Model-free predictive control for a class of asymmetric nonlinear systems

A model-free predictive control (MFPC) method is proposed for a kind of discrete nonlinear asymmetric systems (NASs) whose models are undefined. According to the dynamic characteristics of the asymmetric system, the compact format dynamic linearization (CFDL) technology and the improved projection algorithm are adopted to set up models for the controlled NAS. Then, based on the estimation models, the explicit analytical solution of the controller is obtained by solving the quadratic function of finite-time domain rolling optimization. Finally, a switching law is designed to make the system switch reasonably between the positive and negative models, thus ensuring the stability of the whole system. The effectiveness of the proposed method is testified by a simulation example.