# A Review of Coordinated Control Technology for Chassis of Distributed Drive Electric Vehicles
## 4 Three-Dimensional Coordinated Control Strategies for DDEV Chassis
This mode division directly determines the weighting coefficients of various control components (such as the sideslip angle of the center of gravity, yaw rate, load transfer ratio, and braking force) in the objective function  
Figure 10: Three-dimensional cooperative control in lateral, longitudinal, and vertical directions based on weight allocation. of the upper-layer NMPC. These weighting coefficients are pre-optimized and set for different control modes, ensuring that when specific risks (such as sideslip or rollover) are identified, the controller can prioritize stabilizing the corresponding vehicle dynamics. This method cleverly combines discrete mode judgment with continuous optimal control, achieving an effective coordination of longitudinal, lateral, and vertical aspects. The accurate assessment of driving conditions is also key to successful mode switching. [PERSON] et al. [119] designed a specific vehicle state monitor based on key parameters such as slip ratio to evaluate in real time the criticality of the current driving situation. Based on the monitor's output, the control system can intelligently schedule the intervention timing and intensity of three control actions: braking, steering, and suspension. Through an advanced LPV/Hco control framework, this strategy achieves a smooth coordination between actuators, ensures robustness and stability during mode switching under different operating conditions, and significantly improves the overall dynamic behavior of the vehicle. This study conducted simulations on a complex, experimentally validated full-vehicle model, verifying its effectiveness. However, the researchers also pointed out that when actually deployed on a real vehicle, sensor noise, actuator delays, and robustness under variable environmental conditions still require further validation, and the parameter scheduling of the LPV controllers is highly dependent on an accurate estimation of vehicle states. To pursue its broader adaptability, [PERSON] et al. [120] proposed an adaptive mode switching coordinated control strategy for lateral, longitudinal, and vertical dynamics based on stability domains. The core of this strategy lies in adaptively switching between various preset control modes by the online identification of current driving conditions (such as driver operations, vehicle speed, road adhesion) and the vehicle's comprehensive stability state (multi-dimensional information such as yaw, roll, and pitch). Each mode corresponds to specific control logic and weight configurations for longitudinal, lateral, and vertical subsystems, aiming to optimize comprehensive performance factors such as stability, comfort, safety, and economy under different driving conditions. This stability domain-based adaptive switching mechanism enables the control system to more proactively respond to complex and variable driving environments. In conclusion, three-dimensional coordinated control strategies based on weight allocation (including mode switching) achieve an effective coordination of the vehicle's longitudinal, lateral, and vertical dynamics by dynamically adjusting the weighting relationships between various control objectives or actuators through predefined rules, state monitoring, or optimization algorithms. Contrasting with weight-based allocation methods, optimization-based coordinated control strategies formally define the complex coordination task of multiple chassis subsystems as a mathematical optimization problem. Hierarchical control architectures, due to their inherent modular characteristics and effective management capability for complex system control tasks, are also widely used in vehicles' three-dimensional coordinated control and are often deeply integrated with various optimization algorithms. [PERSON] et al. [20] proposed a modularly integrated longitudinal, lateral, and vertical vehicle stability control strategy.