Nowadays, vehicles are being fitted with systems that improve their maneuverability, stability, and comfort in order to reduce the number of accidents. Improving these aspects is of particular interest thanks to the incorporation of autonomous vehicles onto the roads. The knowledge of vehicle sideslip and roll angles, which are among the main causes of road accidents, is necessary for a proper design of a lateral stability and roll-over controller system. The problem is that these two variables cannot be measured directly through sensors installed in current series production vehicles due to their high costs. For this reason, their estimation is fundamental. In addition, there is a time delay in the relaying of information between the different vehicle systems, such as, sensors, actuators and controllers, among others. This paper presents the design of an \({H}_{\infty }\)-based observer that simultaneously estimates both the sideslip angle and the roll angle of a vehicle for a networked control system, with networked transmission delay based on an event-triggered communication scheme combined with neural networks (NN). To deal with the vehicle nonlinearities, NN and linear-parameter-varying techniques are considered alongside uncertainties in parameters. Both simulation and experimental results are carried out to prove the performance of observer design.

如今，车辆正在安装可改善其机动性，稳定性和舒适性的系统，以减少事故的发生。由于将自动驾驶车辆并入道路，因此改善这些方面尤为重要。车辆侧滑和侧倾角的知识是道路交通事故的主要原因，这对于横向稳定性和侧翻控制器系统的正确设计是必不可少的。问题在于，这两个变量由于成本高昂而无法通过安装在当前批量生产车辆中的传感器直接测量。因此，它们的估计至关重要。另外，在诸如传感器，致动器和控制器之类的不同车辆系统之间的信息中继中存在时间延迟。基于\（{H} _ {\ infty} \）的观察器，它同时为网络控制系统估算车辆的侧滑角和侧倾角，同时基于事件触发的通信方案结合神经网络，实现网络传输延迟网络（NN）。为了解决车辆的非线性问题，除了参数不确定性外，还考虑了神经网络和线性参数变化技术。仿真和实验结果均进行了验证，以证明观测器设计的性能。