How often should one update control and estimation: review of networked triggering techniques

In this article, an event-triggered guaranteed cost optimal tracking control problem is investigated for a class of uncertain nonlinear system with partial loss of actuator effectiveness faults. To begin with, an augmented system consisted of error system and reference system is constructed to simplify the tracking controller design. Then, in order to consider both actuator faults and system uncertainties in optimal tracking control, an improved discounted cost function is developed. Furthermore, a single critic neural network adaptive dynamic programming algorithm is utilized to implement the event-based approximate optimal controller design. Different from static event-triggered mechanism, the dynamic event-triggered mechanism proposed in this article with an internal dynamic signal can further improve execution efficiency. A Lyapunov-based stability analysis is given to demonstrate the uniformly ultimately bounded stability of the closed-loop system. Finally, two typical nonlinear simulations are presented to verify the developed control method.

Section: Dynamic Event-triggered Mechanismmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Event‐triggered guaranteed cost fault‐tolerant optimal tracking control for uncertain nonlinear system via adaptive dynamic programming

Guo

Yao

Bai

et al. 2021

“…Its aim is to design a controller such that all states or outputs of agents in multiagent systems converge to a common value. Some published results on the synchronization have been displayed in References 1‐11, just to name a few. These results were obtained based on a common hypothesis that there exists a safe data exchange between agents via a shared network.…”

Section: Introductionmentioning

confidence: 99%

Event‐triggered output synchronization for nonhomogeneous agent systems with periodic denial‐of‐service attacks

Fang

Pan

et al. 2020

125

Summary This article presents a two‐layered framework to investigate the output synchronization problem in nonhomogeneous agent systems in the presence of periodic energy‐limited denial‐of‐service (DoS) attacks with an event‐triggered control strategy. Under the developed framework, the proposed controller for different agents is composed of a dynamic compensator and a regulator, where the dynamic compensator on the first layer can copy the dynamics of the leader node. DoS attacks with a periodic (partially known) attack strategy are based on a time sequence and interrupt the data exchange over a shard network consisting of the leader node and dynamic compensators in the first layer. An event‐triggered control protocol is proposed to reduce the update frequency of the controller, and it also ensures that the output of every dynamic compensator can track the output of the leader node without occurring “Zeno phenomenon.” Based on the output regulator theory, the regulator for every agent can guarantee that all nonhomogeneous agents can converge to the trajectory of every dynamic compensator in the bottom layer. Finally, a simulation example is demonstrated to verify the new design method developed.

“…On account of the extensively practical applications, studies on adaptive trajectory tracking control design for uncertain nonlinear systems have made a major breakthrough, such as adaptive control, sliding mode control, robust control, fault tolerant control, and so on. In addition, backstepping‐based adaptive neural control and fuzzy control for nonlinear systems have been well studied in the past few years, in which fuzzy logic systems (FLSs) or neural networks (NNs) were viewed as universal approximators to identify the uncertain system nonlinearities, and then, an adaptive controller was designed by combining with the backstepping technique.…”

Section: Introductionmentioning

confidence: 99%

Asymptotic tracking control for constrained nonstrict‐feedback MIMO nonlinear systems via parameter compensations

Pan

Chadli

et al. 2020

This paper studies the problem of adaptive fuzzy asymptotic tracking control for multiple input multiple output nonlinear systems in nonstrict-feedback form.Full state constraints, input quantization, and unknown control direction are simultaneously considered in the systems. By using the fuzzy logic systems, the unknown nonlinear functions are identified. A modified partition of variables is introduced to handle the difficulty caused by nonstrict-feedback structure. In each step of the backstepping design, the symmetric barrier Lyapunov functions are designed to avoid the breach of the state constraints, and the issues of overparametrization and unknown control direction are settled via introducing two compensation functions and the property of Nussbaum function, respectively. Furthermore, an adaptive fuzzy asymptotic tracking control strategy is raised.Based on Lyapunov stability analysis, the developed control strategy can effectually ensure that all the system variables are bounded, and the tracking errors asymptotically converge to zero. Eventually, simulation results are supplied to verify the feasibility of the proposed scheme. K E Y W O R D Sadaptive fuzzy control, asymptotic tracking control, nonstrict-feedback system, state constraints, unknown control direction INTRODUCTIONOn account of the extensively practical applications, studies on adaptive trajectory tracking control design for uncertain nonlinear systems have made a major breakthrough, such as adaptive control, 1-3 sliding mode control, 4-6 robust control, 7-9 fault tolerant control, 10,11 and so on. In addition, backstepping-based adaptive neural control 12-18 and fuzzy control [19][20][21][22][23][24][25][26][27][28][29][30] for nonlinear systems have been well studied in the past few years, in which fuzzy logic systems (FLSs) or neural networks (NNs) were viewed as universal approximators to identify the uncertain system nonlinearities, and then, an adaptive controller was designed by combining with the backstepping technique. Cite several examples, based on FLS's online approximation capability, a new adaptive fuzzy control scheme was presented in Reference 20. Tong et al citebib21 established a fuzzy state observer and proposed a decentralized adaptive fuzzy optimal control approach for nonlinear large-scale systems. In Reference 22, the fuzzy adaptive output feedback controller was constructed for strict-feedback nonlinear systems, where the problem of virtual control coefficients was well handled by using a convex combination technique. Int J Robust Nonlinear Control. 2020;30:3365-3381. wileyonlinelibrary.com/journal/rnc