Intelligent Robust Control Design of a Precise Positioning System

Intelligent Robust Control Design of a Precise Positioning System

This paper addresses an intelligent uncertainty function to improve the robust stability and performance of H∞ controlled system in terms of reduced conservatism. The system is identified, out-put performance and control signal requirements are controlled by proper selection of performance and control weighting functions. Adaptive Neuro Fuzzy Inference System (ANFIS) learns the uncertainty bounds of model uncertainty that results from unmodeled dynamics and parameter variations, then the developed uncertainty weighting function will be included in the synthesis of the H∞ controller. v-gap measure is utilized to validate the intelligent identified uncertainty bounds and measure the stability of the designed H∞ controlled system as well. Experimental results on a servo motion system reveal the advantages of combining intelligent uncertainty identification and robust control. Improved performance is achieved. The proposed approach also allows for iterative experiment design.

This paper addresses an intelligent uncertainty function to improve the robust stability and performance of H∞ controlled system in terms of reduced conservatism. The system is identified, out-put performance and control signal requirements are controlled by proper selection of performance and control weighting functions. Adaptive Neuro Fuzzy Inference System (ANFIS) learns the uncertainty bounds of model uncertainty that results from unmodeled dynamics and parameter variations, then the developed uncertainty weighting function will be included in the synthesis of the H∞ controller. v-gap measure is utilized to validate the intelligent identified uncertainty bounds and measure the stability of the designed H∞ controlled system as well. Experimental results on a servo motion system reveal the advantages of combining intelligent uncertainty identification and robust control. Improved performance is achieved. The proposed approach also allows for iterative experiment design.