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Article
Computer Science, Artificial Intelligence
Fatemeh Sedghi et al.
Summary: This article addresses and studies the problem of distributed finite-time consensus control for a class of stochastic nonlinear multiagent systems in the presence of various factors. Innovative control inputs are designed and proposed, and the stability and consensus of the system are proven.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Guozeng Cui et al.
Summary: This article investigates the problem of finite-time adaptive fuzzy tracking control for multi-input and multi-output (MIMO) nonlinear systems with input saturation. A new finite-time command filter and modified error compensation mechanism are introduced to address complexity explosion and filter error effects. The proposed finite-time adaptive control scheme guarantees finite-time bounded signals and regulation of output tracking errors to a small neighborhood of the origin. Numerical comparison example verifies the effectiveness of the proposed control scheme.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Guozeng Cui et al.
Summary: This article discusses the problem of finite-time adaptive fuzzy prescribed performance control via output-feedback for nonstrict-feedback nonlinear systems. By designing a fuzzy state observer and a novel finite-time command filter, the authors propose a control strategy that can converge the output tracking error to a residual set within prescribed performance bound in finite time.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Automation & Control Systems
Hongjing Liang et al.
Summary: This article proposes a solution to the cooperative fault-tolerant control problem for networks of stochastic nonlinear systems with actuator faults and input saturation. Fuzzy neural networks are used to estimate unknown functions and stochastic disturbance terms, while a smooth nonlinear function is constructed to estimate the saturation function. A novel adaptive fault-tolerant control protocol is proposed using backstepping design technique, and the stochastic Lyapunov functional strategy is used to prove convergence and boundedness of the closed-loop systems.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Kewen Li et al.
Summary: This article investigates the problem of adaptive fuzzy optimal distributed consensus control for stochastic multiagent systems (MASs) with full-state constraints and nonaffine nonlinear faults. Fuzzy logic systems are employed to identify the unknown nonlinearities. To solve the problem of optimal state constraint control, a barrier Lyapunov function based optimal cost function is designed. By introducing Butterworth low-pass filter into control design, the deleterious effects raised by nonlinear fault can be compensated. By utilizing adaptive dynamic programming algorithm in critic-actor construction, a fuzzy adaptive distributed optimal consensus fault-tolerant control method is proposed, which can ensure that all signals of the controlled system are semiglobally uniformly ultimately bounded in probability, and outputs of the follower agents keep consensus with the output of leader. In addition, system states are all not exceeded their constrained bound. Finally, simulation results are provided to illustrate the feasibility of the developed control method and theorem.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Information Systems
Yong Zhao et al.
Summary: This paper addresses the consensus tracking problem of stochastic multi-agent systems with output, partial state constraints, and input saturation. An event-triggered strategy and backstepping techniques are used to design a distributed controller that guarantees boundedness of system signals, accurate consensus tracking with small error, and avoids Zeno behavior.
INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Ying Wu et al.
Summary: This article investigates the distributed containment control problem for high-order stochastic nonstrict-feedback nonlinear multi-agent systems. An auxiliary virtual controller is designed to overcome the difficulty of unknown control input gain, and the unknown terms are estimated by introducing radial basis function neural networks. The proposed control method guarantees semi-globally uniformly ultimately bounded signals and achieves containment control performance.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Engineering, Mechanical
Bing Wu et al.
Summary: An adaptive neural control strategy is proposed for uncertain 2DOF helicopter systems with input saturation and time-varying output constraints. A radial basis function neural network is utilized to estimate the uncertainty terms in the system, while an adaptive auxiliary parameter is introduced to compensate for the composite disturbance caused by saturation error and external disturbance. An asymmetric barrier Lyapunov function is employed to address the constraint violation of system output. The closed-loop stability of the system is verified through Lyapunov theory analysis, and simulation results demonstrate the effectiveness of the proposed control strategy.
Article
Automation & Control Systems
Yongming Li et al.
