Adaptive inverse control of non-linear systems with unknown complex hysteretic non-linearities

Li, Zhong; Hu, Yueming; Liu, Yu; Chen, T.; Yuan, Peisen

doi:10.1049/iet-cta.2010.0740

Cited by 25 publications

(11 citation statements)

References 23 publications

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“…That means the boundedness of z, θ, θ and νi is ensured. From the control signal α i in ( 20), ( 26), (32) is also bounded for i = 1, • • • , n -1. The boundedness of the desired control signal ω(t) can also be deduced based on (41), (37) and (10).…”

Section: System Stability Analysismentioning

confidence: 99%

Event-triggered Adaptive Estimated Inverse Neural Network Control of Uncertain Nonlinear Systems With Input Hysteresis Nonlinearity

Yao,

Zhou,

Deng

2023

Preprint

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This article investigates an event-triggered adaptive estimated inverse control scheme for a class of uncertain nonlinear systems with hysteresis effects, parametric uncertainties and disturbances. An online estimated inverse hysteresis compensation mechanism is developed, where an adaptive technique is employed to obtain the value of unknown hysteresis parameters. Compared with the common approaches, its biggest advantage lies in that it is not necessary to obtain the hysteresis parameters by means of experiment, which relaxes time-consuming off-line identification work.Moreover, an adaptive radial basis functions neural network (RBFNN) is utilized to approximate the unknown disturbances, whose weight coefficients along with parametric uncertainties are all estimated by the adaptive technique. Besides, the communication cost can be largely saved by introducing the relative threshold event-triggered control (ETC). Through Lyapunov analysis, the proposed controller guarantees the boundedness of all the signals and the convergence of the error signals. The results of numerical simulation illustrate the effectiveness and superiority of the developed controller.

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Section: System Stability Analysismentioning

confidence: 99%

Event-triggered Adaptive Estimated Inverse Neural Network Control of Uncertain Nonlinear Systems With Input Hysteresis Nonlinearity

Yao,

Zhou,

Deng

2023

Preprint

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“…Remark 6: To avoid the possible chattering problem caused by the sign function, the sign function in (15) and (33) can be replaced by a hyperbolic tangent function as in [19]:…”

Section: A Full State Feedback Controlmentioning

confidence: 99%

“…The simulation results show that the control strategy is still effective in the presence of parameter perturbations of the hysteresis model. In [19], the continuous Prandtl-Ishlinskii model is decomposed into a finite number of discrete Prandtl-Ishlinskii operators, and the hysteresis is compensated by using the analytical inverse hysteresis model. For the error caused by the continuous model being converted into a discrete model, an adaptive control technique is used to compensate online.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with [18], [20], this method uses the TDE technique to realize the on-line estimation of the nonlinear term of hysteresis, which can be directly used in the controller design, instead of treating the hysteresis nonlinearity as a bounded disturbance. Compared with the feedbackfeedforward control in [17], [19], the proposed method does not need to construct the hysteresis inverse, and only one parameter of the hysteresis is used in the controller, which effectively reduces the calculation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Control of a Piezo-Positioning Mechanism With Hysteresis and Input Saturation Using Time Delay Estimation

Li¹,

Zeng²,

Chen³

et al. 2020

IEEE Access

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In this paper, based on backstepping technique and time delay estimation (TDE) technique, an adaptive time delay compensation control scheme is developed for a class of piezoelectric positioning mechanical systems with Bouc-Wen hysteresis and input saturation constraint. The nonlinear part of the Bouc-Wen model is estimated online by TDE, and the TDE error introduced by TDE is compensated online by an adaptive law. Furthermore, an auxiliary variable system is used to deal with the input saturation constraint. Based on the Lyapunov method, the stability of the closed-loop system is analyzed and proved. Two simulation examples are given to demonstrate the effectiveness of the proposed control scheme. INDEX TERMS Hysteresis, time delay control, adaptive control, backstepping.

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“…Using the inverses as feedforward compensators for the controller designs, the representative works can be found in, see, for example, [2,[9][10][11][12]. In particular, in [13], an adaptive variable structure controller has been developed along with the inverse construction for the MGPI model.…”

Section: Introductionmentioning

confidence: 99%

Inverse error analysis and adaptive output feedback control of uncertain systems preceded with hysteresis actuators

Liu

2014

IET Control Theory & Applications

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The development of control approaches for systems preceded with hysteresis non-linearities has received great attentions in recent decades. The most common approach is the construction of an inverse model as the compensator to mitigate hysteresis effects. However, most of the developed schemes are state-based, requiring the availability of states of systems, which may not be the case for some practical systems. In this study, output control with inverse compensation will be addressed. By using the inverse as a feedforward compensator for the model described by the modified generalised Prandtl-Ishlinskii model, an corresponding analytical expression of the inverse compensation error is first obtained. Then, an observer-based robust adaptive output feedback controller is developed. It is shown that the proposed output feedback control scheme can not only guarantee the stability of the control systems, but also can achieve the desired tracking accuracy.

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Adaptive inverse control of non-linear systems with unknown complex hysteretic non-linearities

Cited by 25 publications

References 23 publications

Event-triggered Adaptive Estimated Inverse Neural Network Control of Uncertain Nonlinear Systems With Input Hysteresis Nonlinearity

Event-triggered Adaptive Estimated Inverse Neural Network Control of Uncertain Nonlinear Systems With Input Hysteresis Nonlinearity

Adaptive Control of a Piezo-Positioning Mechanism With Hysteresis and Input Saturation Using Time Delay Estimation

Inverse error analysis and adaptive output feedback control of uncertain systems preceded with hysteresis actuators

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