A Multidisciplinary Robust Collaborative Optimization Method Under Parameter Uncertainty Based on the Double-Layer EI–Kriging Model

In multidisciplinary design optimization (MDO) of high-end equipment, parameter uncertainty significantly undermines performance robustness. Existing methods are limited in convergence efficiency and in controlling uncertainty propagation. To address this gap, we propose a multidisciplinary robust collaborative optimization method under parameter uncertainty (MRCO-PU). The approach augments traditional Collaborative Optimization (CO) with a collaborative optimization method based on weight distribution difference information (CO-WDDI) to accelerate cross-disciplinary convergence. It also integrates a double-layer EI–Kriging robust optimization model to enhance robustness under complex coupling and small-sample conditions. The MRCO-PU method targets single-objective, strongly coupled, multi-constraint MDO problems with high per-evaluation cost. The method was validated on a mathematical case and on a cantilever roadheader cutting-head case. In the mathematical case, the robust feasibility of the constraints increased from 0.49 to 1.00. In the engineering case, the specific energy consumption decreased by 6.3% under the premise of fully satisfying the robust feasibility of the constraints, leading to operational cost minimization under uncertainty. This work provides an effective approach to multidisciplinary robust optimization for high-end equipment.