A new sequential sampling method of surrogate models for design and optimization of dynamic systems

Abstract When solving combined plant and control optimization (co-design) problems of actual dynamic systems, the models with computationally expensive states derivative functions will often be encountered. Jacobian information cannot be effectively extracted in the optimization process since the expressions of these models are extremely verbose or models are given in the form of simulation programs, which makes gradient-based optimization algorithms difficult. Then the alternative approaches based on finite difference technique are selected. These methods require numerous original expensive evaluations, hence co-design optimizations of these complicated systems are basically not impractical. Here, we propose a new sequential sampling strategy based on error filtering and distance clustering to construct and update surrogate models. The computational cost is significantly reduced through deploying these cheap surrogate models and their easy-to-extract Jacobian information. Dynamic optimization examples of the 2 DOF and 3 DOF robot illustrate the comparison of several sampling strategies and show the feasibility and efficiency of the proposed method. The co-design example of a wind turbine is followed to demonstrate its application prospect in engineering designs.