Adaptive neural control for multiagent systems with asymmetric time‐varying state constraints and input saturation

Yang, Bin; Xiao, Wenbin; Yin, Hao; Zhou, Qi; Lu, Renquan

doi:10.1002/rnc.5004

Cited by 22 publications

(16 citation statements)

References 70 publications

(94 reference statements)

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“…Thus, the event-triggered controller can save communication resources effectively. Remark 4.1 Although two different of TVSCs were researched nonlinear MASs in and [34], both of their initial states must be available to satisfying the constraints. In this paper, it is worth noting in particular that the initial values of x 1,1 and x 2,1 are out of the constraints in Figure 4.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In [32][33][34], the γ-type BLF and state transformation technique using mapping functions have been proposed for MASs to handle the state constraints problem. But all of these methods have the limitations that the initial tracking condition is required to be known and satisfied constraints, and the state constraints must be imposed from the beginning of system operation, which reduce the applicability.…”

Section: Remark 32mentioning

confidence: 99%

“…A state transformation technique based on mapping functions was invoked to address the problem of time-varying state constraints in MASs consensus control in [33]. Both input saturation and asymmetric timevarying state constraints were taken into account in [34], where the full state constraints were handled by using asymmetric BLF. Whereas, the results mentioned above are confined to the fact that the constraint conditions must be imposed from the beginning of system operation, and the initial states are required to be available.…”

Section: Introductionmentioning

confidence: 99%

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Event-Triggered Adaptive Neural Control for Multiagent Systems with Deferred State Constraints

et al. 2021

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This paper focuses on the leader-following consensus control problem for nonlinear multiagent systems subject to deferred asymmetric time-varying state constraints. A distributed eventtriggered adaptive neural control approach is advanced. By virtue of a distributed sliding-mode estimator, the leader-following consensus control problem is converted into multiple simplified tracking control problems. Afterwards, a shifting function is utilized to transform the error variables such that the initial tracking condition can be totally unknown and the state constraints can be imposed at a specified time instant. Meanwhile, the deferred asymmetric time-varying full state constraints are addressed by a class of asymmetric barrier Lyapunov function. In order to reduce the burden of communication, a relative threshold event-triggered mechanism is incorporated into controller and Zeno behavior is excluded. Based on Lyapunov stability theorem, all closed-loop signals are proved to be semi-globally uniformly ultimately bounded. Finally, a practical simulation example is given to verify the presented control scheme.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Remark 32mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Event-Triggered Adaptive Neural Control for Multiagent Systems with Deferred State Constraints

et al. 2021

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“…Remark The advantages of using UBF to handle state constraints are summarized as: 1) Compared with References 19,27,28,32, the “feasibility conditions” are no longer needed in the proposed method. 2) Although Reference 29 proposed a control method without requiring “feasibility conditions”, the structure of its new system need the lower constraining functions be strictly negative and the upper constraining functions be strictly positive (

η_{i, 1} \neq 0

), which restricted the constraint functions greatly.…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

“…Furthermore, the BLF method can also be used to deal with the problem of asymmetric state or output constraints 27,28 . In detail, the time‐varying asymmetric barrier Lyapunov function (TABLF) was used in Reference 27 for multiagent systems and tan‐type TABLF was used in Reference 28 for a class of uncertain SISO nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive event‐triggered control for MIMO nonlinear systems with asymmetric state constraints based on unified barrier functions

Xia

Park

Shen

et al. 2021

Intl J Robust & Nonlinear

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The topic of adaptive event‐triggered control for multi‐input‐multi‐output nonlinear systems with asymmetric state constraints is considered in this article. First, by introducing unified barrier functions method, the initial system is transformed to a non‐constraints system, which brings that the requirement of feasibility conditions could be eliminated and the constraint functions could be relaxed effectively. Then, an adaptive tracking controller is designed by combining some excellent technology, where command filter is utilized to overcome the explosion of complexity, neural network is introduced to approximate unknown nonlinear function, and event‐triggered mechanism is proposed to save communication resources. The designed control scheme can make the outputs of the system to track the target trajectories within a small bounded error range, all signals in the closed‐loop system are bounded, and all states do not escape the state constraints. Finally, practical and comparative examples are given to verify the effectiveness of the method.

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Finite‐time formation control and obstacle avoidance of multi‐agent system with application

Shou

et al. 2021

Intl J Robust & Nonlinear

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The finite-time formation tracking control is investigated for a multi-agent system (MAS) with obstacle avoidance. For the collision and obstacle avoidance problem in the formation process, the artificial potential field is used as the formation planning design, and the virtual structure is adopted to improve the organizational ability of the formation. The trajectory tracking control follows the back-stepping scheme, and the finite-time technique is developed in the control design. Considering the dynamics uncertainty of the agent system, a neural network is applied for estimating and the prediction error-based adaptive law is established to achieve the precise estimation performance. Moreover, the predefined performance function is embedded to satisfy the output constraint. The uniformly ultimate boundedness of the system error signals and the finite-time convergence of the MAS are guaranteed. The simulation study is performed to validate the proposed control for multiple autonomous underwater vehicles system, while the results manifest that the obstacle avoidance with high-precision tracking and formation performance will be achieved under the formation trajectory tracking controller.

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Adaptive neural control for multiagent systems with asymmetric time‐varying state constraints and input saturation

Cited by 22 publications

References 70 publications

Event-Triggered Adaptive Neural Control for Multiagent Systems with Deferred State Constraints

Event-Triggered Adaptive Neural Control for Multiagent Systems with Deferred State Constraints

Adaptive event‐triggered control for MIMO nonlinear systems with asymmetric state constraints based on unified barrier functions

Finite‐time formation control and obstacle avoidance of multi‐agent system with application

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