Distributed model predictive control for consensus of sampled‐data multi‐agent systems with double‐integrator dynamics. Issue 12 (1st August 2015)

• Record Type: Journal Article
• Authors: Zhou, Lifeng; Li, Shaoyuan
• Is Part Of: IET control theory & applications. Volume 9:Issue 12(2015)
• Page Start: 1774
• Page End: 1780
• Publication Date: 2015-08-01
• Subjects: distributed control -- predictive control -- sampled data systems -- multi‐robot systems -- iterative methods -- optimisation -- mobile robots -- control system synthesis -- autonomous aerial vehicles; distributed model predictive control -- sampled‐data multiagent system consensus -- double‐integrator dynamics -- state exchange -- reverse iterative algorithm -- receding horizon optimisation -- sampled‐data double‐integrator dynamics -- distributed MPC consensus strategy -- sampling period; Control theory -- Periodicals; Automatic control -- Periodicals; 629.8312
• DOI: 10.1049/iet-cta.2014.1357
• Languages: English
• ISSNs: 1751-8644

Abstract

Abstract : This study proposes a distributed model predictive control (MPC) strategy to achieve consensus of sampled‐data multi‐agent systems with double‐integrator dynamics. On the basis of the error of state between each agent and the centre of its subsystem, a novel distributed MPC strategy (Algorithm 1) is obtained with the exchange of current states only. Then, a reverse iterative algorithm (Algorithm 2) is specially designed for the receding horizon optimisation of sampled‐data double‐integrator dynamics. Illustrative examples are finally displayed to verify the effectiveness and advantage of the distributed MPC consensus strategy and the impact of sampling period on consensus.