Use of adaptive fuzzy systems in parameter tuning of sliding-mode controllers

Soft computing methodologies, when used in combination with sliding-mode control (SMC) systems, aim to alleviate implementation difficulties of SMCs or to intelligently tune the controller parameters. In this paper, it is proposed to combine adaptive fuzzy systems with SMCs to solve the chattering problem of sliding-mode control for robotic applications. In the design of the controller, special attention is paid to chattering elimination without a degradation of the tracking performance. Furthermore, the a priori knowledge required about the system dynamics for design is kept to a minimum. The paper starts with a consideration of basic principles of sliding-mode and fuzzy controllers. Implementation difficulties and most popular solutions are then overviewed. Next, the design of a SMC reported in the literature is outlined and guidelines for the selection of controller parameters for the best tracking performance without chattering are presented. A novel approach based on the introduction of a ''chattering variable'' is developed. This variable, as a measure of chattering, is used as an input to an adaptive fuzzy system responsible for ringing minimization. On-line tuning of parameters by fuzzy rules is carried out for the SMC and experimental results are presented. Conclusions are presented lastly.