INDIRECT ITERATIVE LEARNING CONTROL OF TAKAGI-SUGENO FUZZY SYSTEMS

An indirect iterative learning control is proposed to update the gains of a Parallel Distributed Compensation controller when the trajectory is repetitive. The corresponding Takagi-Sugeno fuzzy system, to be controlled, is supposed to exhibit time-varying matrices in the consequent part. When the membership functions are fixed, it will be possible to estimate the consequent system for every time, using different control gains for each trial. Finally, when the system parameters are considered as properly estimated, it will be possible to derive a controller which will not only be stable but will also exhibit good tracking properties.