Neural networks for truck backer-upper control system

Artificial neural networks have been studied for many years in the hope of achieving human-like performance in many fields. One of these fields is to use neural networks to solve highly nonlinear control systems. The multilayer feedforward neural network has proven to be a very powerful tool in this field. Nevertheless, such a network suffers from a very time-consuming training procedure. In this paper, based on radial-basis function and recurrent neural networks, the authors develop two different neural network systems for backing up a computer simulated truck to a loading dock in a planar parking lot. In the authors´ simulations, these two neural networks are capable of learning from the training samples and performing generalization, and therefore provide viable alternatives to the multilayer feedforward neural network for real-world applications