Robust adaptive output‐feedback control for a class of nonlinear systems with hysteresis compensation controller

Zhang, Xiuyu; Zhi, Li; Su, Cheng‐Kuan; Lin, Yan

doi:10.1002/acs.2790

Cited by 6 publications

(5 citation statements)

References 23 publications

(59 reference statements)

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“…Same as that in previous DSC methods [4], [18]- [20], from ( 11)-( 13), ( 28)-( 42), there are continuous nonnegative functions B i+1 , i = 1, • • • , ρ − 1 such that the following inequations hold:…”

Section: Definementioning

confidence: 94%

Adaptive Dynamic Surface Control for a Class of Dead-Zone Nonlinear Systems via Output Feedback

et al. 2021

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The paper aims to address output feedback problem for a class of nonlinear systems subjected to unknown dead-zone. High-gain K-filters is firstly designed such that unavailable states of system can be reconstructed. Then, with the help of dynamic surface control (DSC) method, an adaptive output feedback controller design is established, which can alleviate undesired influence of the dead-zone and guarantee the semi-global stability of the system. Furthermore, the L ∞ performance of tracking error is achieved by means of initialization technique. Ultimately, numerical and practical examples clarify the efficiency of the proposed solution.INDEX TERMS Dynamic surface control; Adaptive control; High-gain observer; Dead-zone; Nonlinear systems.

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Section: Definementioning

confidence: 94%

Adaptive Dynamic Surface Control for a Class of Dead-Zone Nonlinear Systems via Output Feedback

et al. 2021

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“…, then, (24) can be rewritten as: S ensures the tracking error e always included in the performance function () pt [10]. End of proof.…”

Section: Bymentioning

confidence: 99%

Adaptive Dynamic Surface Control of Buck Converter Based on Energy Storage System

Yang

Guan

et al. 2023

J. Phys.: Conf. Ser.

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To enhance the stability of the Buck converter and the capacity of resisting disturbance of the controller and converter, come up with an adaptive dynamic surface control strategy based on voltage mode control, which improves the ability of the energy storage system to stabilize the output voltage when the input voltage or load changes. The Radial Basis Function (RBF) neural network is combined with the finite covering lemma to approximate the unknown nonlinear functions with time-delay in the system. The error conversion and performance functions are adopted to ensure the tracking performance index. The adaptive dynamic surface controller is applied to ensure the stability of the output voltage of the system when the external conditions change. The proposed control method is tested on the hardware-in-the-loop (HIL) experimental platform. The consequence illustrate that the designed controller is feasible and has good voltage stability performance.

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“…Generally, there are two types of hysteresis modeling methods. One is to establish the model based on the physical equations or parameters of the mechanism, such as Stoner-Wohlfarth model and Globus model [16]; another is to build the model based on the input-output relationship of the mechanism, which does not need the physical parameters of the system and can be extended to the different systems easily, for example, Preisach model [17], Prandtl-Ishlinskii model [18,19], Bouc-Wen model [20], and Jiles-Atherton (J-A) model [21]. Therein, the Jiles-Atherton modeling method originates from the hysteresis description in the magnetic field, and it can provide an accurate description for the strong saturated hysteresis in the SMA-based compliant actuators [22].…”

Section: Introductionmentioning

confidence: 99%

Jiles‐Atherton Based Hysteresis Identification of Shape Memory Alloy‐Actuating Compliant Mechanism via Modified Particle Swarm Optimization Algorithm

Chen

Feng

et al. 2019

Complexity

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Shape memory alloy- (SMA-) based actuators are widely applied in the compliant actuating systems. However, the measured data of the SMA-based compliant actuating system reveal the input-output hysteresis behavior, and the actuating precision of the compliant actuating system could be degraded by such hysteresis nonlinearities. To characterize such nonlinearities in the SMA-based compliant actuator precisely, a Jiles-Atherton model is adopted in this paper, and a modified particle swarm optimization (MPSO) algorithm is proposed to identify the parameters in the Jiles-Atherton model, which is a combination of several differential nonlinear equations. Compared with the basic PSO identification algorithm, the designed MPSO algorithm can reduce the local optimum problem so that the Jiles-Atherton model with the identified parameters can show good agreements with the measured experimental data. The good capture ability of the proposed identification algorithm is also examined through the comparisons with Jiles-Atherton model using the basic PSO identification algorithm.

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Robust adaptive output‐feedback control for a class of nonlinear systems with hysteresis compensation controller

Cited by 6 publications

References 23 publications

Adaptive Dynamic Surface Control for a Class of Dead-Zone Nonlinear Systems via Output Feedback

Adaptive Dynamic Surface Control for a Class of Dead-Zone Nonlinear Systems via Output Feedback

Adaptive Dynamic Surface Control of Buck Converter Based on Energy Storage System

Jiles‐Atherton Based Hysteresis Identification of Shape Memory Alloy‐Actuating Compliant Mechanism via Modified Particle Swarm Optimization Algorithm

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