Impulsive Adaptive Intelligent Observer-Based Control for Linear Network Controlled Systems

This paper considers the exponential stabilization problem of the linear network controlled systems, and provides a new impulsive observer-based intelligent feedback control design strategy. Firstly, a new impulsive observer is presented based on discrete-time adaptive technique (called impulsive adaptive observer, IAO), which can ensure the presented IAO successfully tracks the states of the continuous-time system (target system) only by using the output information sampled at discrete-time sequences (or called impulsive instants). After that the IAO-based feedback control protocol is provided to stabilize a class of linear network controlled systemss, and the criteria with exponential convergence rate are established for the presented IAO and the plant under the IAO-based feedback control rule. Owing to the intermittently update mechanism of the adaptive protocol, the IAO and the corresponding IAO-based feedback controller exhibit better performance than some already-existing continuous-time-based adaptive schemes in the side of reducing the computation load and saving the system resource. Finally, a muscle system in biomedicine and its simulation are provided to illustrate the validity of the derived theoretic results.