The present disclosure discloses an active suspension control method under vehicle-mounted visual perception preview. It uses a binocular camera combined with multiple visual perception algorithms, and monitors in real time the road surface conditions ahead of the vehicle. By accurately capturing and analyzing the road surface information, based on robust control theory and Lyapunov theory, it designs a matching preview H.sub. controller. The vehicle can effectively reduce bumps and vibrations by timely adjusting the suspension system, providing passengers with a more stable and smooth driving experience. The present disclosure uses a machine vision method to sense in advance the road surface information ahead, improving the time lag problem in the traditional suspension control method, thereby significantly improving the vehicle safety and ride comfort.

Apparatuses and systems for monitoring wheel alignment and/or for controlling vehicle suspension settings to adjust alignment. Described herein are alignment monitoring apparatuses for determining wheel alignment (e.g., camber, castor and/or toe). Also described herein are alignment adjusting or control apparatuses for adjusting one or more of camber, caster and/or toe.

An intelligent collaborative operation control strategy for an electro-hydraulic suspension system of a high-horsepower tractor is provided, including: S1, establishing an operation task database; S2, collecting information of the electro-hydraulic suspension system during an operation of the high-horsepower tractor in real time as real-time data; S3, preprocessing the real-time data; S4, analyzing a dataset and generating a preliminary control strategy for the electro-hydraulic suspension system based on parameters in the operation task database; S5, performing data interaction and collaborative control with operation units; S6, identifying deviations between operation status data and expected control parameters; S7, dynamically adjusting the preliminary control strategy. Through implementation of sensing technology and machine learning algorithms, real-time and precise adjustment of operation parameters for the suspension system of the tractor is achieved, significantly enhancing a level of automation and intelligence in operations, thereby effectively improving efficiency and economic benefits of agricultural operations.