Output Feedback Control and Stabilization for Networked Control Systems With Packet Losses

Abstract

This paper mainly considers the optimal measurement feedback control and stabilization for networked control systems (NCSs) with packet losses. The problems are involved with fundamental difficulties of separation principle and optimal estimation (conditional expectation) for multiplicative noise stochastic systems. First, the optimal estimator (conditional expectation) for NCSs with packet losses is derived and the optimal measurement feedback controller is obtained by using the maximum principle. The sufficient and necessary solvability condition of finite horizon measurement feedback control problem is first presented. Moreover, the asymptotic stability of the optimal estimator is studied. Finally, for the infinite horizon case, based on the introduction of a new Lyapunov function, which is defined with the optimal cost function, it is shown that the system is stabilizable in the mean square sense if and only if an algebraic Riccati equation admits a unique positive definite solution.

Bipartite Consensus of High‐Order Edge Dynamics on Coopetition Multiagent Systems

“…Therefore, all the eigenvalues of matrix have negative real parts under conditions (13), (14), and (15). Thus, system (5) is asymptotically stable under protocol (6). Accordingly, we have lim →∞ ( ) = 0, lim →∞ ( ) = 0, lim →∞ ( ) = 0.…”

“…For a continuous-time edge dynamic multiagent system (5), let the corresponding digraph G be strongly connected, digon sign-symmetric, and structurally balanced. When the assumption holds, system (5) can asymptotically reach the bipartite consensus of edge dynamics under protocol (6) for ̸ = 0, if the following inequalities are satisfied…”

“…Nodes in (a) can be divided into V 1 = {1, 2, 3}, V 2 = {4, 5, 6} and all the negative edges are between different sets. The nodes in (b) (i.e., the edges in (a)) can be divided into E 1 = {(1, 2), (2, 3), (4, 2), (5, 2), (6, 1)} and E 2 = {(2, 6), (3,4), (4,5), (5,6)}. Thus (a) is structurally balanced.…”

“…In recent years, researchers from different fields have done many works for the networked system thanks to the wide applications both to formation control, such as mobile robots, unmanned air vehicles (UAVs), and autonomous underwater vehicles (AUVs), and to distributed control, such as distributed forecasting, monitoring, and diagnostics [1][2][3][4][5][6]. For multiagent network systems, controllability and consensus problems are the focus of researchers [7][8][9][10][11][12][13][14][15].…”

Exponential Synchronization of Neural Networks via Feedback Control in Complex Environment

“…It is well known that all the state variables of many practical systems cannot be measured directly, which means that the state feedback controller can be designed only if a state observer can be designed firstly and this will not only increase the cost but also decrease the system reliability. Therefore, the output feedback controller is more preferable when designing a controller to ensure the desired performance for the closed-loop system [32][33][34]. Different from continuous feedback control, intermittent control, as a kind of discontinuous feedback control strategy, consists of two parts: the nonzero control time and nonzero rest time.…”