# 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
It first receives the specific torque or angle commands allocated by the middle layer. Then, while considering the physical limitations of the actuators (such as motor response rate and maximum output torque), it precisely drives each actuator via its respective drive/braking and steering controllers. This process ultimately achieves coordinated control of the vehicle's longitudinal and lateral dynamics. Numerous advanced control theories have been successfully applied in the field of lateral and longitudinal coordinated control for DDEV chassis, including Linear Quadratic Regulator (LQR) [61], Sliding Mode Control (SMC) [62; 63], MPC [64; 65], feedforward-feedback control [66], and Multi-Agent Systems (MAS) theory [67; 68]. Among these, MPC has received particularly widespread application and in-depth research due to its unique advantages in handling multivariable systems, explicitly considering constraints, and utilizing predictive models to optimize future behavior [69; 70]. Researchers extensively utilize MPC to address the over-actuated characteristics of DDEVs and the complex coupling between lateral and longitudinal dynamics, aiming to enhance vehicle handling stability and path tracking accuracy. Specifically, [PERSON] et al. [71] proposed a hierarchical  
Figure 4: Hierarchical cooperative control of steering and driving/braking systems. control system based on Nonlinear Model Predictive Control (NMPC). The upper-layer NMPC controller was designed to address the challenges of nonlinearity, Multi-Input Multi-Output (MIMO) characteristics, and over-actuation in the coordinated control of lateral and longitudinal motion for four-wheel independent-drive electric vehicles. To achieve this, the controller utilized a nonlinear three-degree-of-freedom vehicle model and a nonlinear tire model. Furthermore, it innovatively employed the wheel slip ratio as a virtual control input. Through Hardware-in-the-Loop (HIL) simulation validation, the system was shown to effectively satisfy comprehensive control objectives for lateral and longitudinal motion under various driving conditions, such as constant speed, acceleration, deceleration cruising, and driving on low-adhesion surfaces. [PERSON] et al. [42] developed an AMPC framework. This framework innovatively includes three distinct components: a dynamic monitoring layer, an online optimization layer, and a bottom-layer control. First, the dynamic monitoring layer establishes a dynamic stability boundary based on the phase plane of the center of gravity's sideslip angle versus its rate of change. It also designs variable weighting factors accordingly. Second, the online optimization layer constructs an AMPC strategy with adaptive objective weights. This strategy is capable of adjusting the relative weights of handling and lateral stability in the objective function in real time, based on weight information from the monitoring layer. Finally, the bottom-layer control achieves a precise and decoupled allocation of the driver-requested driving force and the upper layer's calculated additional yaw moment. This is accomplished by optimizing the drive torque distribution error and tire utilization rate. Simulation results show that this strategy effectively enhances the vehicle's handling flexibility under normal operating conditions and significantly improves lateral stability under extreme operating conditions. MAS theory provides another effective approach for solving distributed coordinated control problems in complex large-scale systems. This theory provides an effective approach by decomposing complex global control problems into several simpler, independent subproblems. Each subproblem is then assigned to an individual agent for analysis and control. The decomposition process begins with a thorough analysis of the controlled object. This involves examining the interrelationships among its subsystems, their mechanisms of action, and their impact on overall system performance.