Chaos control of the permanent magnet synchronous motor with time-varying delay by using adaptive sliding mode control based on DSC

Luo, Shaohua; Gao, Ruizhen

doi:10.1016/j.jfranklin.2018.04.031

Cited by 42 publications

(21 citation statements)

References 37 publications

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“…Backstepping control (BC) and sliding-mode control (SMC) are the major control methods for nonlinear models so far. In contrast to SMC, which has internal chattering that limits its expansion, BC has been proved to overcome this obstacle [31][32][33]. However, the "explosion of complexity" rising from the repetitive differentiation of virtual signals and the highcomplexity caused by the unknown nonlinearities are often encountered in the design of backstepping controller [34].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Backstepping Control of Variable Stiffness Actuator

Xia

Song

Wang

et al. 2021

Preprint

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In this paper, we provide a novel adaptive neural network backstepping control scheme for a special variable stiffness actuator (VSA) based on lever mechanisms with saturation inputs, output constraints and disturbances is presented here. In the controller designing, the prescribed performance-tangent barrier Lyapunov function (PP-TBLF) is introduced to ensure that both the prescribed performance bound of tracking error and the output constraints are not violated. In specific steps of backstepping control scheme, the Chebyshev neural network and the Nussbaum-type function are used to solve the unknown nonlinearities and unknown gain sign. Meanwhile, the inverse hyperbolic sine function tracking differentiator is exploited to solve the “explosion of complexity” caused by the differentiation of virtual inputs and also approximate the complex partial derivative caused by the auxiliary control signals. Finally, the stability of the whole scheme is proved by Lyapunov criterion and the simulation results are presented to illustrate the feasibility of the raised control strategy.

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Section: Introductionmentioning

confidence: 99%

Adaptive Neural Backstepping Control of Variable Stiffness Actuator

Xia

Song

Wang

et al. 2021

Preprint

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“…In [34], a neural network controller is developed based on adaptive DSC by employing the first-order filter for PMSM with load torque disturbance. To stabilize the chaotic PMSM with unknown dynamics, an adaptive SMC based on DSC is designed to improve system performance in [35]. One major drawback of the DSC is that it does not consider the effect of the error caused by low-pass filter, which may increase the difficulty of precise control for the system [36].…”

Section: Introductionmentioning

confidence: 99%

Observer-Based Command Filtered Adaptive Neural Network Tracking Control for Fractional-Order Chaotic PMSM

Wang

2019

IEEE Access

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In this paper, an observer-based command filtered adaptive neural network tracking control problem is addressed for a fractional-order chaotic permanent magnet synchronous motor (PMSM) with the immeasurable state, parameter uncertainties, and external load disturbance. First, the Chebyshev neural networks are introduced to approximate the nonlinear and unknown functions. Next, a neural network reducedorder state observer is designed to obtain the unmeasured state. Then, the command filtering approach based on the first-order Levant differentiator is developed to solve the ''explosion of complexity'' issue of backstepping, and a novel fractional-order error compensation mechanism is employed, which can remove the filtering errors in finite time. After that, the continuous frequency distributed model is investigated to design proper Lyapunov function, and it is demonstrated that the proposed control method not only ensures that all signals in the fractional-order PMSM system are bounded but also suppresses chaotic oscillation. Finally, the simulation studies are provided to verify the correctness and effectiveness of the proposed scheme.

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“…However, it is still possible to modify the behavior of such systems by means of a proper control input. This fact is the basis of successful applications in fields as optics [7][8][9][10], secure communications [11][12][13][14][15][16][17][18][19][20][21][22][23], finance [5,[24][25][26][27][28], power systems [29][30][31][32][33], electrical machinery [34][35][36], and so on.…”

Section: Introductionmentioning

confidence: 99%

Stabilization and Synchronization of Uncertain Zhang System by Means of Robust Adaptive Control

Pérez-Cruz

2018

Complexity

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Standard adaptive control is the preferred approach for stabilization and synchronization of chaotic systems when the structure of such systems is a priori known but the parameters are unknown. However, in the presence of unmodeled dynamics and/or disturbance, this approach is not effective anymore due to the drift of the parameter estimations, which eventually causes the instability of the closed-loop system. In this paper, a robustifying term, which consists of a saturation function, is used to avoid this problem. The robustifying term is added to the adaptive control law. Consequently, the learning law is also modified. The boundedness of the states and the parameter estimations is rigorously and thoroughly proven by means of Lyapunov like analysis based on Barbalat’s lemma. On these new conditions, the convergence to zero cannot be achieved due to the presence of unmodeled dynamics and/or disturbance. However, it is still possible to guarantee the asymptotic convergence to a bounded zone around zero. The width of this zone can be adjusted by the designer. The performance of this robust approach is verified by numerical simulations. Although, for simplicity, this strategy is only applied to the stabilization and synchronization of Zhang system, the procedure can easily be generalized to a broad class of chaotic systems.

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Chaos control of the permanent magnet synchronous motor with time-varying delay by using adaptive sliding mode control based on DSC

Cited by 42 publications

References 37 publications

Adaptive Neural Backstepping Control of Variable Stiffness Actuator

Adaptive Neural Backstepping Control of Variable Stiffness Actuator

Observer-Based Command Filtered Adaptive Neural Network Tracking Control for Fractional-Order Chaotic PMSM

Stabilization and Synchronization of Uncertain Zhang System by Means of Robust Adaptive Control

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