Using CMA-ES for tuning coupled PID controllers within models of combustion engines
This work addresses the time-consuming tuning process for engineers in combustion engine control, representing an incremental improvement in applying existing optimization methods to a specific domain.
The paper tackles the laborious task of tuning PID controller gains for combustion engines by formulating it as a black-box optimization problem, using CMA-ES with specific enhancements to achieve efficient tuning, verified on six real engine models with performance comparisons to PSO and SHADE.
Proportional integral derivative (PID) controllers are important and widely used tools in system control. Tuning of the controller gains is a laborious task, especially for complex systems such as combustion engines. To minimize the time of an engineer for tuning of the gains in a simulation software, we propose to formulate a part of the problem as a black-box optimization task. In this paper, we summarize the properties and practical limitations of tuning of the gains in this particular application. We investigate the latest methods of black-box optimization and conclude that the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) with bi-population restart strategy, elitist parent selection and active covariance matrix adaptation is best suited for this task. Details of the algorithm's experiment-based calibration are explained as well as derivation of a suitable objective function. The method's performance is compared with that of PSO and SHADE. Finally, its usability is verified on six models of real engines.