A Clonal Selection Algorithm Based Optimal Iterative Learning Control Algorithm

摘要

Improved clonal selection algorithms were proposed as a method to implement optimal iterative learning control algorithms. The strength of the method is that it not only can cope with non-minimum phase plants and nonlinear plants even there are uncertainties in their models, but also can deal with constraints on input signals conveniently by a specially designed mutation operator. Simulations show that the convergence speed is satisfactory regardless of the nature of the plants and whether or not the models of the plants are precise.