Digital signal processor based intelligent fractional‐order sliding‐mode control for a linear voice coil actuator

Chen, Syuan‐Yi; Lee, Cheng Yan

doi:10.1049/iet-cta.2016.1127

Cited by 22 publications

(22 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The optimization objective is that the RVCM position x 1 can be controlled to follow the given position reference x * 1 . The objective is mathematically expressed by minimizing the sum of squared error of position tracking, as in (2).…”

Section: B Optimization Problem Formation and Solutionmentioning

confidence: 99%

Data-Driven Digital Direct Position Servo Control by Neural Network With Implicit Optimal Control Law Learned From Discrete Optimal Position Tracking Data

et al. 2019

View full text Add to dashboard Cite

To get better control performance in motor control, more and more researches tend to apply non-linear control laws in the field of motor control. However, most conventional non-linear control theory is based on explicit model of controlled object and often resulting in complexity. Besides, the control parameters tuning is mainly aiming at stability of the system. No valid direct performance-oriented nonlinear control theory has been proposed. Facing the limitations, this paper presents a direct motor position control in an implicit data-driven manner. Unlike conventional non-linear motor controls that are based on explicit models and with stability-based parameters tuning, this study gives performance-oriented non-linear control by mastering non-linear discrete optimal control law in an implicit data-learning manner. Firstly, optimal data of position tracking problem is obtained by solving optimization problem. Secondly, the implicit discrete optimal control law hidden in data is learned by a BP neural network. Finally, the learned control law is implemented in real-time control to reproduce optimal control performance. Simulation and experiment results validated the feasibility of the data-driven controller, which could be helpful for performance-oriented non-linear control designs. The merits and further improvements are also discussed.INDEX TERMS Position control, implicit discrete optimal control, artificial neural network, motor, data learning.

show abstract

Section: B Optimization Problem Formation and Solutionmentioning

confidence: 99%

Data-Driven Digital Direct Position Servo Control by Neural Network With Implicit Optimal Control Law Learned From Discrete Optimal Position Tracking Data

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Due to the abovementioned reasons, researchers have conducted extensive researches on motor control approaches such as robust control [7, 8], fuzzy control [9, 10], neural network control [11, 12] and sliding mode control (SMC) [13, 14]. Owing to its suppression to the uncertainty factors and strong robustness to the disturbance, the SMC method has led to significant performance improvement in the PMLSM control [15].…”

Section: Introductionmentioning

confidence: 99%

Intelligent second‐order sliding mode control for permanent magnet linear synchronous motor servo systems with robust compensator

Zhao

Wang

Huang

2020

IET Electric Power Applications

View full text Add to dashboard Cite

In this study, an intelligent second‐order sliding mode control (SMC) method combining second‐order SMC (SOSMC) and recurrent radial basis function neural network (RRBFNN) applicable to the permanent magnet linear synchronous motor (PMLSM) is proposed to achieve high‐performance servo control fields. On the basis of a dynamic model of PMLSM and the SMC theory, the chattering problem in SMC is weakened and the tracking accuracy is improved by the design of SOSMC. As for the boundary of the uncertainty factors is difficult to obtain, the optimal performance of SOSMC is hard to achieve, the RRBFNN uncertainty observer is introduced for estimating the value of the uncertainty factors. Owing to the strong learning ability, the network parameters can be trained online. Besides, a robust compensator is developed to suppress the uncertainties such as approximation error, optimal parameter vector and higher Taylor series for further improving the robustness. Moreover, the adaptive learning algorithms are obtained by using the Lyapunov theorem to guarantee the asymptotical stability of the system. The experiments demonstrate that the proposed scheme provides high performance dynamic characteristics and strong robustness to uncertainties.

show abstract

“…In [19], A command filter-based FO dynamic surface control technique is employed in nonlinear systems to achieve the adaptive tracking control with unknown control directions and disturbances. Furthermore, some evidence has confirmed that the FO can be introduced into SMC for higher tracking accuracy [20,21]. In [20], a fractional-order SMC (FOSMC) is proposed for controlling the mover position of a VCA to enhance robustness and attenuate chattering.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, some evidence has confirmed that the FO can be introduced into SMC for higher tracking accuracy [20,21]. In [20], a fractional-order SMC (FOSMC) is proposed for controlling the mover position of a VCA to enhance robustness and attenuate chattering. In [22], an adaptive Super-Twisting SMC based on FO method is proposed to obtain faster convergence of SMC and improve performance and precision of MEMS gyroscope.…”

Section: Introductionmentioning

confidence: 99%

Multi-Loop Recurrent Neural Network Fractional-Order Terminal Sliding Mode Control of MEMS Gyroscope

Fei

Wang

2020

IEEE Access

View full text Add to dashboard Cite

This paper proposes a fractional-order nonsingular terminal sliding mode control of a MEMS gyroscope using a double loop recurrent neural network approximator. For the system stability, a nonsingular terminal sliding mode controller is formulated to guarantee the convergence. For higher accuracy and faster convergence, the fractional-order (FO) calculus is employed with additional degree of freedom. For the system robustness, the neural network is designed to approximate the lumped uncertainty. The inner recurrent loop and external recurrent loop are employed to provide a feedback signal to obtain satisfactory approximation accuracy. For higher adaptability of the neural network, the dynamic function is formulated and the updating law of the parameter is given. Furthermore, the Lyapunov stability theorem is employed to verify the asymptotical stability and convergence of system. Simulations for a MEMS gyroscope are studied to exhibit the superiority of the proposed control strategy.

show abstract

Digital signal processor based intelligent fractional‐order sliding‐mode control for a linear voice coil actuator

Cited by 22 publications

References 28 publications

Data-Driven Digital Direct Position Servo Control by Neural Network With Implicit Optimal Control Law Learned From Discrete Optimal Position Tracking Data

Data-Driven Digital Direct Position Servo Control by Neural Network With Implicit Optimal Control Law Learned From Discrete Optimal Position Tracking Data

Intelligent second‐order sliding mode control for permanent magnet linear synchronous motor servo systems with robust compensator

Multi-Loop Recurrent Neural Network Fractional-Order Terminal Sliding Mode Control of MEMS Gyroscope

Contact Info

Product

Resources

About