Model-Free Predictive Control of Nonlinear Processes Based on Reinforcement Learning

Abstract

Abstract Model predictive control (MPC) is a model-based control philosophy in which the current control action is obtained by on-line optimization of objective function. MPC is, by now, considered to be a mature technology owing to the plethora of research and industrial process control applications. The model under consideration is either linear or piece-wise linear. However, turning to the nonlinear processes, the difficulties are in obtaining a good nonlinear model, and the excessive computational burden associated with the control optimization. Proposed framework, named as model-free predictive control (MFPC), takes care of both the issues of conventional MPC. Model-free reinforcement learning formulates predictive control problem with a control horizon of only length one, but takes a decision based on infinite horizon information. In order to facilitate generalization in continuous state and action spaces, fuzzy inference system is used as a function approximator in conjunction with Q-learning. Empirical study on a continuous stirred tank reactor shows that the MFPC reinforcement learning framework is efficient, and strongly robust.