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Distributed Model Predictive Control for Linear-Quadratic Performance and Consensus State Optimization of Multiagent Systems
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DOI：10.1109/TCYB.2020.3001347.png)
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Abstract
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The optimal consensus problem of asynchronous sampling single-integrator and double-integrator multiagent systems is solved by distributed model predictive control (MPC) algorithms proposed in this article. In each predictive horizon, the finite-time linear-quadratic performance is minimized distributively by the control input with consensus state optimization. The MPC technique is then utilized to extend the optimal control sequence to the case of an infinite horizon. Conditions depending only on each agent's weighting scalar and sampling step are derived to guarantee the stability of the closed-loop system. Numerical examples of rendezvous control of multirobot systems illustrate the efficiency of the proposed algorithm.
Keywords:
Multi-agent systems
Optimization
Prediction algorithms
Predictive control
Performance analysis
Transient response
Transient analysis
Asynchronous sampling
distributed optimization
model predictive control (MPC)
multiagent systems
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