## 1 Introduction  
The suspension is an important part of the vehicle chassis system, and its performance has a great influence on the handling stability, ride comfort, and driver's driving experience. Compared with passive suspension, the active suspension has a better control effect in vibration. It can realize dynamic adaptive adjustments according to the driving conditions and ensure the suspension system is always in the best damping state, which significantly improves the ride comfort and handling stability of the vehicle. Therefore, the control algorithms of active suspension have become the research hotspot in the automotive industry. Many experts have conducted many studies on the algorithms, including adaptive PID control [1], fuzzy control [2], sliding mode control [3], neural networks [4], reinforcement learning [5], etc. Besides, the LQR theory, as one of the earliest and most mature control algorithms in modern control theory, has been studied deeply [6; 7; 8; 9; 10]. In the design of the LQR controller of active suspension, the design requirements of comfort, safety, and deflection conflict with each other. The selection of the weighting coefficients of the function indexes plays a very important role in suspension performance. Usually, multiple performance indexes are weighted to calculate an optimal solution. Therefore, how to select the weighting coefficients becomes the key in the design of the LQR controller. At present, many researchers realized that the traditional weights selection relies too much on the designer's experience, so they have made many attempts to solve this subjective problem. Compared with the various analytic hierarchy process methods [11; 12; 13], genetic algorithm (GA) can eliminate more subjective components. [PERSON] et al. [14] proposed a genetic particle swarm optimization algorithm by combining the advantages of the genetic algorithm and particle swarm optimization (PSO), which is used to optimize the weighting coefficients of performance indexes in the LQR control algorithm. [PERSON] et al. [15] proposed an adaptive predator-prey optimization algorithm, which solved the problem of premature convergence of particles in PSO and the imbalance between the exploration and exploitation of particles in global optimization. In references [16; 17], more genetic algorithms are applied to the optimal control of the active suspension. The weighting coefficients are searched in given weighting ranges by using the population search strategy and global search characteristics. The comprehensive performance of suspension is improved obviously by using these genetic algorithms. However, most of these algorithms have poor local search capability and may take quite a long time to run a globe searching. [PERSON] et al. [18] combined a qualitative analytic hierarchy process with GA to the LQR controller, which can increase the global optimization speed efficiently. Most of these PSO, GA, and other algorithms in references [14; 15; 16; 17; 18] adopt the idea of longitudinal iterative to find the optimal solution, so they must meet high requirements on iterative convergence. In addition, we can only obtain one unique solution from these algorithms, which obviously conflict with three compromised indexes. More importantly, individual differences in driving styles are not considered in the above algorithms. Therefore, how to judge a driver's feature and classify it into different modes are valuable. Many machine learning algorithms, even the simplest clustering approaches, have shown good applications on this topic. [PERSON] et al. [19] used a hierarchical clustering and a principal component analysis to identify and classify driving styles. In references [20; 21; 22], a K-means clustering algorithm was used to classify driving styles into normal type, soft type, and aggressive type.