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In this article, a command filtering‐based adaptive event‐triggered neural network control scheme is proposed for a class of uncertain switched nonlinear systems with unknown control coefficient and input saturation. First, radial basis function neural networks are used as function approximators to estimate unknown nonlinear functions. Then, an event‐triggering mechanism based on the tracking error is introduced to avoid the over‐consumption of communication resources. Furthermore, command filters are employed to solve the problem of complexity explosion that exists in conventional backstepping control design, and the error compensation signals are designed to reduce the errors caused by the filters. Considering that the unknown control gain and input saturation exist in many practical applications, a Nussbaum‐type function is thus introduced into the controller design to address these challenging issues. Finally, stability of the closed‐loop system is strictly proven under a standard Lyapunov stability analysis framework. The effectiveness of the proposed control scheme is illustrated by a simulation example. [ABSTRACT FROM AUTHOR]

In this paper, an output‐based event‐triggered control problem of discrete‐time networked control systems (NCSs) subject to bilateral packet dropouts is investigated. In view of stochastic sequences of packet dropouts in the measurement channels (from sensors to controller) and the control channels (from controller to actuators), the NCS is converted into a closed‐loop stochastic parameter system. In the aid of a Lyapunov functional based on stochastic variables, the sufficient conditions on design of a dynamic output feedback control law and bilateral event‐triggering conditions are derived to guarantee the exponentially mean‐square stability for NCSs. Furthermore, an improved iterative algorithm is given to obtain the output‐based control law and event‐triggering parameters from a set of nonconvex inequalities. Finally, a numerical example and the corresponding simulation results are given to illustrate the validity and applicability of the developed techniques. [ABSTRACT FROM AUTHOR]

In this paper, the optimal output tracking problem for a class of switched Boolean networks is investigated by using a graph‐theoretical method. Firstly, by using the semitensor product of matrices, the algebraic equation of switched Boolean networks is formulated, and then by utilizing the concepts of set stabilization and switching invariant set, the trackability of a switched Boolean network is judged. Furthermore, the optimal switching sequence for optimal output tracking of switched Boolean networks is designed by using the heap‐optimized Dijkstra algorithm and the constructed optimal state transition graph, which enables the system to track a given reference signal with the minimum cost. Finally, a numerical example is given to demonstrate the validity of the presented results. [ABSTRACT FROM AUTHOR]