Utilizing higher-order neural networks in U-model based controllers for stable nonlinear plants

The use of intelligent control schemes in nonlinear model based control (NMBC) has gained widespread popularity. Neural networks, in particular, have been used extensively to model the dynamics of nonlinear plants. However, in most cases, these models do not lend themselves to easy maneuvering for controller design. Therefore, a common need is being felt to develop intelligent control strategies that lead to computationally simple control laws. To address this issue, we recently proposed a U-model based controller utilizing nonlinear adaptive filters. The present work extends that concept further to include higher-order neural networks (HONN) for better approximation. The main feature of the proposed structure is its ability to capture higher-order nonlinear properties of the input pattern space while allowing the synthesis of a simple control law. The effectiveness of the proposed scheme is demonstrated through application to various nonlinear models and a comparison with the Backstepping controller is presented.