Abstract
The remarkable properties of sliding mode control (SMC)—such as robustness, accuracy, and ease of implementation—have contributed to its wide adoption by the control community. To accurately compensate for parametric uncertainties, the switching part of the SMC controller should have gains that are sufficiently large to deal with uncertainties, but sufficiently small to minimize the chattering phenomena. Hence, proper adjustment of the SMC gains is crucial to ensure accurate and robust performance whist minimizing chattering. This paper proposes the design and implementation of an optimal fuzzy enhanced sliding mode control approach for a Stewart parallel robot platform. A systematic approach of designing the table of rules of the fuzzy system so as to provide the required coefficients of the sliding mode controller is proposed. The aim is to attain optimum performance and minimum control effort, thus eliminating the need for computationally expensive expert systems and yielding control outputs below the actuator saturation ranges. The proposed approach was validated using a six degrees-of-freedom Stewart platform subject to external disturbances. Its performance was compared to that of a standard SMC approach. The obtained results and comparative study showed that the proposed control algorithm not only reduces chattering, but also responds effectively to the realistic demands of control energy, while preventing actuator saturation.