Reinforcement Learning Based Control Design for a Floating Piston Pneumatic Gearbox Actuator

Abstract

Electro-pneumatic actuators play an essential role in various areas of the industry, including heavy-duty vehicles. This article deals with the control problem of an Automatic Manual Transmission, where the actuator of the system is a double-acting floating-piston cylinder, with dedicated inner-position. During the control design of electro-pneumatic cylinders, one must implement a set-valued control on a nonlinear system, when, as in the present case, non-proportional valves provide the airflow. As both the system model itself and the qualitative control goals can be formulated as a Partially Observable Markov Decision Process, Machine learning frameworks are a conspicuous choice for handling such control problems. To this end, six different solutions are compared in the article, of which a new agent named PG-MCTS, using a modified version of the 'Upper Confidence bound for Trees' algorithm, is also presented. The performance and strategic choice comparison of the six methods are carried out in a simulation environment. Validation tests performed on an actual transmission system and implemented on an automotive control unit to prove the applicability of the concept. In this case, a Policy Gradient agent, selected by implementation and computation capacity restrictions. The results show that the presented methods are suitable for the control of floating-piston cylinders and can be extended to other fluid mechanical actuators, or even different set-valued nonlinear control problems.