In this paper, a new approach to the optimal control with constraints is proposed to achieve a desired end product quality for nonlinear process based on new kernel extreme learning machine (KELM). The contributions of the paper are as follows: (1) In existing ILC algorithm, the model was built only between manipulated input variables U and output variables Y without considering the state variables. However, the states variables X-state are important in the industrial processes, which are usually constrained. In this paper, the variables are divided into state variables X-state, manipulated input variables U and output Y in the process of modeling. Then Delta U can be obtained by batch-to-batch iterative learning control separately. Kernel algorithm is added to ELM. (2) Constraints of state variables X-state and the input variables U are considered in the current version. PSO isused to solve the optimization problem. (3) Kernel trick is introduced to improve accuracy of ELM modeling. New KELM algorithm is proposed in the current version. The input trajectory for the next batch is accommodated by searching for the optimal value through the error feedback at a minimum cost. The particle swarm optimization algorithm is used to search for the optimal value based on the iterative learning control (ILC). The proposed approach has been shown to be effective and feasible by applying bulk polymerization of the styrene batch process and fused magnesium furnace. (C) 2011 Elsevier Ltd. All rights reserved.