Training neural-net controllers with the help of trajectories generated with fuzzy rules (demonstrated with the truck backup task)

In controls, it is known that control path planning is a task quite different from that of 'next-step' control action generation. This is, for example, why training a neural-net controller for the truck backup task is so tedious; the appropriate backing trajectories have to be discovered by trial and error before the net can be depended on to behave well in a 'next-step' manner. In this paper we suggest and demonstrate that this awkwardness can be circumvented by supplying a certain amount of overall system knowledge with use of trajectories (system paths) generated with fuzzy logic, and training the neural-net controller to learn how to generate next-step control actions more or less in conformance with those trajectories. The fuzzy rules and membership functions need not be optimum. The neural-net controller trained in this way performs in a manner superior to that of the fuzzy controller. We demonstrate these circumstances with the truck backup task, using a functional-link net, and making use of only 4 trajectories generated with fuzzy rules.