Neural network based controllers for non-linear systems
Control and computers
In this paper the authors present two approaches for the control of an inverted pendulum on a cart. First, they use a combination of a neural network and a simple algorithm, where the neural network is responsible for ranking the current states and the algorithm decides the control action (+10N or -10N) applied. Simulation results show that this method converges at a faster rate than previous researchers' schemes. In the next approach, the ability of artificial neural networks (ANN) to generate nonlinear mapping is utilized to supplement classical control techniques, to perhaps achieve better performance. The authors present a hybrid controller consisting of a neural network and classical control technique. The neural network was trained to predict nonlinearities in the system. With this prediction in hand, a two-term control law was designed where one term cancels the nonlinear effects, enabling one to use linear control theory (e.g., pole placement, optimal control) to obtain the second term. To check the applicability of the method, the authors tested this scheme on the bilinear model of a paper-making machine. Simulation studies revealed that improvements to the behavior of these systems could be achieved.
Yan, D. and Saif, M.. (1995). Neural network based controllers for non-linear systems. Control and computers, 23 (3), 73-78.