In this paper, the problem of event-triggered robust model predictive control (MPC) was examined for a class of Lipchitz nonlinear networked control systems (NCS) with network-induced delays and subject to external disturbances. An event-triggering scheme for a continuous-time NCS was proposed, which reduced the communication traffic and computational burden of the MPC algorithm simultaneously. In comparison with the existing event-triggered nonlinear MPC (NMPC) approaches, the controller in this paper was designed as a state feedback control law, which minimized a “worst-case” performance index over an infinite horizon subject to constraints on the control input. The controller and event generator parameters were developed as a convex optimization problem, encompassing some linear matrix inequalities (LMIs). Simulation results showed that the proposed event-triggering NMPC scheme preserved closed-loop performance while reducing the communication rate and the computational time.
This article examines a decentralized event-triggered robust model predictive control (MPC) for a class of networked large-scale non-linear Lipchitz systems. It is assumed that the subsystems are geographically distributed and the connections can be made over a communication network and therefore local event generator modules are used. An event-triggering condition is then proposed for each module, which only uses local information to trigger data via the communication channel. In this way, the information exchange between subsystems can be reduced significantly compared to time-triggered conventional control approaches, while the asymptotic stability of the closed-loop is maintained. In contrast to the reported event-triggered MPC results, the optimized controller is calculated based on state feedback control law for individual subsystems, which minimizes the upper limit on the infinite horizon cost function subject to constraints on the control inputs. The validness of the proposed scheme is demonstrated by simulation results.
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