Multidisciplinary design optimization of mechatronic vehicles with active suspensions

A multidisciplinary optimization method is applied to the design of mechatronic vehicles with active suspensions. The method is implemented in a GA-A'GEM-MATLAB simulation environment in such a way that the linear mechanical vehicle model is designed in a multibody dynamics software package, i.e. A'GEM, the controllers and estimators are constructed using linear quadratic Gaussian (LQG) method, and Kalman filter algorithm in Matlab, then the combined mechanical and control model is optimized simultaneously using a genetic algorithm (GA). The design variables include passive parameters and control parameters. In the numerical optimizations, both random and deterministic road inputs and both perfect measurement of full state variables and estimated limited state variables are considered. Optimization results show that the active suspension systems based on the multidisciplinary optimization method have better overall performance than those derived using conventional design methods with the LQG algorithm. (c) 2004 Elsevier Ltd. All rights reserved.