Abstract
Summary. An inverted pendulum represents an unstable system which is excellent for demonstrating the use of feedback control with different kinds of control strategies. In this work state feedback of the inverted pendulum is examined. First a pole placement algorithm is explored. After that artificial intelligence (AI) methods are investigated to better cope with the nonlinearities of the physical model. The technique used is based on a hybrid system combining a neural network (NN) with a genetic algorithm (GA). The NN controller is trained by the GA against the behaviour of the physical model. The results of the training process show that the chromosome population tends to station at a suboptimal level, and that changes in the environmental parameters have to take place to reach a new optimal level. By systematically changing these parameters the NN controller will gradually adapt to the pendulum behaviour.
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