## 1 Introduction  
Rescue vehicles are important in the transportation of rescue goods as well as rescue workers. The hydro-pneumatic suspension is applied to rescue vehicles due to the great load carrying capacity and the complexity of rescue terrain. The hydro-pneumatic suspension system plays a decisive role in the ride comfort and handling stability of the vehicle, but the passive suspension cannot address various road profiles because of its fixed parameters. As a result, it is meaningful and feasible to study a control strategy for the adjustable hydro-pneumatic suspension system which can perform better in ride comfort and handling stability than the traditional passive suspension [1]. In previous studies, many scholars have studied the control strategy of the adjustable hydro-pneumatic suspension. [PERSON] [2] improved the vehicle rollover stability of active suspension through an LQR (linear quadratic regulator)-based controller with estimation of the road. [PERSON] [3] discussed the active control of hydro-pneumatic suspension parameters of wheel loaders based on a fuzzy controller, which reduces the vertical vibration of the wheel loader. [PERSON] [4] presented the possibility of using slow active suspension control to reduce the body roll and thus reduce the rollover propensity. [PERSON], [PERSON] [5] designed a hydro-pneumatic energy harvesting suspension system based on a PID-PSO (particle swarm optimization) controller, which achieved the maximum riding comfort as the peak values of the acceleration. However, past research on adjustable hydro-pneumatic suspension mainly focused on the analysis of vehicles' ride comfort or handling stability under different operating conditions separately, because it is difficult to achieve optimality in both aspects of ride comfort and handling stability due to the conflicting relationship between them. Hence, it is urgent to concentrate on the dynamic switch control strategy between the vehicle's ride comfort and handling stability to meet different requirements of working conditions. As a powerful controller, the sliding mode controller (SMC) has a great use in control systems because of its good robustness to external disturbance and fast dynamic response. [PERSON], [PERSON] [6] applied a composite SMC for wind power extraction in a remotely located solar PV-wind hybrid system. [PERSON], [PERSON] [7] used an SMC three-phase dynamic voltage restorer with adaptive notch filter to protect sensitive loads. [PERSON], [PERSON] [8] designed a fuzzy SMC for a cooperative robotic system with uncertainty for handling an object. [PERSON], [PERSON] [9] suppressed chaotic vibrations in a suspension system of vehicle dynamics based on the optimal SMC. Inspired by the previous studies mentioned above, an SMC is used to control the hydro-pneumatic suspension system to enhance the ride comfort and handling stability of the rescue vehicle in this paper. Parameters of SMCs should be tuned suitably to achieve the optimal control effect. At present, many optimization methods are used in SMCs, for example, particle swarm optimization (PSO) [10], Genetic Algorithm (GA) [11], and adaptive clonal selection algorithm (ACSA) [12; 13; 14; 15]. PSO has relatively fast convergence speed, while it is easy to fall into local optimal solution, and it is unstable. GA is able to get the global optimal solution based on the natural evolution principle, but it has the shortcomings of longer calculation. ACSA has received increasing attention as one of the approaches to optimize parameters, due to its highly adaptive and easy-to-implement nature [12]. [PERSON] [13] used ACSA to optimize the support vector machine parameters and feature subset selection. [PERSON] [14] presented a novel objective optimization approach based on ACSA to solve the problems of weapon-target assignment in warship formation antiaircraft application.