Robust adaptive control
Adaptive control algorithms with multiple models and fuzzy logic switching
Adaptive control algorithms with multiple models and fuzzy logic switching
A new generation of adaptive control: an intelligent supervisory loop approach
A new generation of adaptive control: an intelligent supervisory loop approach
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This paper presents a fuzzy multiple-reference-model generator-based Model Reference Adaptive Control (MRAC) framework for controlling systems that perform a wide range of operating conditions. Following a rule base, the Fuzzy Logic Switching Scheme (FLSS) effectively monitors changes in operating conditions or such drastic changes in plant parameters, and generates a fuzzified reference model output. Then, a single adaptive controller forces the plant output to track the reference, even when plant mode changes. The proposed fuzzy switching Multiple Reference Model Adaptive Controller (MRMAC) is effective as well as feasible for online application, monitoring the plant output at selected control intervals. Unlike static multiple-model algorithms for switching (individual model-based filters do not interact) or switching dynamic algorithms (which are susceptible to numerical overflow), this scheme provides an interactive multiple model generator with soft switching. The strength of the scheme is demonstrated by an application to a theoretical system with disturbed model parameters and for the position tracking of a single-link manipulator. Investigation results show that the proposed scheme performs very positively at different operating modes.