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Self-triggered distributed model predictive control for flocking of multi-agent systems

Self-triggered distributed model predictive control for flocking of multi-agent systems

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This study presents a self-triggered distributed model predictive control algorithm for the flock of a multi-agent system. All the agents in a flock are endowed with the capability of determining the sampling time adaptively to reduce the unnecessary energy consumption in communication and control updates. The agents are dynamically decoupled in a flock, and each agent is driven by a local model predictive controller, which is designed by minimising the position irregularity between the agent and its neighbours, velocity tracking errors as well as its control efforts. Moreover, the collision avoidance is considered by introducing constraints in the model predictive minimisation problem. In order to adaptively determine the sampling time, a self-triggered algorithm is designed by guaranteeing the decrease of the Lyapunov function. Finally, numerical simulations are given to demonstrate the feasibility of the proposed flocking algorithm.

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