Model-free learning control of neutralization processes using reinforcement learning

The pH process dynamic often exhibits severe nonlinear and time-varying behavior and therefore cannot be adequately controlled with a conventional PI control. This article discusses an alternative approach to pH process control using model-free learning control (MFLC), which is based on reinforcement learning algorithms. The MFLC control technique is proposed because this algorithm gives a general solution for acid-base systems, yet is simple enough to be implemented in existing control hardware without a model. Reinforcement learning is selected because it is a learning technique based on interaction with a dynamic system or process for which a goal-seeking control task must be performed. This "on-the-fly" learning is suitable for time varying or nonlinear processes for which the development of a model is too costly, time consuming or even not feasible. Results obtained in a laboratory plant show that MFLC gives good performance for pH process control. Also, control actions generated by MFLC are much smoother than conventional PID controller. (C) 2006 Elsevier Ltd. All rights reserved.