Nonlinear control system with radial basis function controller using random search method of variable search length

An optimization method which is a kind of random searching is presented. The proposed method is called RasVal (random search method with variable search length) and it can search for a global minimum based on the probability density functions of searching, which can be modified using informations on success or failure of the past searching in order to execute intensified and diversified searching. By applying the proposed method to a nonlinear crane control system which can be controlled by the universal learning network with radial basis function (RBF), it has been proved that Ras Val is superior in performance to the commonly used backpropagation learning algorithm, and it has also been shown that the Ras Val has better performance of the generalization capability than the gradient method.