A fast dual gradient algorithm for distributed model predictive control with coupled constraints
(2017) 2017 IEEE 56th Annual Conference on Decision and Control (CDC) — Location: Melbourne, Australia (12.December.2017)
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- Abstract
- This paper proposes a DMPC approach for a family of discrete-time linear systems with local (uncoupled) and global (coupled) constraints. The proposed approach is based on the dual problem of an overall MPC optimization problem involving all systems which is then solved distributively using a modified distributed Nesterov-accelerated-gradient algorithm. To further reduce the computational requirement, this approach allows for early termination of the distributed gradient algorithm. This is made possible via a consensus algorithm that determines the satisfaction of the termination condition and by appropriate tightening of the coupled constraints. Under reasonable assumptions, the approach is able to produce a suboptimal solution so long as the network of the systems is connected while ensuring recursive feasibility and exponential stability of the closed-loop system. The performance of the proposed approach is demonstrated by a numerical example.
- Affiliations
National University of Singapore
Citations
Wang, Z., & Ong, C.-J. (2017). A fast dual gradient algorithm for distributed model predictive control with coupled constraints. 2017 IEEE 56th Annual Conference on Decision and Control (CDC). Published. 2017 IEEE 56th Annual Conference on Decision and Control (CDC), Melbourne, Australia. https://doi.org/10.1109/cdc.2017.8264592