Augmentation of optimal control with recurrent neural network and wavelet signals

A modular dynamic neural network which has been used in preceding research to solve uncertain algebraic problems and to represent and compress signals, is applied to the representation of an adjustment control in the optimal control of simple nonlinear systems. The optimal control method implemented is based on the loop transfer recovery (LTR) technique, and the basis for signal representation is composed of Daubechies wavelets. Averaging the optimal controllers obtained at three representative equilibrium states followed by using the adjustment control signal leads to a single structure capable of controlling the system over a wider range of operation. A simple inverted pendulum example is presented, and an averaging optimal controller is designed. The neural network is used to represent an adjustment control signal by a linear combination of six wavelets. The resulting signal is incorporated with the averaging LTR controller in order to enhance its performance. Simulation results are presented and discussed