JOURNAL ARTICLE

Event-triggered robust model predictive control for Lipschitz nonlinear networked control systems subject to communication delays

Saeid GhorbaniAli Akbar SafaviS. Vahid Naghavi

Year: 2020 Journal:   Transactions of the Institute of Measurement and Control Vol: 43 (5)Pages: 1126-1142   Publisher: SAGE Publishing

Abstract

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.

Keywords:
Model predictive control Control theory (sociology) Convex optimization Nonlinear system Controller (irrigation) Computer science Networked control system Optimization problem Event (particle physics) Scheme (mathematics) Linear matrix inequality Lipschitz continuity Control (management) Regular polygon Mathematics Mathematical optimization Algorithm Artificial intelligence

Metrics

11
Cited By
0.73
FWCI (Field Weighted Citation Impact)
37
Refs
0.72
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Control Systems Optimization
Physical Sciences →  Engineering →  Control and Systems Engineering
Stability and Control of Uncertain Systems
Physical Sciences →  Engineering →  Control and Systems Engineering
Fault Detection and Control Systems
Physical Sciences →  Engineering →  Control and Systems Engineering

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