This paper investigates new achievements in global chassis control, involving Active Front steering (AFS) and Direct Yaw Control (DYC), to improve the overall vehicle performance, i.e. the vehicle manoeuvrability, lateral stability and rollover avoidance. Two multilayer control architectures, each formed by three hierarchical layers, are developed, validated and compared. The lower layer represents the actuators implemented into the vehicle which generate their control inputs based on the orders sent from the middle layer. The middle layer is the control layer which is responsible to generate the control inputs that minimise the errors between the desired and actual vehicle yaw rate, side-slip angle and roll angle, regardless of the driving situation. The control layer is the main difference of the proposed architectures, where a centralised and a decentralised controllers are developed. In the centralised architecture, the novelty is that one single Multi-Input-Multi-Output MIMO optimal controller generates the optimal additive steering angle provided by the AFS and the optimal differential braking provided by the DYC to minimise -at once- all the vehicle state errors . The optimal ${\mathcal{H}}_{\mathrm{\infty }}$ control technique based on offline Linear Matrix Inequality optimal solutions, in the framework of Linear-Parameter-Varying systems, is applied to synthesise the controller. In the decentralised architecture, a heuristic solution is proposed by decoupling the control problem where the Super-Twisting Sliding Mode (STSM) technique is applied to derive the AFS control input which minimises only the errors on the yaw rate, and the roll angle. Similarly, the DYC control input is privileged to minimise only the error on the side-slip angle. The higher layer of both architectures is the decision making layer which instantly monitors two criteria laying on lateral stability and rollover risks. Then, it generates two weighted parameters which adapt the controller(s) performance(s) according to the driving conditions in order to improve the vehicle's manoeuvrability, lateral stability and rollover avoidance. Both control architectures are tested and validated on the professional simulator ‘SCANeR Studio’. Simulation shows that both architectures are relevant to the global chassis control. The centralised one is optimal, complex and overall closed-loop stability is guaranteed, while the decentralised one does not guarantee the overall closed-loop stability, but it is intuitive, simple and robust.

本文研究了全球底盘控制方面的新成果，包括主动前转向（AFS）和直接偏航控制（DYC），以提高车辆的整体性能，即车辆的机动性、横向稳定性和防侧翻。开发、验证和比较了两个多层控制体系结构，每个体系结构由三个层次结构组成。下层代表实现在车辆中的执行器，它们根据中间层发送的命令生成控制输入。中间层是控制层，负责生成控制输入，以最小化期望和实际车辆偏航率、侧滑角和侧倾角之间的误差，无论驾驶情况如何。控制层是所提出架构的主要区别，其中开发了集中式和分散式控制器。在集中式架构中，新颖之处在于一个单一的多输入多输出 MIMO 优化控制器生成由 AFS 提供的最佳附加转向角和由 DYC 提供的最佳差动制动，以立即最小化所有车辆状态错误。最优的${\mathcal{H}}_{\mathrm{\infty }}$将基于离线线性矩阵不等式最优解的控制技术，在线性参数变系统的框架内，应用于控制器的综合。在分散式架构中，通过解耦控制问题提出了一种启发式解决方案，其中应用了超扭转滑动模式 (STSM) 技术来导出 AFS 控制输入，该输入仅最小化偏航率和侧倾角的误差。类似地，DYC 控制输入的特权是仅最小化侧滑角的误差。两种架构的较高层是决策层，它即时监控横向稳定性和侧翻风险的两个标准。然后，它生成两个加权参数，根据驾驶条件调整控制器的性能，以改善车辆' s 机动性、横向稳定性和防侧翻。两种控制架构都在专业模拟器“SCANeR Studio”上进行了测试和验证。仿真表明，这两种架构都与全局底盘控制相关。集中式最优，复杂，保证整体闭环稳定性，分散式不保证整体闭环稳定性，但直观、简单、鲁棒。