Efficient Model Predictive Algorithms for Tracking of Periodic Signals

This paper studies the design of efficient model predictive controllers for fast-sampling linear time-invariant systems subject to input constraints to track a set of periodic references. The problem is decomposed into a steady-state subproblem that determines the optimal asymptotic operating point and a transient subproblem that drives the given plant to this operating point. While the transient subproblem is a small-sized quadratic program, the steady-state subproblem can easily involve hundreds of variables and constraints. The decomposition allows these two subproblems of very different computational complexities to be solved in parallel with different sampling rates. Moreover, a receding horizon approach is adopted for the steady-state subproblem to spread the optimization over time in an efficient manner, making its solution possible for fast-sampling systems. Besides the conventional formulation based on the control inputs as variables, a parameterization using a dynamic policy on the inputs is introduced, which further reduces the online computational requirements. Both proposed algorithms possess nice convergence properties, which are also verified with computer simulations.