Distributed adaptive output feedback containment control for time-delay nonlinear multiagent systems

Li, Yafeng; Park, Ju H.; Chen, Hua; Liu, Guopin

doi:10.1016/j.automatica.2021.109545

Cited by 32 publications

(23 citation statements)

References 36 publications

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“…The proposed distributed containment control scheme using quantized state feedback and communication signals is composed of (32)- (35). To achieve the boundedness of the errors between the quantized and unquantized signals regardless of unknown nonaffine nonlinearities, the error-normalization-based adaptive parts −z * a,i ||𝚿 * a,i || 2 θa,i are derived in (32) and (33). This is proved in the next section for the stability analysis part.…”

Section: Quantization-based Local Adaptive Tracker Designmentioning

confidence: 99%

“…[14][15][16][17] Furthermore, adaptive tracking control approaches have been widely studied for several uncertain nonlinear systems [18][19][20][21] and practical nonlinear systems such as nonlinear servo systems, [22][23][24][25] active suspension systems, 26 direct-current motor drive systems, 27,28 and fractional-order systems. 29 Neural networks and fuzzy logic systems have been employed to compensate for unknown nonparametric uncertainties in approximation-based adaptive containment control schemes of nonlinear multi-agent systems in strict-feedback [30][31][32][33] and pure-feedback forms. 34,35 To increase the efficiency of signal transmission under capacity-limited networks, quantization-based distributed cooperative control problems have been investigated for uncertain nonlinear multi-agent systems.…”

Section: Introductionmentioning

confidence: 99%

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Distributed containment control of networked uncertain MIMO pure‐feedback nonlinear systems via quantized state feedback and communication

Kim

Yoo

2022

Intl J Robust & Nonlinear

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This article presents a distributed adaptive containment control strategy using quantized state feedback and communication for uncertain multiple‐input‐multiple‐output pure‐feedback nonlinear multi‐agent systems with state quantization. It is assumed that quantized state variables of multiple followers and quantized outputs of multiple dynamic leaders are only available for constructing local adaptive feedback controllers of followers under a directed network. Compared with existing containment control approaches, the primary contribution of this study is dealing with discontinuously quantized state feedback and communication problems for containment control design in the presence of unknown nonaffine nonlinearities. Based on quantized state and communication information, local adaptive neural network trackers of followers and their adaptive tuning laws are developed to guarantee that the trajectories of all followers converge to the dynamic convex hull spanned by the multiple leaders regardless of unknown pure‐feedback nonlinearities. By analyzing the stability of the local quantization errors between quantized signals and original signals, we prove the boundedness of all closed‐loop signals and the convergence of the containment errors to a sufficiently small domain around the origin. Simulation examples involving flexible‐joint manipulators are presented to validate the proposed quantized feedback scheme.

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Section: Quantization-based Local Adaptive Tracker Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed containment control of networked uncertain MIMO pure‐feedback nonlinear systems via quantized state feedback and communication

Kim

Yoo

2022

Intl J Robust & Nonlinear

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“…where the differentiations of the LK functions Q 1 and R 1 are given in ( 16) and (17). By using the following inequalities…”

Section: Stability Analysismentioning

confidence: 99%

“…Numerous studies have employed LK and LR functions to investigate different classes of nonlinear systems, with a significant portion explicitly addressing constant state delays [8][9][10][11][12][13][14] or time-varying state delays. [15][16][17][18][19][20][21][22] However, the LR functions method necessitates strict adherence to stability conditions, which can result in conservative conclusions. Consequently, recent research has predominantly emphasized the adoption of the LK functions method.…”

Section: Introductionmentioning

confidence: 99%

Finite‐time tracking control for time‐delayed pure‐feedback systems with guaranteed performance

Dai,

Wang,

Wang

2023

Intl J Robust & Nonlinear

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This article investigates the finite‐time tracking control problem with guaranteed performance for a class of nonlinear pure‐feedback systems subject to both time‐varying state delays and control input delays. Both the state delays and control input delays are assumed to be unknown yet bounded by some known constants. A new performance function is introduced that ensures the finite‐time convergence of tracking errors and guaranteed performance. The key to handling both the states and input delays lies in a novel construction of the Lyapunov–Krasovskii (LK) function, which also avoids the reconstruction of the LK function in stability analysis. In addition, a continuous package function is introduced, which allows the unknown nonlinear terms arising from the uncertain perturbation and the derivation of the LK function to be eliminated. Under the proposed method, the semi‐global uniform ultimate boundedness of all signals in the closed‐loop system can also be ensured. Simulation examples are provided to demonstrate the effectiveness of the proposed approach in this article.

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“…Existing control theory research results contain no solutions to this problem. Some researchers [23,24] study the robot's motion control problem by restricting the motion of the underwater robot-like vehicle, assuming that the AUV's trajectory is in a plane and does not involve space motion [25,26]. Such assumptions can simplify the motion problem from the AUV's six degrees of freedom to the three degrees of freedom.…”

Section: Introductionmentioning

confidence: 99%

Backstepping Control Strategy of an Autonomous Underwater Vehicle Based on Probability Gain

2022

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In this paper, an underwater robot system with nonlinear characteristics is studied by a backstepping method. Based on the state preservation problem of an Autonomous Underwater Vehicle (AUV), this paper applies the backstepping probabilistic gain controller to the nonlinear system of the AUV for the first time. Under the comprehensive influence of underwater resistance, turbulence, and driving force, the motion of the AUV has strong coupling, strong nonlinearity, and an unpredictable state. At this time, the system’s output feedback can solve the problem of an unmeasurable state. In order to achieve a good control effect and extend the cruising range of the AUV, first, this paper will select the state error to make it a new control objective. The system’s control is transformed into the selection of system parameters, which greatly simplifies the degree of calculation. Second, this paper introduces the concept of a stochastic backstepping control strategy, in which the robot’s actuators work discontinuously. The actuator works only when there is a random disturbance, and the control effect is not diminished. Finally, the backstepping probabilistic gain controller is designed according to the nonlinear system applied to the simulation model for verification, and the final result confirms the effect of the controller design.

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Distributed adaptive output feedback containment control for time-delay nonlinear multiagent systems

Cited by 32 publications

References 36 publications

Distributed containment control of networked uncertain MIMO pure‐feedback nonlinear systems via quantized state feedback and communication

Distributed containment control of networked uncertain MIMO pure‐feedback nonlinear systems via quantized state feedback and communication

Finite‐time tracking control for time‐delayed pure‐feedback systems with guaranteed performance

Backstepping Control Strategy of an Autonomous Underwater Vehicle Based on Probability Gain

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