# A Review of Coordinated Control Technology for Chassis of Distributed Drive Electric Vehicles
## 3 Two-Dimensional Coordinated Control Strategies for DDEV Chassis
### Lateral and Vertical Coordinated Control Strategies
The upper-layer controller determines the desired vehicle motion state based on driver input and environmental road information, in conjunction with relevant lateral and vertical vehicle dynamics reference models. The middle-layer controller first receives the desired targets from the upper layer. It then calculates the generalized forces and moments needed to achieve these targets, such as the desired additional yaw moment for the steering system and the total adjustment force for the suspension system. This calculation is based on the vehicle's dynamics model and current state. While performing this task, the controller also adjusts the weights of the responding subsystems according to different control objectives. The main vertically coordinated subsystems include SAS and ASS. Its schematic diagram is shown in Figure 7. SAS actively adjusts the damping force magnitude of its shock absorbers based on real-time perceived road unevenness and vehicle body vibration information. By varying the rate of energy absorption and dissipation by the suspension for wheel hop and body motion, it optimizes tire-ground adhesion and attenuates the transmission of vibrations to the vehicle body, thereby improving the vehicle's ride comfort and handling stability without actively inputting energy, and to some extent it assists in controlling the vehicle's roll and pitch attitudes [93; 94; 95]. ASS incorporates an actuation system capable of actively generating and outputting force, along with complex control units. Based on the vehicle's real-time dynamic parameters and road preview or real-time feedback information, it can not only adjust damping characteristics but also actively apply or absorb energy to generate precise control forces to independently control the vertical motion of each wheel. Through the active control of the suspension system, changes in vehicle body attitude (such as roll angle) can be reduced to varying degrees, and the dynamic tendencies of the vehicle's body can be suppressed, thereby better balancing and satisfying the comprehensive requirements for vehicles' ride comfort and handling stability [96; 97; 98]. In coordinated control frameworks, various control algorithms have emerged in the research field to achieve complex coordinated control objectives for vehicles' lateral and vertical dynamics; Table 3 lists common lateral-vertical coordinated control strategies. Among these, SMC [99; 100; 101] has gained a widespread application in vehicles' dynamic control due to its strong robustness against system parameter uncertainties and external disturbances. [PERSON] et al. [43], in their research into an integrated control strategy for semi-active suspension and differential braking, employed SMC to construct the upper-layer controller for generating the desired yaw control moment and roll control moment. Fuzzy Logic Control (FLC) [102; 103] is another important control strategy. Its prominent advantages lie in not relying on an accurate mathematical model of the controlled object and being able to conveniently incorporate the designer's empirical knowledge and heuristic rules. This makes FLC exhibit a good applicability and flexibility in handling highly nonlinear complex systems with significant uncertainties, as well as in dynamically allocating weights among multiple objectives or adaptively adjusting controller parameters within hierarchical control architectures. For example, in their research on coordinated control for multi-axle steering and active suspension systems, [PERSON] et al. [104] developed a strategy to dynamically balance handling stability and ride comfort. Their approach utilized fuzzy logic algorithms to adjust the weighting coefficients between two controllers: a Dual Sliding Mode Controller (DSM) and a Dual Linear Quadratic Regulator (DLQR). This adjustment was performed in real time, based on the vehicle's speed and front-wheel steering angle. As a result, their method effectively coordinated the vehicle's dynamic demands under various driving conditions. Similarly, [PERSON] et al.