Abstract
This article proposes a fuzzy sliding mode control based on multi-objective genetic algorithm for the path control of a two-link robot. Sliding mode control (SMC) is known for its competences in manipulating imprecision and bounded tribulations in modeling. In SMC, the high frequency that eventuates from a halting control action, known as the chattering phenomenon, is a severe problem. Fuzzy logic controller (FLC) has recently gained the popularity of a grand method that is used in combination with the sliding mode control method, which can remove certain drawbacks from this issue. In this hybrid controlling system, the strength of the sliding mode control lies in its fitness to account for modeling imprecision and external tribulations, while the FLC provides premier damping and reduced chattering. Nevertheless, the major drawback in a fuzzy control is its lack of designing techniques. Most of fuzzy rules are human knowledge oriented, and accordingly these rules vary from person to person in spite of the same performance of the system. To handle these time-consuming problems, multi-purpose genetic algorithms are applied for the optimization of fuzzy sliding mode control, through the Pareto approach.
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