-adaptive regulation ability, a neural network inverse system decoupling fuzzy self-tuning proportional–derivative control strategy is proposed for a bearingless induction motor system. First, under the conditions of considering the stator current dynamic of torque winding, and by neural network inverse system method, the bearingless induction motor system is decoupled into four pseudo-linear integral subsystems. Second, the traditional proportional–derivative controller is improved, and the fuzzy control algorithm is used to adjust the parameters of improved proportional–derivative controller adaptively. Thus, a neural network inverse system decoupling fuzzy self-tuning proportional–derivative control system is constructed for a bearingless induction motor. The simulation experimental results show that the proposed control strategy not only can effectively improve the stability, robustness and steady-state control accuracy of bearingless induction motor system, but also can significantly improve the dynamic response speed and the ability to resist the influences of motor parameter change and load disturbance.

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

Cited by 9 publications

References 41 publications

Torque Ripple Suppression of Switched Reluctance Motor Based on Fuzzy Indirect Instant Torque Control

Torque Ripple Suppression of Switched Reluctance Motor Based on Fuzzy Indirect Instant Torque Control

Command filtered adaptive neural tracking control of uncertain nonlinear time-delay systems with asymmetric time-varying full state constraints and actuator saturation

Neural network inverse system decoupling fuzzy self-tuning proportional–derivative control strategy of a bearingless induction motor

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