Norm-Optimal Iterative Learning Control for a High-Speed Linear Axis with Pneumatic Muscles

This paper presents a norm-optimal iterative learning control for a new linear axis. Its guided carriage is driven by a nonlinear mechanism consisting of a rocker with a pair of pneumatic muscle actuators arranged at both sides. This innovative drive concept allows for an increased workspace as well as higher carriage velocities as compared to a direct actuation. Modelling leads to a system of four nonlinear differential equations. The proposed control has a cascade structure. The internal pressure of each pneumatic muscle is controlled by a fast underlying control loop. Hence, the control design for the outer control loop can be simplified by considering these controlled muscle pressures as ideal control inputs. For the outer control loop a norm-optimal iterative learning control of the rocker angle is proposed. The implemented ILC algorithm takes advantage of actual state information as well as of data from previous trials. Experimental results from an implementation on a test rig show an excellent control performance.