# A Review of Coordinated Control Technology for Chassis of Distributed Drive Electric Vehicles
## 3 Two-Dimensional Coordinated Control Strategies for DDEV Chassis
### Lateral and Longitudinal Coordinated Control Strategies
Ultimately, this method facilitates the entire closed-loop control process, from dynamic modeling and real-time data acquisition to intelligent reasoning and decision-making [72; 73; 74]. Figure 5 illustrates a traditional mechanism model-based multi-agent coordinated control scheme for AFS and DYC, wherein AFS and DYC are treated as two independent agents that perform online optimization based on the vehicle's physical model to formulate a coordinated control strategy. These strategies are then specifically implemented by lower-level execution controllers, acting upon the chassis system to achieve the dual objectives of enhancing path tracking accuracy and vehicle stability. When deeply integrated with advanced control methods and specific control architectures, the concept of decomposing complex systems and achieving control through agent coordination has attracted widespread attention and in-depth research from numerous scholars in the field of chassis coordinated control. For example, [PERSON] et al. [75] proposed a coordinated optimal control method based on MAS for AFS and DYC in Four-Wheel Independent-Drive Electric Vehicles (4 WIDEV), where the four wheels of the vehicle are modeled as independent agents. This method transforms the vehicle's complex high-dimensional model into four lower-dimensional agent models and employs Distributed Model Predictive Control (DMPC) and SMC for coordinated control, aiming to dynamically balance vehicle driving stability and energy economy. Research results show that, compared to traditional centralized control strategies, this MAS method can increase the solving speed of torque distribution by 32.33 times and reduce the average energy consumption of in-wheel motors by 16.6%, while ensuring driving stability under typical operating conditions such as constant-speed cornering and accelerating cornering. [PERSON] et al. [76] adopted a multi-agent method based on Cooperative Distributed Model Predictive Control (Co-DMPC) for the coordinated control of Four-Wheel Drive/Four-Wheel Steering (4 WD/4 WS) vehicles. This method begins by establishing distributed state equations for both the steering and the drive/braking systems. These equations are designed to reflect the coupling between their respective states and control inputs. Next, it meticulously designs local optimization problems that feature cost function coupling. This is complemented by information exchange protocols and iterative solving mechanisms between the agents. Through this structured approach, the method achieves effective coordinated control of the vehicle's complex coupled chassis system. This not only reduces the computational burden on each local controller but also effectively improves the vehicle's path tracking accuracy, dynamic response speed, and overall driving stability. Table 2 further lists other relevant chassis lateral and longitudinal coordinated control strategies. From this, it can be seen that the combination of AFS and DYC is one of the most common and extensively researched coordinated control pairings. AFS is primarily responsible for providing precise lateral path guidance and improving vehicles' transient response characteristics, while DYC effectively suppresses vehicles' instability tendencies by actively generating direct yaw moment. Its effect is particularly significant under limit operating conditions where the tire adhesion force approaches saturation, and this inherent functional complementarity enables the combination to achieve significant results in enhancing vehicles' handling stability and active safety. Furthermore, examining the control effects achieved by these studies, the vast majority point to improvements in vehicle stability (covering yaw stability, roll stability, etc.) and handling performance (such as path tracking accuracy, steering sensitivity). This profoundly reflects the fundamental original intention and core objectives of chassis integrated control research, namely ensuring vehicles' controllability and safety under various complex driving conditions.