Neural network based control for a backward maneuvering trailer truck

The control of non-linear systems using neural networks has gained increasing interest in recent years. The non-linear capabilities of neural networks can be utilized for a large number of non-linear systems that are not controllable by linear techniques. However, controllers based on neural networks are quite difficult to design due to the lack of good training data. The physical plant developed and designed in this work is a scale model of a trailer truck. A neural network based controller is to be developed to drive the truck backwards from any initial condition to a loading dock. One approach to solve this control problem is to design a valid path, and then track the path. Since the valid path depends on initial conditions, a neural network is trained to generate selective points on the path for any initial condition. The path data used to train the neural network are obtained from software based on a previously published work on similar systems. Based on generated path data, a control is designed and is able to successfully maneuver the truck to the loading dock from any initial condition that was within a particular region. The region represented a group of non-trivial trajectories that the truck is to follow.