Adaptive event-triggered output-feedback controller for uncertain nonlinear systems

Li, Fengzhong; Liu, Yungang

doi:10.1016/j.automatica.2020.109006

Cited by 69 publications

(50 citation statements)

References 27 publications

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“…It also seems natural to make the DTP and thus the event trigger dependent on some adaptive gain/parameter in such a way as to gain more adaptiveness during the scheduling and control codesign. For example, an adaptive event-triggered control method is presented in [98] for a class of single-input and single-output uncertain nonlinear systems, where the DTP and the controller gain are both adaptively adjusted via some adaptive weights. However, such an adaptive event-triggered control method may exhibit a potential limitation of practical implementation since it requires both the controller, normally remotely located, and the event-trigger, often locally embedded in an intelligent sensor device, to be synchronously orchestrated at all times.…”

Section: σ(T)mentioning

confidence: 99%

Dynamic Event-triggered Control and Estimation: A Survey

Han

Zhang

et al. 2021

Int. J. Autom. Comput.

140

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The efficient utilization of computation and communication resources became a critical design issue in a wide range of networked systems due to the finite computation and processing capabilities of system components (e.g., sensor, controller) and shared network bandwidth. Event-triggered mechanisms (ETMs) are regarded as a major paradigm shift in resource-constrained applications compared to the classical time-triggered mechanisms, which allows a trade-off to be achieved between desired control/estimation performance and improved resource efficiency. In recent years, dynamic event-triggered mechanisms (DETMs) are emerging as a promising enabler to fulfill more resource-efficient and flexible design requirements. This paper provides a comprehensive review of the latest developments in dynamic event-triggered control and estimation for networked systems. Firstly, a unified event-triggered control and estimation framework is established, which empowers several fundamental issues associated with the construction and implementation of the desired ETM and controller/estimator to be systematically investigated. Secondly, the motivations of DETMs and their main features and benefits are outlined. Then, two typical classes of DETMs based on auxiliary dynamic variables (ADVs) and dynamic threshold parameters (DTPs) are elaborated. In addition, the main techniques of constructing ADVs and DTPs are classified, and their corresponding analysis and design methods are discussed. Furthermore, three application examples are provided to evaluate different ETMs and verify how and under what conditions DETMs are superior to their static and periodic counterparts. Finally, several challenging issues are envisioned to direct the future research.

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Section: σ(T)mentioning

confidence: 99%

Dynamic Event-triggered Control and Estimation: A Survey

Han

Zhang

et al. 2021

Int. J. Autom. Comput.

140

View full text Add to dashboard Cite

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“…In comparison with SETM, DETM can generate a larger execution interval and save resources more effectively. Therefore, DETM is used to solve different problems [15][16][17][18][19][20][21][22][23][24][25][26][27][28]. For networked switched linear systems with time-varying delays [18], a DETM is constructed to guarantee the exponential stability of the closed-loop system, and the simulation comparison is performed to show that the proposed DETM can reduce the times of data transmissions.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Event-Triggered Adaptive Tracking Control for a Class of Unknown Stochastic Nonlinear Strict-Feedback Systems

et al. 2021

Symmetry

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In this paper, the dynamic event-triggered tracking control issue is studied for a class of unknown stochastic nonlinear systems with strict-feedback form. At first, neural networks (NNs) are used to approximate the unknown nonlinear functions. Then, a dynamic event-triggered controller (DETC) is designed through the adaptive backstepping method. Especially, the triggered threshold is dynamically adjusted. Compared with its corresponding static event-triggered mechanism (SETM), the dynamic event-triggered mechanism (DETM) can generate a larger execution interval and further save resources. Moreover, it is verified by two simulation examples that show that the closed-loop stochastic system signals are ultimately fourth moment semi-globally uniformly bounded (SGUUB).

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“…With the rapid development of networked control systems, including those described by partial differential equations (PDEs), 1–4 the burden of the shared (but limited) network has become more and more heavy. Facing this situation, event‐triggered control as a potential routine for saving communication/computation resources is proposed, in which the information transmission and controller updating are determined by a certain event on system behavior and hence only occur when necessary for system 5–11 . Despite the appealing advantage of event‐triggered control in saving communication/computation resources, the discontinuous behavior caused by the event‐triggering mechanism indeed challenges the design and analysis.…”

Section: Introductionmentioning

confidence: 99%

“…(ii) The considered system in this article consists of ODE subsystem and PDE subsystem with nonlocal term . Despite many results on the event‐triggered control problems of uncertain ODE systems (see, e.g., References 6–8 and references therein), the associated control design and performance analysis therein are essentially different from those for PDE‐ODE cascade systems. Moreover, although some results on the event‐triggered control problems of pure hyperbolic PDE systems and sandwiched hyperbolic PDE systems have been, respectively, obtained in works 1,2,19 and work, 22 the coexistence of the ODE and PDE subsystems and the presence of nonlocal term make the key techniques in this article, including the infinite‐dimensional backstepping transformations and the construction of Lyapunov function for stability analysis, extremely different from those in References 1,2,19,22.…”

Section: Introductionmentioning

confidence: 99%

Adaptive event‐triggered control for a class of uncertain hyperbolic PDE‐ODE cascade systems

Liu

et al. 2021

Intl J Robust & Nonlinear

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This article investigates the event‐triggered stabilization for a class of hyperbolic PDE‐ODE cascade systems with nonlocal term. The presence of unknown parameters in the system brings technical challenges from design to analysis. This compels us to integrate certain powerful dynamic compensation into event‐triggering control mechanism. To solve the problem, by adaptive technique, a compensation strategy is developed to counteract the parametric uncertainties. Then, a desired event‐triggered controller is delicately designed to guarantee that all the signals in the closed‐loop system are bounded and the original system states converge to zero in the L2 norm. Particularly, the event‐triggering mechanism with an additional dynamic signal in the threshold is devised to overcome the negative influence of execution error, while ensuring a positive lower bound for the interexecution intervals. Finally, a simulation example is given to illustrate the effectiveness of the proposed control strategy.

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Adaptive event-triggered output-feedback controller for uncertain nonlinear systems

Cited by 69 publications

References 27 publications

Dynamic Event-triggered Control and Estimation: A Survey

Dynamic Event-triggered Control and Estimation: A Survey

Dynamic Event-Triggered Adaptive Tracking Control for a Class of Unknown Stochastic Nonlinear Strict-Feedback Systems

Adaptive event‐triggered control for a class of uncertain hyperbolic PDE‐ODE cascade systems

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