Explicit Distributed MPC: Reducing Computation and Communication Load by Exploiting Facet Properties
This work addresses efficiency issues in real-time control applications with tight latency and computation constraints, representing an incremental improvement over existing iteration-free DiMPC methods.
The paper tackles the high computational and communication burdens in Distributed Model Predictive Control (DiMPC) by proposing FACET-DiMPC, an iteration-free method that reduces average computation time by 98% compared to classic iterative DiMPC and 42% compared to iteration-free DiMPC, while maintaining comparable control performance to centralized methods.
Classical Distributed Model Predictive Control (DiMPC) requires multiple iterations to achieve convergence, leading to high computational and communication burdens. This work focuses on the improvement of an iteration-free distributed MPC methodology that minimizes computational effort and communication load. The aforementioned methodology leverages multiparametric programming to compute explicit control laws offline for each subsystem, enabling real-time control without iterative data exchanges between subsystems. Extending our previous work on iteration-free DiMPC, here we introduce a FAcet-based Critical region Exploration Technique for iteration-free DiMPC (FACET-DiMPC) that further reduces computational complexity by leveraging facet properties to do targeted critical region exploration. Simulation results demonstrate that the developed method achieves comparable control performance to centralized methods, while significantly reducing communication overhead and computation time. In particular, the proposed methodology offers substantial efficiency gains in terms of the average computation time reduction of 98% compared to classic iterative DiMPC methods and 42% compared to iteration-free DiMPC methods, making it well-suited for real-time control applications with tight latency and computation constraints.