Model-free reinforcement learning approach to optimal speed control of combustion engines in start-up mode

Abstract

This paper presents a model-free reinforcement learning approach for optimal speed control of gasoline engines. First, the physics of the controlled internal combustion engines are discussed to show the uncertainty and the complexity in the model of the dynamics during start-up operation mode, which is the main motivation for challenging learning-based design. Then, a learning algorithm, particularly focused on the continuous time nonlinear dynamics, is constructed to avoid the use of the probing noise usually required in the existing learning algorithms. With the constructed learning algorithm, a learning-based control scheme is designed to solve the optimal speed control problem of a production gasoline engine. Finally, experiments are conducted on a full-scale test bench with a 4-cylinder gasoline engine used for the production of hybrid electric vehicles, and simulation and experimental validation are demonstrated.