LEARNING CONTROL-BASED ON LOCAL LINEARIZATION BY USING DFT

Learning control is one of the most interesting subjects in robotics f ield, and several works on this topic were extensively investigated. L earning control is necessary for high-speed and high-precision traject ory control in cases where an objective system includes uncertain para meters and/or has practical limitations on the feedback control. Conve ntional learning control methods, however, have a problem concerning h ow to determine a learning operator that guarantees the convergence of the scheme without a priori knowledge of an objective system. For ins tance, designing learning controllers that will work for complex robot systems, such as pneumatic robots with complicated dynamics or robots with complex sensory feedback, is extremely difficult. This article p rovides a new type of learning control scheme for a class of discrete- time nonlinear systems. The algorithm of proposed learning control uti lizes local linearization techniques by using Discrete Fourier Transfo rm (DFT) to design the learning operator and the numerical function it erative techniques. In our case, the secant method is used, which can find the best learning operator by itself at each learning step, in ot her words, at each calculation step of iteration. This proposed learni ng algorithm has been extensively tested by simulation on the computer . (C) 1994 John Wiley & Sons, Inc.