Genetic learning for direct inverse neural network control

The application of neural nets (NN) as a direct inverse controller for general nonlinear systems is considered. Since little knowledge of the nonlinear plant is normally available, it is difficult to obtain an analytical expression for its Jacobian. Thus, an emulator is required as a channel to compute the derivative of the output with respect to the input for NN training. Neural net training using genetic algorithms (GA) offers several advantages. No understanding of the plant model is required. Since no derivative computations are involved, it is less likely for these algorithms to get trapped in local minima. The scheme generates individual controllers with the best fitness values. A hybrid coding method and several appropriate modifications of the classical genetic algorithms for NN control purposes are discussed. To overcome the difficulties of saturation and fluctuation in the controller output, the output of the NN controller is obtained as the sum of several small sigmoidal functions. This effectively increases the linear range of operation of controller output without affecting the nonlinear feature of a sigmoidal function. It is noted in this case that, better control is achieved. Fuzzy logic with dynamic features is used to provide an optimal direction for genetic search. It, thus, speeds up the process of convergence by bringing the chromosomes near to the problem space and bringing more exploration amongst the most desirable ones. The method is demonstrated with the control of a single-link flexible manipulator