# A Review of Coordinated Control Technology for Chassis of Distributed Drive Electric Vehicles
## 2 Architectures for Coordinated Control of DDEV Chassis
### Hierarchical Architecture  
To effectively overcome the insufficient coordination capability of decentralized architectures and the excessive implementation complexity and reliability risks of centralized architectures, the hierarchical control architecture emerged. This control architecture was first proposed by [PERSON] et al. [41] and is widely considered one of the most promising technical approaches for achieving the coordinated control of complex systems, especially chassis coordinated control. The typical structure of a hierarchical architecture is shown in Figure 3. In this architecture, complex vehicle control tasks are decomposed and processed at different layers. A typical hierarchical structure usually includes three distinct levels: a Decision Layer, a Coordination Layer, and an Execution Layer. The Decision Layer is responsible for generating the desired overall vehicle motion control targets (e.g., the desired yaw rate, center of gravity sideslip angle) based on driver intent, real-time vehicle status, and external environmental information. The Coordination Layer calculates the generalized forces/moments (e.g., total driving/braking torque) required to achieve the vehicle control targets issued by the Decision Layer. Subsequently, it optimally distributes these calculated forces/moments to the individual physical actuators. This distribution is achieved through advanced control allocation algorithms and by comprehensively considering physical constraints such as the dynamic response characteristics and torque output limits of actuators (e.g., drive motors, brakes), as well as tire adhesion limits. The Execution Layer is responsible for receiving and accurately tracking the specific control commands allocated by the Coordination Layer. It rapidly and precisely drives the corresponding actuating mechanisms through the controllers of various subsystems (e.g., motor controllers, steering actuator controllers, suspension controllers). The hierarchical architecture provides a clear and efficient framework for achieving complex chassis coordinated control, and thus has attracted extensive research and exploration from numerous scholars. Against this backdrop, [PERSON] et al. [42] proposed a three-layer control framework based on Adaptive Model Predictive Control (AMPC). This framework, through its use of the AMPC algorithm and a dynamic weight adjustment mechanism, effectively coordinates the handling and lateral stability of DDEVs. This approach fully demonstrates the unique advantages of hierarchical control in handling multi-objective, time-varying nonlinear systems. [PERSON] et al. [43] proposed a hierarchical  
Figure 3: Hierarchical architecture. architecture for integrated chassis control that fuses semi-active suspension and differential braking control. In the control allocation layer, they introduced suspension damping control based on a roll-region index, successfully achieving effective coordination between the semi-active suspension and differential braking, thereby simultaneously enhancing the vehicle's lateral stability and roll control performance. [PERSON] et al. [44], on the other hand, utilized a hierarchical control architecture to effectively address the real-time coordination challenges of path tracking, handling, and stability control for autonomous vehicles in actual driving scenarios. The study employed an adaptive coordinated control strategy based on Model Predictive Control (MPC). To enhance the model's prediction accuracy, it used Adaptive Forgetting Factor Recursive Least Squares (AFF-RLS) to estimate tire cornering stiffness in real time. Additionally, the strategy combined phase plane analysis to delineate vehicle stability regions with an adaptive weight function adjustment to achieve a dynamic coordination of multiple control objectives. Under challenging conditions of a high speed, large curvature, and varying road adhesion, this method significantly improved the vehicle's path tracking accuracy, handling flexibility, and lateral stability.