Multi-Objective Model Predictive Control

For many real-world problems, true function form cannot be given a prior. Consequently, for example, engineering design requires experiments and/or numerical simulations to evaluate objective and constraint functions as function in terms of design variables. However, those experiments/simulations are computationally expensive. For alleviating this burden of function evaluations, model predictive optimization (or surrogate model-based optimization depending on literatures) methods have been used extensively in recent years. As a result, constructing good surrogate model with as few function evaluations as possible is essential to finding an optimal solution for problems. This research considers model predictive optimization problems under a dynamic environment with multiple objectives. Some techniques using machine learning such as support vector regression or radial basis function networks are applied to the generation of surrogate model. Although they are effective for model prediction, their prediction abilities may become worse due to a long prediction period. In order to develop accurate and stable prediction in model predictive optimization under a dynamic environment with multiple objectives, we propose computational intelligence methods with predetermined model, and investigate its effectiveness through numerical examples.