Data-driven self-tuning control by iterative learning control with application to optimize the control parameter of turbocharged engines

The applications of iterative learning control (ILC) in control of modern process and mechatronic system have received more attentions in recent years. This is due the fact that ILC does not depend on physical model of the system. In the application of ILC in automotive industry, the restriction is that these methods calculate the input value and the tuning of an available controller with fixed structure is not possible. To solve this problem a method is proposed in this paper which consists of two main steps: the first step is the calculation of an input variable, based on an ILC algorithm, and the second step is the optimization of the given parameters of the feedforward controller. The performance and effectiveness of the proposed method are shown with experiments on a test vehicle with an one stage turbocharged gasoline motor with wastegate.