A data-driven adaptive iterative learning predictive control for a class of discrete-time nonlinear systems

On the basis of dynamic linearization method along the iteration axis, a novel data-driven adaptive iterative learning predictive control (AILPC) is presented for a class of general repeatable discrete-time nonlinear systems. The highlight of the algorithm is that the controller design only depends on the I/O data of the dynamical system without using any priori knowledge of the system. The monotonic convergence and effectiveness of the AILPC algorithm are proven and verified through rigorous analyses, numerical example and freeway traffic flow control application.