This paper presents a methodology embodying identification procedures, uncertain modeling, and robust control design of embedded multivariable control systems. Concerning the identification, this methodology involved the determination of probabilistic uncertainty bounds for multivariable plants based on the black box or gray box identification. The bounds obtained were used in the derivation of an uncertain model in the form of upper Linear Fractional Transformation (LFT). This model was used in the robust control design implementing μ-synthesis. The problems arising on the different design stages were illustrated by an example presenting the embedded robust control of a two-input two-output analog model. The plant was identified by using black box and gray box identification methods that produced the necessary information to develop the corresponding uncertainty models. Two discrete-time robust controllers relevant to the two types of identification were designed and embedded in the physical system. Simulation results for the embedded closed-loop system and experimental results obtained by using the robust controllers were compared.
Robust Control of Robot Manipulator based on QFT and H∞
