摘要：AbstractModel Predictive Control (MPC) is a powerful and flexible design tool of high-performance controllers for physical systems in the presence of input and output constraints. A challenge for the practitioner applying MPC is the need of tuning a large number of parameters such as prediction and control horizons, weight matrices of the MPC cost function, and observer gains, according to different trade-offs. The MPC design task is even more involved when the control law has to be deployed to an embedded hardware unit endowed with limited computational resources. In this case, real-time implementation requirements limit the complexity of the applicable MPC configuration, giving rise to additional design tradeoffs and requiring to tune further parameters, such as the sampling time and the tolerances of the on-line numerical solver. To take into account closed-loop performance and real-time requirements, in this paper we tackle the embedded MPC design problem using a global, data-driven optimization approach. We showcase the potential of this approach by tuning an MPC controller on two hardware platforms characterized by largely different computational capabilities.
关键词：KeywordsModel Predictive ControlMachine learningGlobal optimizationSelf-tuning controlEmbedded systems
