Data-driven adaptive tuning method of traditional incremental integral control law

In this paper, we propose a data-driven adaptive tuning (DDAT) method of traditional incremental integral control laws for unknown nonlinear and non-affine systems. Firstly, the original nonlinear system is transformed into its equivalent linear model based on the input and output (I/O) data using the compact form dynamic linearization (CFDL) method. Then the learning gain of the integral control law is adjusted by designing and optimizing an objective function. This method only needs the I/O data instead of relying on the model information of the controlled plant. The convergency analysis is also given. Finally, a numerical example and a heat exchanger example are adopted to illustrate the effectiveness of the proposed method.