Experimental verification of constrained iterative learning control using successive projection

In many practical applications, constraints are often present on, for example, the magnitudes of the control inputs. Recently, based on a novel successive projection framework, two constrained iterative learning control (ILC) algorithms were developed with different convergence properties and computational requirements. This paper investigates the effectiveness of these two methods experimentally on a gantry robot facility, which has been extensively used to test a wide range of linear model based ILC algorithms. The results obtained demonstrate the effectiveness of the algorithms in solving one form of the general constrained ILC problem.