Adaptive dynamic surface control of a class of pure feedback systems preceded by generalized P‐I hysteresis

Liu, Ye; Liu, Lulu; Li, Yuanyuan; Cao, Le

doi:10.1002/asjc.2433

Cited by 2 publications

(3 citation statements)

References 19 publications

(44 reference statements)

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“…Different from the traditional backstepping method, the derivative term in the backstepping method was replaced via the output of a first-order filter in the control signal. Likewise, the filtering error is introduced into the Lyapunov function [16]. On the premise of Lyapunov stability and smooth projection operator, a new neural network adaptive finite time DSC algorithm was proposed [17].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Dynamic Surface Control for a Class of Time Delay Nonlinear Systems with Extreme Learning Machine

jingyang

Liu

2022

Preprint

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This paper is aimed at investigating the problem of adaptive track- ing control for a class of strict-feedback nonlinear systems with unknown ex- ternal disturbance and time delay. First of all, the original system state equa- tion is transformed into a new form of state equation by coordinate trans- formation. In the next part, extreme learning machine is applied to approx- imate the unknown functions which exist in the whole system states. Based on this, adaptive dynamic surface controller is invented to cope with the dif- ferential explosion problem. In addition, the combination of the control law and the compensation signal of the filter improves the accuracy performance. Lyapunov-Krasovskii functional is introduced to handle the influence of time delay successfully. Subsequently, all signals of the whole closed-loop system can be ultimately uniformly bounded through a series of proofs. In the end, simulation examples are presented to verify the feasibility and effectiveness of the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%

Adaptive Dynamic Surface Control for a Class of Time Delay Nonlinear Systems with Extreme Learning Machine

jingyang

Liu

2022

Preprint

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“…Still, there exists an error in the measurement of the angle of rotation in the open-loop drive axis control of MEMS gyroscopes if any single system parameter is changed. Similarly, closed-loop driving control techniques, for example, phase-locked loop control, force balancing feedback control [6], automatic gain control [7], dynamic surface control (DSC) [8], adaptive control [9][10], and Lyapunov control [11] increased the throughput and range of the MEMS gyroscope than open-loop control, still, these are affected by the parameter variations and quadrature error which affects the angular rate. Most of the already proposed control schemes only present mathematical models and do not consider uncertainties as in [6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, sensitivity of the gyroscope is degraded due to the different factors like imperfections in the fabrication process, environmental changes which result in a change of parameters, the mechanical coupling of both axes, and several types of noises alongside axes. So, to boost the performance of the MEMS gyroscope multi-agent systems and control the drive axis oscillations, both closed-loop, and open-loop controllers can be used [5][6][7][8][9][10][11][12]. Still, there exists an error in the measurement of the angle of rotation in the open-loop drive axis control of MEMS gyroscopes if any single system parameter is changed.…”

Section: Introductionmentioning

confidence: 99%

Robust tracking and consensus control of a MEMS gyroscope multi-agent systems with H-infinity performance: A transfer function approach

Nasir

Ahmed

Aslam

et al. 2022

Preprint

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Microelectromechanical system (MEMS) devices face unique issues of control and measurement in contrast to conventional devices due to smaller size, lower manufacturing cost, and less power utilization. A MEMS gyroscope multi-agent system is one example of a MEMS device which have many industrial applications. Despite the significant industrial applications of MEMS gyroscope multi-agent systems such as airbag filling systems, stabilization of image, etc., it faces problems of stability and performance due to the existence of external disturbances, noise, and variations in the parameters. So, designing a consensus controller in the presence of uncertainties and disturbances is a challenging task. The basic purpose of this work is to investigate the issues faced by the MEMS gyroscope multi-agent system and propose a non-linear robust tracking and consensus control technique for the drive axis of the multi-agent system MEMS gyroscope. For this purpose, a controller is designed for the robust tracking and consensus control of MEMS gyroscope multi-agent system by the combination of H∞ consensus control and dynamic surface control approaches. The proposed H∞ consensus controller minimizes the external disturbance effects, makes the drive axis achieve resonance, and a tuning method is derived to tune the output response of the drive axis to the desired level. Further, we simulated a closed-loop system to discuss the controller features. In the last part of the paper, simulation examples are presented to verify the effectiveness of the proposed controller in the presence of uncertainties

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Adaptive dynamic surface control of a class of pure feedback systems preceded by generalized P‐I hysteresis

Cited by 2 publications

References 19 publications

Adaptive Dynamic Surface Control for a Class of Time Delay Nonlinear Systems with Extreme Learning Machine

Adaptive Dynamic Surface Control for a Class of Time Delay Nonlinear Systems with Extreme Learning Machine

Robust tracking and consensus control of a MEMS gyroscope multi-agent systems with H-infinity performance: A transfer function approach

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