Distributed model predictive control of constrained weakly coupled nonlinear systems |
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Affiliation: | 1. Department of Automatic Control & Systems Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, UK;2. Department of Systems and Automation Engineering, University of Seville, Seville, Spain;1. School of Applied Physics and Physico Informatics, Keio University, Yokohama, Japan;2. School of Electrical Engineering, KTH Royal Institute of Technology, 10044 Stockholm, Sweden |
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Abstract: | This paper proposes a distributed model predictive control (MPC) strategy for a large-scale system that consists of several dynamically coupled nonlinear systems with decoupled control constraints and disturbances. In the proposed strategy, all subsystems compute their control signals by solving local optimizations constrained by their nominal decoupled dynamics. The dynamic couplings and the disturbances are accommodated through new robustness constraints in the local optimizations. The paper derives relationships among, and designs procedures for, the parameters involved in the proposed distributed MPC strategy based on the analysis of the recursive feasibility and the robust stability of the overall system. The paper shows that, for a given bound on the disturbances, the recursive feasibility is guaranteed if the sampling interval is properly chosen. Moreover, it establishes sufficient conditions for the overall system state to converge to a robust positively invariant set. The paper illustrates the effectiveness of the proposed distributed MPC strategy by applying it to three coupled cart-(nonlinear) spring–damper subsystems. |
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Keywords: | Distributed model predictive control (MPC) Coupled nonlinear system Optimization Recursive feasibility |
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