Model-Free Learning Control of a Nonlinear CSTR system

The control of two variables of a Nonlinear continuous stirred-tank reactor (CSTR) by the Model-Free Learning Control (MFLC) system is performed in this work. Firstly, it will show the model math of the plant. Secondly, it will design the MFLC System based on Reinforcement Learning (RL) approach, selecting the characteristics of the states, actions, rewards functions and other parameters of design. Finally, it will perform the simulations for step references, sinusoidal references and constant reference with disturbance signal.