Design and Optimization on Active Engine Mounting Systems for Vibration Isolation by CaoYucong M-B1-5479-3 Thesis Supervisor: Dr. Xie Zhengchao Thesis Co-supervisor: Prof. Wong Pak Kin Department of Electromechanical Engineering One of the main functions of the automotive vehicle engine mounting system is to support the engine body and provide comfort ride to passengers by reducing vibration caused by engine excitations. Traditional elastomeric mount has a simple structure but just can only provide a small amount of damping. Hydraulic engine mounts (HEM) can offer a better performance than traditional elastomeric mounts which operate at a lower frequency. At the same time, the numerical simulation shows that the active control engine mount (ACM) is capable of significantly reducing the vibration transmission. In this work, an extended test bench model of active control mounts in powertrain from previous existing research work is developed and implemented in MATLAB. In order to validate the implementation of the test bench model of active control engine mounting systems in this work, a finite element analysis (FEA) method is used in ANSYS and compared with analytical models for validation. After the validation, a control strategy is integrated into the analytical model by using the linear quadratic regulator (LQR) method, which is a well-knows design technique that provides practical feedback gains. In addition, fuzzy logic control strategy is compared with LQR. The weight matrices in LQR are very significant and it affects the control performance immediately. The improvement of accuracy of the model, as is known to all, which will leads to model of high complexity. As one of originality of this work, the application of genetic algorithms (GA) is also adopted in optimizing the weight matrices of linear quadratic regulator and will aid in enhancing prediction and performance of vehicle NVH. Compared with the experience selection in a typical design technique linear quadratic regulator method, as it turns out, GA can be more efficient and effective in finding the optimum feedback gain with the cost function in the numerical example.