# Vehicle Rollover Warning and Control Based on Attitude Detection and Fuzzy PID
## 1 Introduction
At this stage, the research on wire-controlled braking technology has not yet fully met the requirements of EHB owing to the lack of stable control of pure motor braking. This study entailed the use of an electric power-assisted electrohydraulic line control actuation system for anti-rollover auxiliary braking, based on the hydraulic braking system of a forest fire truck, using an electric motor as a power-assisted device and a plunger pump high-pressure accumulator to apply oil pressure directly to the brake wheel cylinder to achieve brake pressure control. Many control algorithms have been proposed to control the anti-rollover of vehicles more accurately, such as linear quadratic optimal control, sliding mode control, proportional-integral-derivative (PID) algorithms, model predictive control (MPC), robust control, and differential flatness theory [34; 35; 36; 37]. Among them, linear quadratic optimal control has been applied to design the controller based on the system state space, and the accuracy of the control unit is an extremely complex nonlinear dynamical system that is not within the scope of this study. Sliding mode control is highly resistant to disturbances; however, additional jitter is generated during the sliding process owing to system inertia. Predictive control is yet to be solved for MPC of nonlinear, uncertain, and time-varying systems. Generally, robust control systems do not operate in an optimal state and are less stable. Differential flat theory has been commonly applied to the trajectory planning of vehicles. To improve the control performance and robustness of the traditional PID algorithm, scholars at home and abroad have proposed targeted controllers in combination with their research objects. [PERSON] et al. [36] employed a differential braking control strategy on the basis of a flexible PID control algorithm to optimally compensate for the vehicle transverse sway moment and effectively improve the vehicle rollover stability. [PERSON] et al. [38] used a PID control algorithm to solve the optimal transverse moment for stable vehicle driving and selected the outer wheel of the front axle of the vehicle as the braking wheel to realize anti-rollover control of differential braking. [PERSON] [2] designed a dual-input fuzzy controller to control a hydraulic stabilizer bar, which effectively decreased the rollover index of the vehicle and improved its stability and driving safety. By comparing the applications of the fuzzy-PID and sliding film control strategies, [PERSON] et al. [39] and [PERSON] et al. [40] proposed a front and rear axle braking force distribution method, designed a sliding film controller that could improve system stability, and proposed a braking coordination control strategy that combined regenerative braking and anti-lock braking. [PERSON] [41] and other scholars discussed the path tracking performance of a PID controller in empirical rectification method, particle swarm algorithm, and BAS, respectively, and the results showed that the PID control algorithm has strong credibility in robot modeling as well as path tracking function. The use of deep learning for visual tracking and velocity detection of UAVs was also proposed [42]. [PERSON] et al. [43] proposed a segmented optimal PID, which effectively improved the conflict between regulation performance and damping characteristics of hydropower systems. [PERSON] [44] combined intelligent PID and a PD-feedforward controller, which has the advantage of not requiring an exact model and improving the immunity of the controller to disturbances. [PERSON] et al. [45] proposed a fuzzy fractional-order PID controller, which was shown to have good control performance and immunity to disturbances. [PERSON] et al. [46] developed an intelligent PID algorithm based on particle swarm optimization for automatic guided vehicles. [PERSON] et al. [47] designed a multi-objective particle swarm optimization algorithm and to verify its effectiveness, a PID control algorithm based on multi-objective control was proposed for comparison.