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Distributed model predictive control for asynchronous multiagent systems with self-triggered coordinator
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DOI：10.1002/asjc.3532.png)
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Abstract
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This paper investigates a distributed model predictive control strategy for asynchronous nonlinear multiagent systems (MASs) with external disturbances via self-triggered generators and prediction horizon regulators. First, a shrinking constraint related to the error between the actual state and the predicted state is introduced into the optimal control problem to enhance the robustness of the system. Then, the triggering interval and the corresponding prediction horizon are determined by altering the expression of the Lyapunov function, thus achieving a trade-off between control performance and energy loss. By implementing the proposed algorithm, the coordination objective of the MASs is achieved under asynchronous communication. Finally, the recursive feasibility and closed-loop stability are proven successively. An illustrative example is conducted to demonstrate the merits of the presented approach.
Keywords:
asynchronous multiagent systems
distributed model predictive control
nonlinear predictive control
self-triggered control
Journal
IF:
2.7
Papers:
553
Citations:
4.7K