Controller Design for a Non-Redundant Cable Robot Under Input Constraint

Abstract

Cable-suspended robots are structurally similar to parallel actuated robots but with the fundamental difference that cables can only pull the end-effector but not push it. From a scientific point of view, this feature makes feedback control of cable-suspended robots lot more challenging than their counterpart parallel actuated robot. In this paper, we look into control design for a non-redundant cable-suspended robot under positive input constraints. The design is based on feedback linearization controllers augmented with reference signal management. Reference signal management operates in accordance with the receding horizon strategy, by generating admissible reference signals which makes system not to violate the input constraints. An important issue in implementing predictive horizon algorithm for nonlinear systems is to predict the system behavior in a computationally efficient way. We show that feedback linearization controllers with reference signal management can offer an efficient way to predict the system’s future states, using the error dynamics of inner feedback loop. Finally, the effectiveness of the proposed method is illustrated by numerical simulation and laboratory experiments on a six degree-of-freedom suspended cable robot.