LMI-Based Additive Gain for Guaranteed Cost Control of Robotic Manipulator with Deadzone

In this paper, deadzone compensator with additive gain is proposed for robust tracking problem. After modeling a two-link manipulator via TS fuzzy model, the neural network is trained on-line to reduce the tracking error caused by deadzone. Based on the linear matrix inequality (LMI) design approach, a class of a state feedback controller with additive gain such as neurocontroller is established. Also sufficient condition for the existence of guaranteed cost (GC) controller is derived. Under the proposed intelligent control scheme, it is shown that we succeed in reducing the tracking error.