Controller Applications Using Radial Basis Function Networks
Koji Takahashi · Studies in fuzziness and soft computing · 2001
Methods of designing a radial-basis-function-network-based (RBFN) controller and implementing it for servo controlling mechanical systems are presented. Focusing on the derivative of sigmoid function, we derive an RBFN controller by applying a differential operator to a neural servo controller. Applications for controlling a flexible micro-actuator and a 1-degree-of-freedom robot manipulator using RBFN controller are also described. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.