Control of nonholonomic mobile robots using a feedback error learning

The inverse model of a nonholonomic system is not unique. Such an inverse model changes depending on initial conditions and desired values. In this paper, a kind of internal model control (IMC) using feedback error learning is discussed for the tracking control of nonholonomic mobile robots. The present method is different from a conventional IMC whose control system consists of an inverse model, a direct model and a filter. The present disturbance rejection method need not use a direct model, where the remaining two elements are composed of the same inverse model based on neural networks.