Optimal control of dynamic systems using a new adjoining cell mapping method with reinforcement learning

This work aims to improve and simplify the procedure used in the Control Adjoining Cell Mapping with Reinforcement Learning (CACM-RL) technique, for the tuning process of an optimal contro ller during the pre-learning stage (controller design), making easier the transition from a simulation environment to the real world. Common problems, encountered when working with CACM-RL, are the adjustment of the cell size and the long-term evolution error. In this sense, the main goal of the new approach, developed for CACM-RL that is proposed in this work (CACMRL*), is to give a response to both problems for helping engineers in defining of the control solution with accuracy and stability criteria instead of cell sizes. The new approach improves the mathematical analysis techniques and reduces the engineering effort during the design phase. In order to demonstrate the behaviour of CACM-RL*, three examples are described to show its application to real problems. In All the examples, CACM-RL* improves with respect to the considered alternatives. In some cases, CACM- RL* improves the average controllability by up to 100%.