Updating reduced-order dynamic model for flexible-joint arm to design adaptive feedback linearization control

This study deals with the construction of an enhanced dynamic model for a lever-arm with flexible-joint to design a feedback linearization controller. The uncertainties of reduced-order model with nominal parameters are compensated via a complementary term to upgrade it to the actual system. The proposed method uses data fusion of encoder/gyroscope to remove the measurement errors. Mathematical analyses are provided to show the stochastic stability of the proposed scheme. Accordingly, a fuzzy adaptive solution is presented to effectively schedule the free parameters of the algorithm. The enhanced reduced-order model is used to design a feedback linearization law for control of lever-arm position. The controller adapts itself to the actual system, and is reliable and cost-effective because of using less sensors. The simulation studies indicate a higher accuracy of the proposed system compared with the controller designed by the full-order model in the presence of parametric uncertainties and disturbances. Also, experimental results conducted on a fabricated platform demonstrate the effectiveness of the suggested system to control the arm position in different scenarios. The comparative results with prevalent controllers indicate higher accuracy with smaller control input for the proposed controller to reject the disturbances and other uncertainties.