Model-Free Learning Control for processes with constrained incremental control

This paper proposes a technique to design controllers for systems with constrained incremental control and input-output constraints called model-free learning control (MFLC). MFLC, which is based on reinforcement learning algorithms, is a simple approach without needing precise detailed information of the system. MFLC is proposed for process control, which in practical problems exhibit constraints. As a simple example, the controller is designed and tested for a two-tank system. Simulation results show that the MFLC controller learns to adequately control the process