ABSTRACT
Neural networks can be used to solve highly nonlinear control problems. This chapter shows how a neural network can learn of its own accord to control a nonlinear 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. Two examples are given to illustrate these ideas. A neural network is trained to control an inverted pendulum on a cart. This is a self-learning “broom-balancer.” The “truck backer-upper,” a neural network controller steering a trailer truck while backing up to a loading dock, is also 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 nonlinear control problems.
