Neural networks for self-learning control systems

Abstract

Neural networks can be used to solve highly nonlinear control problems. This paper shows how a neural network can learn of its own accord to control a non-linear dynamic system. An emulator, a multilayered neural network, learns to identify the system's dynamic characteristics. The controller, another multilayered neural network, next learns to control the emulator. The self-trained controller is then used to control the actual dynamic system. The learning process continues as the emulator and controller improve and track the physical process. An example is given to illustrate these ideas. The ‘truck backer-upper’, a neural network controller steering a trailer truck while backing up to a loading dock, is demonstrated. The controller is able to guide the truck to the dock from almost any initial position. The technique explored here should be applicable to a wide variety of non-linear control problems.