Feedback scheduling of model predictive controllers

The paper presents some preliminary results on dynamic scheduling of model predictive controllers DMPCsE. In an MPC, the control signal is obtained by on-line optimization of a cost function, and the MPC task may experience very large variations in execu-tion time from sample to sample. Unique to this ap-plication, the cost function offers an explicit, on-line quality-of-service measure for the task. Based on this insight, a feedback scheduling strategy for multi-ple MPCs is proposed, where the scheduler allocates CPU time to the tasks according to the current val-ues of the cost functions. Since the MPC algorithm is iterative, the feedback scheduler may also abort a task prematurely to avoid excessive input-output latency. A case study is presented, where the new ap-proach is compared to conventional fixed-priority and earliest-deadline-first scheduling. General problems related to the real-time implementation of MPCs are also discussed. 1