Distributed output feedback MPC with randomly occurring actuator saturation and packet loss

Abstract

In this paper, a distributed output feedback model predictive control (OFMPC) algorithm is presented for the polytopic uncertain system subject to randomly occurring actuator saturation and packet loss. Compared with the intensively applied state feedback control in MPC, the OFMPC is more feasible to the real world because the system states are often unmeasurable. With taking both actuator saturation and packet loss into account, the presented OFMPC algorithm is more practical. Moreover, by splitting the controller inputs into two independent parts, the presented dynamic output feedback control (DOFC) strategy provides more freedom to the controller design. With the global system decomposed into some subsystems, the computation complexity is reduced, thus the online designing time can be saved. By defining the estimation error function and forming an augmented system to handle the DOFC and by transforming the nonlinear feedback law into a convex hull of linear feedback laws, the distributed controllers are obtained by solving a linear matrix inequality (LMI) optimization problem. Finally, some simulation examples are employed to show the effectiveness of the techniques proposed in this paper. 3037Note that the results by OFMPC strategy provided earlier are mostly on centralized MPC. However, with the rapid development of the science technology and the network application, the systems to be handled are becoming more and more complex. It is often infeasible to apply the classical centralized MPC control solution to the practical systems accounting for the computational complexity and communication bandwidth limitations. Thus, a distributed MPC strategy has attracted much attention to reduce the computation time and the network transmission burden recently [11,12]. In a distributed MPC framework, each subsystem is controlled by an independent controller, all subsystems communicate with each other via network, and the technical target is to achieve the global performance of entire system. In this way, the system with distributed MPC has better error-tolerance and robustness compared with the traditional centralized MPC. Mercangöz et al. proposed an iterative implementation of a distributed MPC scheme and applied it to a four-tank system [13]. In [14], a class of linear systems coupled with neighbors through inputs was discussed and the corresponding distributed MPC algorithms were presented for the cooperative subsystems. A robust distributed MPC problem was discussed for a system with polytopic uncertainty in [15] and [16], where the model uncertain problems were casted into an LMI optimization problem.In [17], Zhang et al. presented a distributed MPC algorithm for the polytopic system subject to inputs saturation.On the other hand, the obstacles in delivering are often owing to the physical limitations of system components, of which the most commonly encountered one stems from the saturation that occurs in actuators. Saturation brings in nonlinear characteristics that can severely restrict the amount of transmitted...

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