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