In　this　paper,　based　on　hierarchical　control　structure　in　steady-state　optimization　of　industrial　processes　and　iterative　learning　control　law　for　linear　industrial　process　control　systems,　the　iterative　learning　control　is　applied　to　saturated　nonlinear　industrial　control　systems　and　nonlinear　industrial　control　systems　with　changing　gains,　the　weighted　PD-type　closed-loop　iterative　learning　control　algorithm　and　weighted　power-type　open-closed-loop　iterative　learning　algorithm　are　discussed　respectively,　The　definitions　of　delta　-reachability　of　objective　trajectory　and　epsilon　-convergence　of　the　iterative　learning　control　algorithm　are　suggested.　By　means　of　Bellman-Gronwall　inequality　and　lambda　-norm　theory,　the　convergence　of　the　algorithms　is　also　proved.　The　numerical　simulation　shows　that　the　iterative　learning　control　can　remarkably　improve　the　dynamic　performance　of　industrial　control　systems　in　steady-state　optimizing.