Summary: This article studies the adaptive optimized formation control problem for second-order stochastic multiagent systems with unknown nonlinear dynamics. Fuzzy logic systems are used to approximate the nonlinear functions in the control design. By employing the actor-critic architecture and Lyapunov stability theory, the proposed optimal formation control strategy ensures that all the error signals are bounded in probability. Simulation examples verify the effectiveness of the proposed formation control approach.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Automation & Control Systems
Xinyu Song et al.
Summary: This paper considers finite-time leader-following consensus control for high-order stochastic nonlinear multi-agent systems with input constraints. It designs a finite-time consensus tracking controller based on adaptive fuzzy command filtered backstepping, uses a fractional power form-based error compensation method, and employs a fuzzy logic system to approximate unknown nonlinear functions. The practical finite-time stability in mean square is assured under the given control method, and simulation results show the proposed controller's effectiveness and feasibility.
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2022)
Article
Automation & Control Systems
Zhengqing Shi et al.
Summary: This article investigates the consensus tracking problem for a class of nonstrict-feedback nonlinear multi-agent systems under a directed graph. A novel observer-based adaptive control protocol is proposed to estimate unmeasurable states and compensate command filter errors. The protocol guarantees semi-globally uniformly ultimately bounded signals and satisfies prescribed performance requirements. Numerical examples are presented to validate the effectiveness of the proposed protocol.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Computer Science, Artificial Intelligence
Bocheng Yan et al.
Summary: In this article, the asymptotic tracking control problem for a class of nonlinear multi-agent systems is studied using radial basis function neural networks and an improved dynamic surface control technology. The proposed control strategy ensures that all the closed-loop system variables are ultimately bounded and the followers can track the leader's output with zero tracking errors.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Information Systems
Guozeng Cui et al.
Summary: This paper investigates finite-time adaptive fault-tolerant tracking control for nonstrict-feedback nonlinear systems by introducing a novel finite-time command filter and a fractional order error compensation mechanism. A control strategy combining neural networks and command filtered backstepping approach is established to ensure output tracking error convergence within finite-time and bounded closed-loop system signals. Simulation examples are provided to verify the effectiveness of the proposed method.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Huaguang Zhang et al.
Summary: This article introduces an adaptive fuzzy finite-time control approach for uncertain strict-feedback nonlinear systems with backlashlike hysteresis and stochastic disturbances. The proposed control scheme effectively overcomes complexity and singularity issues, providing good tracking performance for systems with such characteristics. The effectiveness of the strategy is further validated through simulation examples.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Automation & Control Systems
Ying Wu et al.
Summary: This article proposes a quantized adaptive finite-time control method for high-order stochastic pure-feedback nonlinear multiagent systems, addressing unknown parameters and hysteresis effects using distributed control and adaptive neural-network compensation approaches. By utilizing radial basis function neural networks and dynamic surface control technique, the system stability and control performance are achieved, as demonstrated by simulation results.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Automation & Control Systems
Liang Cao et al.
Summary: An adaptive neural network event-triggered control scheme is proposed for nonlinear nonstrict-feedback multiagent systems to address input saturation, unknown disturbance, and sensor faults. By utilizing the mean-value theorem and Nussbaum-type function, the structure of input saturation is transformed and the difficulty of unknown control directions is overcome. The effectiveness of the control strategy is demonstrated through simulation results.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Yang Yang et al.
Article
Computer Science, Information Systems
Zhenhua Qin et al.
INFORMATION SCIENCES
(2019)
Article
Automation & Control Systems
Xiu You et al.
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Computer Science, Information Systems
Wencheng Zou et al.
IEEE SYSTEMS JOURNAL
(2019)
Article
Automation & Control Systems
Lin Zhao et al.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2018)
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Chang-E Ren et al.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2017)
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Jiahu Qin et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2017)
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Mehdi Hosseinzadeh et al.
IET CONTROL THEORY AND APPLICATIONS
(2017)
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IEEE TRANSACTIONS ON CYBERNETICS
(2017)
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Guozeng Cui et al.
IET CONTROL THEORY AND APPLICATIONS
(2016)
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Milad Shahvali et al.
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Changyun Wen et al.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2011)
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Jay A. Farrell et al.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2009)
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HB Ji et al.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2006)
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JA Fax et al.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2004)
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CJ Qian et al.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2001)
