Research on automobile four-wheel steering control system based on yaw angular velocity and centroid cornering angle

In order to improve the handling stability of four-wheel steering (4WS) cars, a two-degree-of-freedom 4WS vehicle dynamics model is constructed here, and the motion differential equation of the system model is established. Based on the quadratic optimal control theory, the optimal control of 4WS system is proposed in this paper. When running at low speed and high speed, through yaw rate feedback control, state feedback control, and optimal control, the 4WS cars are controlled based on yaw rate and centroid cornering angle with MATLAB/Simulink simulation. The result indicates that 4WS control based on the optimal control can improve the displacement of the cars. And, the optimal control of 4WS proposed in this paper can eliminate centroid cornering angle completely compared with other two traditional optimal control methods. Besides, the optimal control enjoys faster response speed and no overshoot happens. In conclusion, the optimal control method proposed in the paper represents better stability, moving track and stability, thereby further enhancing the handling property of cars.

Intelligent Rotary Steerable System, Coupled with an Instrumented Bit, Delivers Section Plan in Deepwater GOM Project

The challenging offshore drilling environment has increased the need for cost-effective operations to deliver accurate well placement, high borehole quality, and shoe-to-shoe drilling performance. As well construction complexity continues to develop, the need for an improved systems approach to delivering integrated performance is critical. Complex bottom hole assemblies (BHA) used in deepwater operations will include additional sensors and capabilities than in the past. These BHAs consist of multiple cutting structures (bit/reamer), gamma, resistivity, density, porosity, sonic, formation pressure testing/sampling capabilities, as well as drilling dynamics systems and onboard diagnostic sensors. Rock cutting structure design primarily relied on data capture at the surface. An instrumented sensor package within the drill bit provides dynamic measurements allowing for better understanding of BHA performance, creating a more efficient system for all drilling conditions. The addition of intelligent systems that monitor and control these complex BHAs, makes it possible to implement autonomous steering of directional drilling assemblies in the offshore environment. In the Deepwater Gulf of Mexico (GOM), this case study documents the introduction of a new automated drilling service and Intelligent Rotary Steerable System (iRSS) with an instrumented bit. Utilizing these complex BHAs, the system can provide real-time (RT) steering decisions automatically given the downhole tool configuration, planned well path, and RT sensor information received. The 6-3/4-inch nominal diameter system, coupled with the instrumented bit, successfully completed the first 5,400-foot (1,650m) section while enlarging the 8-1/2-inch (216mm) borehole to 9-7/8 inches (250mm). The system delivered a high-quality wellbore with low tortuosity and minimal vibration, while keeping to the planned well path. The system achieved all performance objectives and captured dynamic drilling responses for use in an additional applications. This fast sampling iRSS maintains continuous and faster steering control at high rates of penetration (ROP) providing accurate well path directional control. The system-matched polycrystalline diamond (PDC) bit is engineered to deliver greater side cutting efficiency with enhanced cutting structure improving the iRSS performance. Included within the bit is an instrumentation package that tracks drilling dynamics at the bit. The bit dynamics data is then used to improve bit designs and optimize drilling parameters.

Adaptive Preview based Control System for Unmanned Vehicle Path Tracking

The unmanned vehicle control technology is constantly updated. How to accurately track the path has become a key issue. For this reason, a path tracking control system for an unmanned vehicle is designed. The system control module solves the lateral and longitudinal control problems of the unmanned vehicle. The preview compensation controller corrects the deviation of the vehicle approaching the normal track. The steering control module changes the direction of the vehicle based on the motor command signal. In the software part, the kinematics model of the unmanned vehicle in the plane rectangular coordinate system is built. In this model, the steering geometric track is constructed based on the Stanley algorithm. Track tracking preview model can adjust the preview adaptively according to the lateral deviation and heading angle deviation of the vehicle and gets the adaptive preview point. The simulation results show that the maximum absolute value of preview deviation angle, the root mean square of preview deviation angle and the root mean square of tracking error are lower. The effect of path tracking control is better. The effect of path tracking control is less affected by vehicle speed and road environment.

Comparative Analysis of Steering Angle Prediction For Automated Object Using Deep Neural Network

Deep learning's rapid gains in automation are making it more popular in a variety of complex jobs. The self-driving object is an emerging technology that has the potential to transform the entire planet. The steering control of an automated item is critical to ensuring a safe and secure voyage. Consequently, in this study, we developed a methodology for predicting the steering angle only by looking at the front images of a vehicle. In addition, we used an Internet of Things-based system for collecting front images and steering angles. A Raspberry Pi (RP) camera is used in conjunction with a Raspberry Pi (RP) processing unit to capture images from vehicles, and the RP processing unit is used to collect the angles associated with each image. Apart from that, we've made use of deep learning-based algorithms such as VGG16, ResNet-152, DenseNet-201, and Nvidia's models, all of which were trained using labeled training data. Our models are End-to-End CNN models, which do not require extracting elements from data such as roads, lanes, or other objects before predicting steering angle. As a result of our comparative investigation, we can conclude that the Nvidia model's performance was satisfactory, with a Mean Squared Error (MSE) value of 0. But the Nvidia model outperforms the other pre-trained models, even though other models work well.<br>

Effect of Fixed and sEMG-Based Adaptive Shared Steering Control on Distracted Driver Behavior

Driver distraction is a well-known cause for traffic collisions worldwide. Studies have indicated that shared steering control, which actively provides haptic guidance torque on the steering wheel, effectively improves the performance of distracted drivers. Recently, adaptive shared steering control based on the forearm muscle activity of the driver has been developed, although its effect on distracted driver behavior remains unclear. To this end, a high-fidelity driving simulator experiment was conducted involving 18 participants performing double lane change tasks. The experimental conditions comprised two driver states: attentive and distracted. Under each condition, evaluations were performed on three types of haptic guidance: none (manual), fixed authority, and adaptive authority based on feedback from the forearm surface electromyography of the driver. Evaluation results indicated that, for both attentive and distracted drivers, haptic guidance with adaptive authority yielded lower driver workload and reduced lane departure risk than manual driving and fixed authority. Moreover, there was a tendency for distracted drivers to reduce grip strength on the steering wheel to follow the haptic guidance with fixed authority, resulting in a relatively shorter double lane change duration.