Control of high-order nonlinear systems under error-to-actuator based event-triggered framework

Wang, Huanqing; Ling, Song; Liu, Peter; Li, Yuan‐Xin

doi:10.1080/00207179.2021.1934734

Cited by 25 publications

(20 citation statements)

References 48 publications

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“…By utilizing Lemmas 2 and 4, (22), and ( 23), one has , where the Theorem 1. Considering the strong interconnected nonlinear stochastic system (1) that including the state observer (12), the command filter ( 21), the compensating signals of the command filter (24), the virtual control signals ( 43), (53), and (67), the adaptive laws ( 44), (54), and (68), and the dynamic event-triggered controller (66) under Assumptions 1 and 2. Then the boundedness in probability of the all signals in the systems can be guaranteed for the bounded initial conditions, and the tracking errors and observer errors can eventually converge to a small neighborhood of the origin.…”

Section: Controller Design and Stability Analysismentioning

confidence: 99%

“…On the other hand, due to the increasing demand for communication resources in the practical system, the phenomenon of communication resources waste has been paid more and more attention. In order to reduce the waste of communication resources, the event-triggered control method [17][18][19][20][21][22][23][24] has been further developed. Different from the traditional periodicity-based time-triggered control schemes, the execution times of event-triggered control schemes are determined by predefined events, which makes the demand of communication resources greatly reduced.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive neural network dynamic event‐triggered control for strong interconnected stochastic nonlinear systems with output constraint

Zhu

2022

Intl J Robust & Nonlinear

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This paper proposes a dynamic event‐triggered mechanism based command filtered adaptive neural network (NN) tracking control scheme for strong interconnected stochastic nonlinear systems with time‐varying output constraints. By designing a state observer, the unmeasured states of the systems can be estimated. The NNs are utilized to handle the unknown intermediate functions. In the controller design process, the asymmetric time‐varying barrier Lyapunov functions are used to guarantee that the systems outputs do not violate the constraint regions. By integrating the command filter with variable separation technique, the controller design process is more simple, and the problem of algebraic‐loop can be solved which caused by interconnected functions. According to the Lyapunov stability theory, it can be ensured that all signals of the systems are bounded in probability. Finally, the availability of the developed control scheme can be showed by the simulation example.

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Section: Controller Design and Stability Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive neural network dynamic event‐triggered control for strong interconnected stochastic nonlinear systems with output constraint

Zhu

2022

Intl J Robust & Nonlinear

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“…As a special class of dynamical systems [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], Markovian switching systems (MSSs) can effectively deal with abrupt changes in system parameters and structures [41], [42], [43], [44], [45]. To date, numerous systems are modeled as MSSs, including network communication systems, process control systems, economic systems, etc; for details, see [46], [47].…”

Section: Introductionmentioning

confidence: 99%

SMC for Discrete-Time Networked Semi-Markovian Switching Systems With Random DoS Attacks and Applications

Hou

Park

et al. 2023

IEEE Trans. Syst. Man Cybern, Syst.

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This article concentrates on the discrete-time sliding mode control (DTSMC) problem for uncertain networked semi-Markovian switching systems (S-MSSs) under random denialof-service (DoS) attacks. The semi-Markovian kernel (SMK) approach is utilized such that the switching among different modes is mutually governed by the transition probability and the sojourn-time distribution function. Considering randomly occurring DoS attacks, a sliding mode function related to the attack probability is constructed to analyze the impact of malicious attacks. Then, sufficient conditions under the equivalent DTSMC law are derived in view of the σ -error mean square stability criterion. Furthermore, the synthesis problem of the proposed DTSMC law ensures that the resulting closed-loop system dynamics can be driven onto the prespecified sliding region within a limited time. Finally, an electronic throttle control system is shown to validate the proposed algorithm.

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“…During the last decade, intelligent approximators including neural networks (NNs) and fuzzy logic systems (FLSs) have been coupled with the backstepping technique to overcome this difficulty and these approaches have shown to be promising. 12,[23][24][25] Because of simplifications or misconceptions, measurement noises, and modeling errors, a part of the system dynamics may not be considered in modeling which is called unmodeled dynamic and may lead to degradation of controller performance and even instability. Consequently, several works have used a dynamical signal to cope with this problem.…”

Section: Introductionmentioning

confidence: 99%

Fixed‐time fault‐tolerant control of uncertain MIMO switched interconnected systems subject to state delay, unknown control directions, and quantized nonlinear inputs

Ghoreishee

Shahrokhi

Vafa

et al. 2022

Adaptive Control & Signal

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Summary This paper investigates design of an adaptive fixed‐time fault‐tolerant decentralized controller for a class of uncertain multi‐input multi‐output (MIMO) switched large‐scale non‐strict interconnected systems under arbitrary switching subject to unknown control directions, quantized nonlinear inputs, actuator failures unknown external disturbances, and unmodeled dynamics. In addition to interconnected terms, time‐varying delayed interconnected terms have been considered in the system model which makes it more general than previous works in the literature. The proposed controller can handle switched systems with unknown switching signal and different types of input nonlinearities including, saturation, backlash, and dead‐zone. The singularity problem in designing the fixed time controller has been solved. The quantizer and actuators fault parameters are assumed to be unknown. The Razumikhin lemma has been used to deal with the delayed interconnected terms. To cope with the system unknown dynamics, neural networks (NNs) have been applied and by updating the maximum norms of the networks weight vectors the computational load has been reduced. The explosion of complexity occurring in the traditional back‐stepping technique has been avoided by applying dynamic surface control (DSC). Finally, by defining an appropriate common Lyapunov function (CLF), fixed‐time convergence of system outputs and the closed‐loop system stability have been established. The effectiveness of the proposed controller has been shown via simulation study.

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Control of high-order nonlinear systems under error-to-actuator based event-triggered framework

Cited by 25 publications

References 48 publications

Adaptive neural network dynamic event‐triggered control for strong interconnected stochastic nonlinear systems with output constraint

Adaptive neural network dynamic event‐triggered control for strong interconnected stochastic nonlinear systems with output constraint

SMC for Discrete-Time Networked Semi-Markovian Switching Systems With Random DoS Attacks and Applications

Fixed‐time fault‐tolerant control of uncertain MIMO switched interconnected systems subject to state delay, unknown control directions, and quantized nonlinear inputs

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