Distributed Model Predictive Control for Linear–Quadratic Performance and Consensus State Optimization of Multiagent Systems

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Abstract
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.
Funding Information
  • National Key Research and Development Program of China (2017YFB13010000, 2018AAA0102703)
  • National Natural Science Foundation of China (U1713223, 11932003, 61673026, 61903083)
  • Hong Kong Research Grants Council through GRF (CityU 11200317)
  • China Postdoctoral Science Foundation (BX20190025, 2019M660405)
  • Natural Science Foundation of Jiangsu Province of China (BK20190333)
  • Fundamental Research Funds for the Central Universities (2242020K40236)
  • Southeast University “Zhishan Youth Scholars” Project

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