A one neuron truck backer-upper

The truck backer-upper has been used to demonstrate the ability of neural networks to solve highly nonlinear control problems where the solution is not easily obtained by analytical techniques. The authors demonstrate that good linear solutions to this problem exist, and that it is very easy to find such solutions. It is shown how to design a controller to perform this task, and how it is implemented with a single control neuron. The control neuron requires only two input variables and two weights to produce correct steering signals. The probability that random weights are adequate to solve the problem is so high that a random search is highly successful. It is shown that a single neuron is also sufficient to solve the seemingly more difficult task of backing up a truck with two trailers, and that with small addition in network complexity the problem of providing minimum length backup trajectories can be solved too.<>