Event-triggered adaptive NN tracking control with dynamic gain for a class of unknown nonlinear systems

Li, Jing; Liu, Han; Zhang, Zhaohui; Li, Xiaobo; Yang, Xiaoli

doi:10.1016/j.neucom.2021.09.069

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…□ Lemma 4. For the whole system, combining the multi-model adaptive parameters ( 9)-( 14) and the distributed adaptive laws (15), and ( 16), then ∃T i4 , when T > T i4 , and the multi-model adaptive controllers are converted into a normal single one.…”

Section: Auxiliary Lemmasmentioning

confidence: 99%

“…Theorem 1. If Assumptions 1-4 hold, under the multi-model adaptive parameters ( 9)-( 14) and the distributed adaptive laws (15), and ( 16), a discrete-time non-linearly parameterized heterogeneous MAS (1) exhibits the following performance:…”

Section: Tracking Performance Of the Multi-agent Systemmentioning

confidence: 99%

“…For each agent, it is usually hard and often nearly impossible to create an accurate dynamic model. To deal with the various uncertainties, adaptive control to solve the unknown parameters and structural uncertainties has been studied using different methods [14,15].…”

Section: Introductionmentioning

confidence: 99%

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Distributed Adaptive Tracking Control of Hidden Leader-Follower Multi-Agent Systems with Unknown Parameters

Yang,

Lee

2024

Mathematics

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The distributed leader-follower control of multi-agent systems is discussed. Each agent is expressed in a discrete-time and non-linear dynamic model with an unknown parameter and can be affected by its neighbors’ history information. For each agent, to identify the parameter, one switching set of the parameter estimates is constructed and the optimal parameter estimate is chosen based on the index switching function. Using the given desired reference signal, the leader agent’s control law is designed, and relying on the neighbors’ history information, each follower agent’s local control law is designed. With the designed distributed tracking adaptive control laws, the whole system tracks the given desired reference signal, and in the face of strong couplings the closed-loop system ultimately reaches an agreement. Finally, by comparing simulations of the control strategy with a normal projection algorithm, the results indicate that the adaptive control method with a switching set of the parameter estimates is effective in improving the control performance.

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Section: Auxiliary Lemmasmentioning

confidence: 99%

Section: Tracking Performance Of the Multi-agent Systemmentioning

confidence: 99%

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Distributed Adaptive Tracking Control of Hidden Leader-Follower Multi-Agent Systems with Unknown Parameters

Yang,

Lee

2024

Mathematics

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“…Both unknown dead-zone and model-free dynamics are considered simultaneously in [25] for multiagent systems. Adaptive ETC RBFNN back stepping control is studied more, including in [26] for MIMO switched nonlinear systems with output and state constraints and non-input-to-state practically stable (ISpS) model-free dynamics, in [20] for completely unknown nonlinear functions with dynamic gain, and in [27] for underactuated marine surface vessels using an NN-based disturbance estimator. Adaptive ETC RBF NN is also presented in [21] for a class of single-input-single-output uncertain nonlinear continuous-time (CT) systems by integrating input-tostate linearization techniques, impulsive dynamical system and RBF NN with adaptive ETC threshold.…”

Section: B Neural Network (Nns)mentioning

confidence: 99%

Machine Learning in Event-Triggered Control: Recent Advances and Open Issues

et al. 2022

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Network control systems have gained considerable attention over the last decade as a result of the trend towards decentralised control applications and the emergence of cyber-physical system applications. However, real-world network control systems suffer from limited communication bandwidths, reliability issues, and a lack of awareness of network dynamics due to the complex nature of wireless networks. Combining machine learning and event-triggered control has the potential to alleviate some of these issues. For example, machine learning can be used to overcome the problem of a lack of network models by learning system behavior or adapting to dynamically changing models by continuously learning model dynamics. Event-triggered control can help to conserve communication bandwidth by transmitting control information only when necessary or when resources are available. The purpose of this article is to conduct a review of the literature on the use of machine learning in combination with event-triggered control. Machine learning techniques such as statistical learning, neural networks, and reinforcement learning-based approaches such as deep reinforcement learning are being investigated in combination with event-triggered control. We discuss how these learning algorithms can be used for different applications depending on the purpose of the machine learning use. Following the review and discussion of the literature, we highlight open research questions and challenges associated with machine learning-based event-triggered control and suggest potential solutions.

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Event‐triggered practical tracking control via switching for a class of uncertain high‐order nonlinear systems with unknown input powers and control directions

Li,

Liang,

et al. 2024

Intl J Robust & Nonlinear

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This article is devoted to the event‐triggered practical tracking control for a class of high‐order nonlinear systems with serious uncertainties. Remarkably, the serious uncertainties are greatly reflected from the unknown control directions and input powers which give rise to essential obstacles in control design and hence lead to incapability of the traditional control methods on this topic. To solve the control problem, a novel switching event‐triggered control scheme is proposed by a skillful incorporation of logic‐based switching mechanism into the event‐triggered one. Specifically, two pivotal events are smartly chosen to give the event‐triggered mechanism for the determination of the sampling times. In particular, one of the events is used to give the switching varying mechanism, by which the controller parameters are respectively switched in two groups of sequences which are carefully chosen for the compensation of serious uncertainties. Under the proposed switching event‐triggered mechanism, a state‐feedback controller is designed which guarantees that all the signals of the closed‐loop system are bounded while system output practically tracks the reference signal, along with a finite number of switching of controller parameters as well as the exclusion of the Zeno phenomenon. Finally, simulation examples are provided to validate the effectiveness of the proposed theoretical results.

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Event-triggered adaptive NN tracking control with dynamic gain for a class of unknown nonlinear systems

Cited by 7 publications

References 16 publications

Distributed Adaptive Tracking Control of Hidden Leader-Follower Multi-Agent Systems with Unknown Parameters

Distributed Adaptive Tracking Control of Hidden Leader-Follower Multi-Agent Systems with Unknown Parameters

Machine Learning in Event-Triggered Control: Recent Advances and Open Issues

Event‐triggered practical tracking control via switching for a class of uncertain high‐order nonlinear systems with unknown input powers and control directions

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