Browse Topic: Steering systems
A futuristic vehicle chassis rendered in precise detail using state-of-the-art CAD software like Blender, Autodesk Alias. The chassis itself is sleek, low-slung, and aerodynamic, constructed from advanced materials such as high-strength alloys or carbon-fibre composites. Its polished, brushed-metal finish not only exudes performance but also emphasizes the refined form and engineered details. Underneath this visually captivating structure, a sophisticated system of self-hydraulic jacks is seamlessly integrated. These jacks are situated adjacent to the four shock absorber mounts. These jacks are designed to lift the chassis specifically at the tyre areas, and the total vehicle, ensuring that underbody maintenance is efficient and that, in critical situations, vital adjustments or emergency lifts can be performed quickly and safely. The design also incorporates an intuitive control system where the necessary buttons are strategically placed to optimize driver convenience. Whether
Tippers transporting loose bulk cargo during prolonged descents are subject to two critical operational challenges: cargo displacement and rear axle lifting. Uncontrolled cargo movement, often involving loose aggregates or soil, arises due to gravitational forces and insufficient restraint systems. This phenomenon can lead to cabin damage, loss of control, and hazardous discharge of materials onto roadways. Simultaneously, load imbalances during descent can cause rear axle lift, increasing stress on the front steering axle, resulting in tire slippage and compromised maneuverability. This study proposes a dynamic control strategy that adjusts the tipper lift angle in real time to align with the descent angle of the road. By synchronizing the trailer bed angle with the slope of the terrain, the system minimizes cargo instability, maintains rear axle contact, and enhances braking performance, including engine and exhaust braking systems. Computational modelling is employed to assess the
This research addresses the issues of permanent - magnet synchronous motor parameter matching and sudden - load compensation in four - wheel independent steering systems and proposes a composite control strategy. By analyzing their dynamic characteristics, it is found that traditional rotational inertia identification methods and existing load observers have deficiencies. The research uses the gradient correction algorithm to construct an online rotational inertia identification model, achieving real - time parameter identification with the characteristics of adjustable parameters and low computational complexity. At the same time, a load observer is designed based on the terminal sliding - mode control theory to solve the problem of observation lag in sudden - load conditions and provide timely compensation. Simulation and experimental results show that after a sudden load is applied, the angle - tracking error of this method is reduced to ±0.12°, the convergence time of rotational
Innovators at NASA Johnson Space Center have developed a programmable steering wheel called the Tri-Rotor, which allows an astronaut the ability to easily operate a vehicle on the surface of a planet or moon despite the limited dexterity of their spacesuit. This technology was originally conceived for the operation of a lunar terrain vehicle (LTV) to improve upon previous Apollo-era hand controllers. In re-evaluating the kinematics of the spacesuit, such as the rotatable wrist joint and the constant volume shoulder joint, engineers developed an enhanced and programmable hand controller that became the Tri-Rotor.
Autonomous vehicle motion planning and control are vital components of next-generation intelligent transportation systems. Recent advances in both data- and physical model-driven methods have improved driving performance, yet current technologies still fall short of achieving human-level driving in complex, dynamic traffic scenarios. Key challenges include developing safe, efficient, and human-like motion planning strategies that can adapt to unpredictable environments. Data-driven approaches leverage deep neural networks to learn from extensive datasets, offering promising avenues for intelligent decision-making. However, these methods face issues such as covariate shift in imitation learning and difficulties in designing robust reward functions. In contrast, conventional physical model-driven techniques use rigorous mathematical formulations to generate optimal trajectories and handle dynamic constraints. Hybrid Data- and Physical Model-Driven Safe and Intelligent Motion Planning and
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