Model-free learning adaptive controller with neural network compensator and differential evolution optimization

A new design for a model-free learning adaptive control (MFLAC), based on pseudo-gradient concepts with compensation using neural network, is presented in this paper. A radial basis function neural network using differential evolution optimization technique is applied to the control design. Motivation for developing a new approach is to overcome the limitation of the conventional MFLAC design, which cannot guarantee satisfactory control performance when the plant has different gains for the operational range. Robustness of the MFLAC with neural compensation scheme is compared to the MFLAC without compensation. Simulation results for a nonlinear chemical reactor are given to show the advantages of the proposed compensation approach.