Summary
This paper focuses on the problem of adaptive containment control about privacy preservation in nonlinear multi‐agent systems, where followers suffer from multiple cyber‐attacks and leaders have non‐zero control inputs. A multiple attacks mold consisting of denial‐of‐service (DoS) attacks and false data‐injection attacks is constructed for nonlinear systems. The containment control protocol is updated by applying an event‐triggered scheme. This scheme is proposed to dispatch the updating information in the presence of multiple attacks. Meanwhile, to deal with the information insecurity that may exist in the control process, the protocol for privacy preservation about initial information is developed by introducing time‐varying functions as masks. Tracking errors of agents are described by taking the inputs of leaders as the external disturbances. This method does not depend on the widths of boundary layers and reduce the computational complexity. The stability of multi‐agent systems is analyzed by applying the Cauchy inequality, the Young's inequality and the Lyapunov function theory. Finally, two simulation examples are shown to illustrate the effectiveness of the proposed control strategy.
Summary
This paper proposes a self‐triggered (ST) adaptive prescribed‐time tracking control method for a class of stochastic nonlinear systems. Different from the existing results, an improved ST mechanism is proposed by adding a judgment condition to reduce the negative effect of excessive design interval on system performance. Based on the one‐to‐one mapping and backstepping technique, an adaptive prescribed‐time tracking control method is proposed, which can make the error converge to the predefined precision set within the predetermined time. Simultaneously, applying the Lyapunov stability method, the boundedness of all signals in the closed‐loop system can be ensured. Finally, a detailed simulation example is provided to show the effectiveness of the proposed control strategy.
This article investigates a reinforcement learning‐based optimal backstepping control strategy for strict‐feedback nonlinear systems, which contain output constraints, external disturbances and uncertain unknown dynamics. The simplified reinforcement learning algorithm with the identifier‐critic‐actor architecture is constructed in the control design to build optimal virtual and actual controllers. To compensate for the disturbance, a lemma is adopted to transform external disturbances into an unknown “bounding functions‘’, which satisfy a triangular condition. Moreover, the unknown nonlinear functions, which composed of unknown dynamics and external disturbances, approximated by neural networks. Meanwhile, in order to avoid violating output constraints, a barrier‐type Lyapunov function approach is integrated into the optimal control strategy to satisfy output constraints requirements under the framework of backstepping technique. Furthermore, the presented optimal control strategy guarantees that all signals in the closed‐loop system are semi‐globally uniformly ultimately bounded. Finally, the effectiveness of the proposed optimal control approach is performed by a numerical example.
In this paper, the adaptive tracking control problem is investigated for the multiagent systems with event‐triggered (ET) communication and asymmetric input saturation. By adopting an auxiliary system, the problem of asymmetric input saturation is successfully handled. Two ET mechanisms are employed in the controller‐to‐actuator channel and communication channel respectively to economize the limited communication resources. The update frequency of the controller can be reduced by devising a novel switching ET mechanism, which can unify the three existing ET schemes. Based on a backstepping technique, a distributed ET controller is devised, which only requires the sampled value of neighboring states. Due to the discontinuity of the ET state signals, the repetitive differentiation of virtual control laws will not be computed. To solve this problem, the predesigned differentiable partial derivatives of virtual control laws are used to construct the ET virtual control laws. By applying the Lyapunov stability method, it is proved that the desired tracking performance and the stability of the closed‐loop system can be guaranteed. Finally, a simulation example demonstrates that the proposed control strategy is effective.
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