###### Abstract
In addition, a dual sliding mode control is used to calculate the additional steering angle and yaw moment while the Lagrange optimization algorithm is used in the distribution of the tire forces which effectively improves the handling of the vehicle [13]. In the literature [14], a multi-agent system (MAS) is introduced where AFS and the DYC act as agents that work together to improve the vehicle's lateral stability and reduce the workloads of the drivers. At the same time, the Pareto-optimality theory is used to ensure the optimal performance of both AFS and the DYC. However, the vehicle uncertainties and time-varying parameters could affect the stability control performance. Accordingly, [PERSON] et al. proposed a robust linear matrix inequality (LMI)-based H-infinite feedback algorithm for the integration of AFS and the DYC [15]. Based on the active disturbance rejection control and SMC, [PERSON] et al. proposed a coordinated controller of the DYC and AFS where an improved particle swarm optimization algorithm is used to obtain the coordination work weight [16]. As well as integrating AFS and the DYC, [PERSON] et al. used a multi-constrained distributed model predictive control (MDMPC) to integrate the AFS and active suspension [17]. [PERSON] et al. proposed a rule-based coordinated control of DYC and active suspension [18]. [PERSON] et al. used the active suspension force and electronic stability program (ESP) brake torque to improve the yaw and roll stability [19]. The active suspension is also directly used to control the yaw stability [20; 21]. [PERSON] et al. proposed an improved electronic control suspension system consisting of multiple sensors and controllers that can improve the yaw-roll-pitch motion at the same time [22]. The AFS, DYC, active roll control (ARC) and AFS, DYC, and active suspension are, respectively, integrated in the literature [23; 24]. In the literature [25], [PERSON] et al., integrated and optimized a differential steering system, differential braking system, and active suspension system by using an adaptive weight particle swarm optimization algorithm, which significantly improved the driving comfort and steering stability at the same time. However, there are still some problems in this field such as the control chattering and overshoot, an insufficient control accuracy, an excessive control duration, tremendous computation, coupling effects between the controllers, and so on. In this paper, we intend to explore a coordinated control that can combine active suspension and the DYC to further improve both the roll and yaw stability. The detailed contributions are summarized as: with the genetic and fuzzy algorithm optimized proportional integral (PI) active suspension, the body roll angle can be reduced up to 88% less than the passive suspension. With the genetic algorithm optimized sliding mode DYC, the yaw's stability performance is good. More importantly, the chattering is greatly suppressed compared with the traditional SMC. Additionally, the proposed adaptive distribution strategy enables the active suspension and DYC to work in coordination. The roll and yaw stability performance are both further improved. The rest of this article is organized as follows. Section 2 gives the modeling of a ten degree of freedom (10-DOF) vehicle model. In Section 3, the active suspension roll controller, genetic sliding mode DYC, and the coordinated control are developed. The simulation results are provided in Section 4 followed by the conclusions in Section 5.