Model‐free adaptive tracking control for networked nonlinear systems with data dropout

Wang, Yuan; Wang, Zhanshan

doi:10.1002/rnc.5888

Cited by 10 publications

(2 citation statements)

References 55 publications

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“…[4][5][6] In recent years, the data-driven control (DDC) is getting much attention. [7][8][9] This approach includes adaptive dynamic programming, 10 iterative learning control, 11 model-free adaptive control [12][13][14] and so on. Generally speaking, the data-driven method refers to use the offline or online I/O data of the system, not the external information obtained from the explicit model.…”

Section: Introductionmentioning

confidence: 99%

Event‐triggered model‐free adaptive control for nonlinear systems with output saturation

Liu

2023

Intl J Robust & Nonlinear

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In this article, an original event‐triggered model‐free adaptive control is presented for nonlinear systems with output constraints. Firstly, a compact form dynamic linear model for this nonlinear system is established. Based on the output saturated data, the pseudo partial derivative (PPD) parameter is designed to identify the linear model. Then, a novel event‐triggered mechanism is inserted into the controller. The mechanism will be activated only if the event‐triggered error satisfies the predefined condition; Otherwise, it keeps the last triggering state. The purpose is to alleviate the transmission burden and save the communication resources. Varying from the existing studies, the main innovation of this article is that the controller is reconstructed in virtue of saturated data and event‐triggered condition. The present algorithm convergence is proved. Finally, the simulation study illustrates the feasibility of the plan.

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Section: Introductionmentioning

confidence: 99%

Event‐triggered model‐free adaptive control for nonlinear systems with output saturation

Liu

2023

Intl J Robust & Nonlinear

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“…For instance, the authors (Ge et al, 2020) introduce an RBFNN to estimate the compounded interference. The authors (Wang and Wang, 2022) introduce an RBFNN to compensate for the negative impacts of data dropout. Inspired by these methods, the RBFNN is employed to solve unknown nonlinear terms.…”

Section: Introductionmentioning

confidence: 99%

Neural adaptive finite-time dynamic surface control for the PMSM system with time delays and asymmetric time-varying output constraint

Zhang

et al. 2022

Journal of Vibration and Control

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This paper presents a neural adaptive finite-time dynamic surface control for the permanent magnet synchronous motor system with time delays and asymmetric time-varying output constraint. The core challenge is how to address the time delays and asymmetric output constraint when designing a finite-time control scheme. Given this, a proper Lyapunov–Krasovskii functional is introduced to address time delays, and a nonlinear transformation function is considered to convert the output-constrained problem into an unconstrained one. Then, a neural adaptive finite-time dynamic surface control approach is devised in the finite-time backstepping framework, which applies neural networks to estimate the unknown nonlinear functions and introduces first-order filters to solve the “explosion of complexity” problems. Furthermore, it is demonstrated that all the signals of the resulting system are finite-time stable and the tracking error converges to a neighborhood of origin in finite time without violating the output constraint. Finally, the simulation results show that the integration of squared error results, the integral of time and absolute error results as well as the integration of absolute value error results of the proposed scheme is smaller than the tested scheme by 0.3458 [Formula: see text], 22.2977 [Formula: see text], and 2.2513 [Formula: see text], respectively, when the time delays are considered. It further elucidates the availability and superiority of the developed method.

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Dynamic event‐triggered model‐free adaptive control for networked control systems with random packet loss

Sun,

Lu,

Yan

et al. 2024

Intl J Robust & Nonlinear

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A novel dual‐channel dynamic event‐triggered model‐free adaptive control method with compensation is proposed for nonlinear networked control systems (NCSs) subjected to random packet loss. In order to tackle the issue of redundant data transmission, a dual‐channel triggering control framework is designed, where data transmission only occurs upon meeting the triggering conditions. Compared with the traditional static event‐triggered schemes with fixed triggering thresholds, the proposed method allows for dynamically adjustable triggering thresholds. This means that the number of data transmissions in the forward and feedback channels can be further reduced. In addition, considering the detrimental impact of random packet loss and output event‐triggered on the system, compensation is applied to the system's output data using the linear data model of the nonlinear system, which is directly derived from I/O data without incorporating any other mechanistic model information, thereby ensuring optimal control performance. In contrast to existing results, the proposed control strategy can enhance system performance while conserving network communication resources. The effectiveness and superiority of the proposed scheme have been validated through two simulations.

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Model‐free adaptive tracking control for networked nonlinear systems with data dropout

Cited by 10 publications

References 55 publications

Event‐triggered model‐free adaptive control for nonlinear systems with output saturation

Event‐triggered model‐free adaptive control for nonlinear systems with output saturation

Neural adaptive finite-time dynamic surface control for the PMSM system with time delays and asymmetric time-varying output constraint

Dynamic event‐triggered model‐free adaptive control for networked control systems with random packet loss

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