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This paper is concerned with the design of adaptive event-triggered scheme for networked nonlinear interconnected systems via T-S fuzzy models. The transmission of control signals is based on a novel adaptive event-triggered communication scheme, where the adaptive threshold is dependent on a dynamic error of the system rather than a predetermined constant as the one in the existing results. The amount of the releasing data is regulated with the adaptive threshold that plays a crucial role in decision of whether releasing the sampled data or not. This technique reflects an inherent dynamic balance between the control performance and the utilization of the network resource. A corresponding Lyapunov function is constructed to achieve sufficient conditions of stability and stabilization. Finally, a simulation example is given to show the effectiveness of the proposed theoretic results. [ABSTRACT FROM AUTHOR]

In this study, we propose a systematic homogenous polynomial approach for fuzzy control design of discrete-time Takagi-Sugeno fuzzy systems under unreliable communication links. A novel delayed fuzzy control scheme, which is homogenous polynomially parameter-dependent on both the current normalized fuzzy weighting function and the one-step-past normalized fuzzy weighting function, is designed to accomplish the assignment of relaxed control synthesis. In contrast to the existing methods, the closed-loop controlled system is stochastically stable in the mean square sense with less conservative stabilization conditions. Finally, simulation results are provided to verify the advantage and the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we propose an output-based tracking control scheme for a class of continuous-time nonlinear systems via the adaptive dynamic programming (ADP) technique. A neural networks (NNs) observer is constructed to reconstruct immeasurable information of the nonlinear systems, and, by introducing a new state vector and appropriate coordinate transformation, tracking control issues are converted into optimal regulation problems where critic-actor neural networks structures are developed for the solution of Hamilton–Jacobi–Bellman (HJB) equation corresponding to tracking errors. In addition, a robust term is introduced to eliminate effects from approximation errors. It is proven that all signals in the closed-loop system are uniformly ultimately bounded (UUB) by the Lyapunov approach. Finally, simulation examples are provided for illustration of the theoretical claims. [ABSTRACT FROM AUTHOR]

This paper addresses a secure bipartite tracking control problem for a class of nonlinear multi-agents (MASs) with nonsymmetric input constraints. In the presence of adversarial sensor attacks, a secure measurement preselector, along with an explicit sufficient condition, and a neural network (NN) secure state observer are introduced for achieving secure state estimation. Then, a secure bipartite tracking control strategy is proposed, where observation predictors are designed to reconstruct prediction errors in such a way as to improve control performance. Furthermore, an auxiliary system is presented to eliminate influence from nonsymmetric input saturations. It is theoretically proved that the proposed control strategy not only guarantees bipartite tracking of the MAS but also preserves the stability of the resulting closed-loop system in spite of senor attacks. Finally, two illustrative examples are presented to verify the effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]