Model Predictive Control of Nonlinear MIMO Systems Based on Adaptive Orthogonal Polynomial Networks

Abstract

This paper considers a new design of model predictive control based on specific models in the form of adaptive orthogonal polynomial networks, built around a specially tailored basis of generalized orthogonal functions. Polynomial model has a single layer structure and a smaller number of model parameters than classical neural networks, usually used for model predictive control design, leading to lower complexity and shorter calculation time. Desired property of adaptability of the model is achieved by using additional variable factors inside the orthogonal basis. The designed controller was applied in control of twin-rotor aero-dynamic system as a representative of nonlinear multiple input-multiple output systems and compared to the other state-of-the-art control algorithms.

Keywords

• adaptive polynomial neural network
• generalized orthogonal functions
• model predictive control
• twin-rotor aero-dynamic system