An inverted pendulum presents a typical problem of controlling a nonlinear multivariable system. In the paper a discussion is presented of inverted pendulum control by a neural network. The neutral network learns a control law through the trial and error method. In the simulation and actual system, it was possible to invert the pendulum stably at the target position after some trial and error.>

A mobile robot with behavior controlled by a neural network, and its learning method are presented. The robot has four wheels and travels with two motors; it has twelve sensors for detecting internal conditions and changes in environment. The sensor signals are fed into the input layer of the network, and the network outputs motor control commands. The network model is divided into two subnetworks connected to each other with a short-term memory to process time-dependent data. The robot can learn various habits by changing the patterns to be taught. One example, the habits to play a cops-and-robbers game, was taught. Through training, the robots learned habits such as capture and escape.>