Model-free adaptive control design using evolutionary-neural compensator

It is well-known that conventional control theories are widely suited for applications where the processes can be reasonably described in advance. However, when the plant’s dynamics are hard to characterize precisely or are subject to environmental uncertainties, one may encounter difficulties in applying the conventional controller design methodologies. In this case, an alternative design is a model-free learning adaptive control (MFLAC), based on pseudo-gradient concepts with compensation using a radial basis function neural network and optimization approach with differential evolution technique presented in this paper. Motivation for developing a new approach is to overcome the limitation of the conventional MFLAC design, which cannot guarantee satisfactory control performance when the nonlinear process has different gains for the operational range. Robustness of the MFLAC with evolutionary-neural compensation scheme is compared to the MFLAC without compensation. Simulation results for a nonlinear chemical reactor and nonlinear control valve are given to show the advantages of the proposed evolutionary-neural compensator for MFLAC design.