Model-free Predictive Control

Model predictive control, MPC, is a model-based control philosophy that select control actions by on-line optimization of objective functions. Design methods based on MPC have found wide acceptance in industrial process control applications, and have been thoroughly studied by the academia. Most of the work so far have relied on linear models of different sophistication because of their advantage of providing simple and straightforward implementations. However, when turning to the nonlinear domain, problems often arise as a consequence of the difficulties in obtaining good nonlinear models, and the computational burden associated with the control optimization. In this paper we present a new approach to the nonlinear MPC problem using the recently proposed concept of model-ondemand. The idea is to estimate the process dynamics locally and on-line using process data stored in a database. By treating the local model obtained at each sample time as a local linearization, it is thus possible to reuse tools and concepts from the linear MPC framework. Three different variants of the idea, based on local linearization, linearization along a trajectory and nonlinear optimization respectively, are studied. They are all illustrated in numerical simulations.