A Generalized Frequency Domain Learning Control Design with Experimental Validation

This paper presents a generalized iterative learning control (ILC) design in the frequency domain with experimental validation. The optimal ILC learning function and robustness filter function are simultaneously optimized by solving a linear programming problem using frequency response functions. Moreover, the design realizes an optimal trade-off between robust convergence, converged tracking performance, convergence speed, and input constraints. The proposed ILC method is experimentally validated on a lab scale overhead crane system. The results demonstrate the advantages of the approach as an automation design with optimal solutions, efficient computation, robustness and intuitive tuning for tradeoff analyses between multiple ILC specifications.status: publishe