# 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
[105], in their roll controller design for active suspension, also utilized fuzzy control to adaptively adjust the parameters of a proportional-integral (PI) controller based on real-time roll angle error and its rate of change, to cope with varying operating conditions and enhance the adaptability of the control system. [PERSON] et al. [106] applied Fuzzy Logic Control to coordinate AFS and ASS. In their scheme, the output of the fuzzy controller served as the reference input for the lower-level suspension normal force controller. Simulation results verified that this integrated chassis control scheme achieved a more effective and comprehensive improvement in overall vehicle performance compared to independent subsystem control. MPC is also finding an increasing application in lateral-vertical coordinated control, being capable of synergistically optimizing suspension systems, steering systems, and even DDEV drive torques within a single framework. For example, [PERSON] et al. [107] proposed an ideal integrated control architecture for AFS, ASS, and DYC, structured with high-level and low-level controllers. In the high-level controller, they utilized an MPC algorithm. This algorithm determines the constrained optimal control targets, including the four-wheel longitudinal forces, front tire lateral forces, and a supplementary roll moment. Subsequently, the low-level controller is responsible for implementation. It uses an optimal distribution algorithm for the actuators and tire slip controllers to execute the control actions computed by the high-level MPC. Furthermore, MAS theory and learning-based control methods also provide new perspectives for solving such complex coordination problems. [PERSON] et al. [86] adopted a distributed MAS architecture, wherein AFS and ASS are treated as independent agents, each employing a Multi-constraint Distributed Model Predictive Control (MDMPC) strategy. This architecture allows AFS and ASS agents to work cooperatively through information exchange while handling multiple system constraints, aiming to jointly optimize the vehicle's lateral and vertical dynamic performance, ultimately enhancing the vehicle's comprehensive handling performance, safety, and comfort. Its effectiveness has been validated through simulation analysis and Hardware-in-the-Loop (HIL) testing. In terms of learning-based control, [PERSON] et al. [108] applied a Generalized Regression Neural Network (GRNN) to construct inverse system models of AFS and Hydraulic Power Suspension (HPS). The study first analyzed the coupling characteristics between AFS and HPS, and then utilized the GRNN to learn and identify the inverse dynamics characteristics of these two subsystems. The inputs to the GRNN are the state variables of the AFS and HPS systems, and its outputs directly serve as the desired steering commands for AFS and the desired actuator control commands for HPS. Through this decoupling control strategy based on the GRNN inverse system model, effective decoupling and coordinated control of the vehicle's lateral (dominated by AFS) and vertical (dominated by HPS) motions were ultimately achieved. Prospectively, research on coordinated lateral and vertical control for DDEV chassis will deepen towards a more profound understanding of coupling mechanisms, more comprehensive performance optimization, and engineering implementation closer to practical applications. Further research is needed to provide a more solid foundation for the design of coordinated control strategies. A primary requirement is to deepen the understanding of lateral and vertical coupling mechanisms, especially under extreme and highly dynamic conditions. Additionally, there is a need to develop advanced models. This includes creating online parameter identification methods and high-fidelity coupled dynamics models that can accurately capture tires' nonlinear characteristics, suspension's dynamic responses, and complex vehicle body attitude changes.