Learning Feedforward Control for Industrial Manipulators

Abstract

In this work, an iterative learning control (ILC) algorithm is proposed for industrial manipulators. The proposed ILC algorithm works coordinately with the inverse dynamics of the manipulator and a feedback controller. The entire control scheme has the ability of compensating both repetitive and non-repetitive disturbances; guaranteeing the control accuracy of the first implementation; and improving the control accuracy of the manipulator progressively with successive iterations. In order to build the the convergence of the proposed ILC algorithm, a composite energy function is developed. A case study on a four degree of freedom industrial manipulator is demonstrated to illustrate the effectiveness of the proposed control scheme. By implementing the ILC algorithm, the maximum root mean square error of the control accuracy is improved from 0.0262 rad to 0.0016 rad within ten iterations.

Humanoid control of lower limb exoskeleton robot based on human gait data with sliding mode neural network

“…The robot model is a dynamic equation of motion established based on the link model. Feedforward control was also widely used in other robotic control; Chengyuan designed a control scheme that included an iterative learning control feedforward controller, an inverse dynamics feedforward controller, and a PD feedback controller [17]. The accuracy of the robotic arm control has been improved.…”