## 1 Introduction  
The suspension system, playing a significant role in improving ride comfort and driving safety performance of in vehicles, is divided into the sprung mass part and the unsprung mass part [1]. A considerable number of studies about suspension control methods have been carried out to suppress the vibration of the sprung mass and the unsprung mass. For example, sky-hook/ground-hook control, sliding mode control, fuzzy control algorithm, etc. have been used for the suspension control system [2; 3; 4; 5] to adapt to various conditions. Because of the simpler algorithm and better reliability, sky-hook control has been widely used in various vehicles. However, the ride comfort performance and the driving safety performance of the vehicle under sky-hook control is conflicting that the better ride comfort leads to the worse driving safety [6]. Therefore, in the light of vehicle state, intelligently restraining sprung mass and unsprung mass vibration at the same time is key to improving the comprehensive performance of the suspension system. Furthermore, the calibration process exerts a significant effect on suspension controller design. Based on the above analysis, instead of single control methods, hybrid control approaches with intelligent switching rules are suggested to improve the vehicle comprehensive performance. Among them, the sky-ground hook (S-GH) control [1] was used to enhance the performance of the controlled system. In order to handle conflict requirements between the ride comfort and driving safety, the S-GH control method has been studied by various researchers for the semi-active suspension system. [PERSON] et al. proposed this hybrid control method firstly, combining sky-hook control and ground-hook control [7]. [PERSON] et al. presented an adaptive sliding mode control algorithm based on the S-GH reference model [8]. [PERSON] et al. added a state observer to improve the accuracy of the state estimation for the S-GH suspension model [9]. [PERSON] et al. proposed a hybrid system that consisted of acceleration driven damper control and ground-hook control and designed a fuzzy controller to decide the weight of each control [3]. [PERSON] et al. proposed sky-ground hook control to suppress the vibration of energy-regenerative suspension and the mode switch strategy was decided by sliding mode controller [10]. Therefore, S-GH control is a good choice to alleviate the conflict problem between ride comfort and driving safety. However, S-GH control is essentially a compromise, not the desired solution. Compared with single S-GH control, hybrid control combining with S-GH control may be a better solution. In other fields, the methods of the lookup table and rule base have been proposed to apply for the hybrid system. To make battery energy storage system more dispatchable, rule-based control scheme was proposed to incorporate the operating constraints of the battery energy storage system (BESS) by [PERSON] et al. [11] [PERSON] et al. proposed the torque-leveling threshold-changing strategy, which involved a small number of rules than common rule-based control, to solve the energy management problem in parallel hybrid electric vehicles [12]. In terms of the angle measurement performance problem of a coprime linear array, [PERSON] et al. adopted the direction-of-arrival estimation algorithm based on the lookup table method [13]. An alternative strategy for logarithmic transformation was presented by [PERSON] et al. based on the lookup table to accelerate the imaging processing [14]. However, the design process of the lookup table and rule-based methods is time and effort consuming. In addition, the lookup table and rule-based methods are always switching systems, whose switching stability should be considered. Therefore, the novel theoretical guidance should be provided for the lookup table and rule-based methods to conduct the design process and eliminate the switching condition. Fuzzy control has also been widely applied for a hybrid system recently. [PERSON] et al.