Adaptive fault‐tolerant containment control for stochastic nonlinear multi‐agent systems with input saturation

Cheng, Shen; Cheng, Zhijian; Ren, Hongru; Lu, Renquan

doi:10.1002/oca.2899

Cited by 7 publications

(11 citation statements)

References 49 publications

(82 reference statements)

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“…Reference 51 has addressed the event‐triggered containment control problem of stochastic MASs which reduced the computation burden. Reference 52 had considered containment control problem for SNMASs in the presence of input saturation and sensor faults. Although References 50–52 discusses the containment control problem of stochastic MASs, stochastic nonlinear control and containment control as two important research areas, the literatures on this research field are still far from sufficient.…”

Section: Introductionmentioning

confidence: 99%

Fast finite‐time adaptive backstepping containment control for high‐order stochastic nonlinear multi‐agent systems

Song,

Zhao

2023

Adaptive Control & Signal

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SummaryIn this paper, the fast finite‐time backstepping containment control strategy is considered for high‐order stochastic multi‐agent systems. The addition of the finite‐time command filter avoids calculating explosion which occurs in the differential process of virtual control signals for the high‐order system on the traditional backstepping and makes convergence speed of control system faster. The influence of filtering errors generated by filter for control systems is eliminated by establishing error compensation systems. The fuzzy logic systems approximate unknown dynamics of systems. It proves the closed‐loop systems are practically fast finite‐time stable in mean square. The given simulation results show the effectiveness of the proposed control strategy.

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Section: Introductionmentioning

confidence: 99%

Fast finite‐time adaptive backstepping containment control for high‐order stochastic nonlinear multi‐agent systems

Song,

Zhao

2023

Adaptive Control & Signal

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“…The fifth group of papers 24‐27 discusses data‐based control for distributed control systems. A mission‐driven control scheme, including a consensus‐based near‐optimal formation controller and a finite‐time precise formation controller, is proposed aiming at different requirements of unmanned aerial vehicle swarm 24 .…”

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confidence: 99%

“…The neighbor Q‐learning based consensus control algorithm is developed for discrete‐time multiagent systems 26 . The fault‐tolerate containment control problem is considered for stochastic nonlinear multiagent systems in the presence of input saturation and sensor faults 27 …”

mentioning

confidence: 99%

“…22 A singularity-free online neural network-based sliding mode control method is proposed to realize the fixed-wing perch maneuver. 23 The fifth group of papers [24][25][26][27] discusses data-based control for distributed control systems. A mission-driven control scheme, including a consensus-based near-optimal formation controller and a finite-time precise formation controller, is proposed aiming at different requirements of unmanned aerial vehicle swarm.…”

mentioning

confidence: 99%

“…26 The fault-tolerate containment control problem is considered for stochastic nonlinear multiagent systems in the presence of input saturation and sensor faults. 27 The sixth group of papers [28][29][30] considers applications of data-based learning methods to industrial processes. A stochastic gradient algorithm based on the minimum Shannon entropy is proposed to identify a type of Hammerstein system with random noise.…”

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confidence: 99%

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Data‐based learning control for optimization of nonlinear systems

Wei

Song

Zhang

et al. 2023

Optim Control Appl Methods

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With the development of science and technology, practical systems such as the power systems, traffic systems, robot manipulator systems, etc., have become more complex. Therefore, it is difficult to build practical systems by accurate models. Under the lack of accurate process models, using system data to improve system performance and learn optimal decisions becomes very important. Through the recent years, data-based learning control theories and technologies have widely been investigated, including adaptive dynamic programming, reinforcement learning, iterative learning control, and so on. Data-based methods require the system data instead of the accurate knowledge of system dynamics that can be considered as model-free learning control methods. The data-based methods are effective solutions for the optimal control of nonlinear systems, which motivate this special issue.This special issue aims to collect and present original research dealing with data-based learning and their applications for optimization and control problems. The first group of papers [1][2][3][4][5][6][7] focuses on data-based control theory, approaches, and applications. A fuzzy model predictive control approach is proposed for stick-slip type piezoelectric actuator to realize the precise control of the end effector. 1 A systematic online adaptive dynamic programming control framework is proposed for smart buildings control to ensure hard constraints to be satisfied. 2 A multi-verse optimizer tuned PI-type active disturbance rejection generalized predictive control method is described for the motion control problems of ships. 3 The sufficient optimality conditions for the optimal controls are established under some convexity assumptions. 4 A receding-horizon reinforcement learning algorithm is proposed for near-optimal control of continuous-time systems under control constraints. 5 In order to solve the interference compensation control problem of a class of nonlinear systems, a method based on memory data is introduced to suppress interference greatly. 6 A new controller design method is proposed for the trajectory tracking problem of robots with imprecise dynamic properties and interference. 7 The second group of papers 8-12 considers iterative learning identification and iterative learning control. An iterative learning control approach is proposed for linear parabolic distributed parameter systems with multiple actuators and multiple sensors. 8 The quantized data-based iterative learning tracking control problem is studied for nonlinear networked control systems with signals quantization and denial-of-service attacks. 9 The output tracking problem is considered for a class of nonlinear parabolic distributed parameter systems with moving boundaries. 10 A just-in-time learning based dual heuristic programming algorithm is proposed to optimize the control performance of autonomous wheeled mobile robots under faults or disturbances. 11 A novel optimal constraint-following controller is proposed for uncertain mechanical systems. 12 The third...

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Adaptive containment fault‐tolerant control for saturated nonlinear multiagent systems with multiple faults and periodic disturbances

Tang,

Wang,

Niu

et al. 2024

Asian Journal of Control

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This paper addresses the problem of adaptive containment fault‐tolerant control for nonlinear multiagent systems with periodic disturbances. Different from most existing fault‐tolerant control schemes, the form of multiple faults is explicitly considered in this paper, including actuator faults and sensor faults. By combining the Fourier series expansion with neural networks, the unknown nonlinear dynamics subject to time‐dependent periodic disturbances are approximated. Then, the “complexity of explosion” issue that exists in traditional backstepping‐based results is avoided by introducing a first‐order sliding‐mode differentiator. It is proved that the developed containment control policies can ensure that all signals of the close‐loop systems are uniformly ultimately bounded, and all followers can converge to a convex area formed by multiple leaders. Simulation results verify the validity of the proposed scheme.

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Adaptive fault‐tolerant containment control for stochastic nonlinear multi‐agent systems with input saturation

Cited by 7 publications

References 49 publications

Fast finite‐time adaptive backstepping containment control for high‐order stochastic nonlinear multi‐agent systems

Fast finite‐time adaptive backstepping containment control for high‐order stochastic nonlinear multi‐agent systems

Data‐based learning control for optimization of nonlinear systems

Adaptive containment fault‐tolerant control for saturated nonlinear multiagent systems with multiple faults and periodic disturbances

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