You can concentrate instead on your driver's primary tasks. With the legacy Windows NT model, you do not have to be concerned about PnP and power because kernel-mode services operate in an environment that is completely independent from PnP and power management. Our recommendation is that you use KMDF, especially if you are already familiar with it.
If you want your driver to be completely independent from PnP and power management, use the legacy Windows NT model. Note In the very rare case that you need to write a software driver that is aware of PnP or power events, and your driver needs access to data that is not available through KMDF, you must use WDM. For help with choosing a model for a file system filter driver, see File system driver samples.
Note that file system drivers can be quite complex and may require knowledge of advanced concepts for driver development. For help with choosing a model for a file system filter driver, see File system minifilter drivers and File system filter drivers. For help choosing a model for a file system minifilter driver, see File System Minifilter Drivers. Skip to main content. This browser is no longer supported. Download Microsoft Edge More info. Contents Exit focus mode. Please rate your experience Yes No.
Any additional feedback? Submit and view feedback for This product This page. View all page feedback. It was difficult to test the relationship between the output torque of the motor and the effective input of the accelerator pedal, because of the lack of test equipment; thus, dynamic items were ignored.
This study mainly investigated the driving behaviour of the driver. Therefore, the transmission system was regarded as rigid, regardless of the torque and energy loss of the transmission process. The HRTE electric racing transmission system mainly included a transmission chain, antiskid differential, drive shaft, and wheel. Among them, the transmission chain played the role of transmission and main reducer in traditional vehicles with an internal combustion engine.
According to the number of teeth of the small and large sprockets, the transmission ratio should be 3. The antislip differential only had a torque distribution function. This paper focused on the torque input and output characteristics of the power drive system. The motor output torque and the driving torque acting on the drive shaft satisfied the relationship in 7 , which is the transmission system model:. This system facilitated adjustment of the front and rear wheel braking force ratio.
In the simplified prediction optimisation model of artificial neural network driver direction control, movement of the vehicle in the prediction time was assumed to be uniform acceleration movement [ 15 ], even though this is not the case in reality.
In previous work [ 23 ], it was assumed that during the period of prediction time, the movement could be regarded as uniform acceleration, provided the speed did not change dramatically. On this basis, a PID control strategy could be used to achieve longitudinal velocity control [ 22 , 24 ].
The control strategy is depicted in Figure A longitudinal speed control simulation system for the HRTE electric racer was built based on the Simulink-Carsim simulation platform, which was the same as the system removing and in Figure The expected speed tracking results are shown in Figure 15 a , which indicates that the tracking response time of the simple PID was slightly longer and did not meet the fast response characteristics of a racing driver.
Thus, we built a longitudinal velocity control strategy combined with feedforward-PID feedback, as shown in Figure The feedback, and , could be obtained by analysis of the control system.
The simulation results are presented in Figure 15 b. The response time and overshoot of the improved model were both less than that of model based only on PID feedback under the same step signal.
Therefore, a longitudinal speed control strategy combined with feedforward-PID feedback could be effectively used in the development of an electric racing chassis control system. Thus, we should establish a driver model of direction and longitudinal speed integrated control. In addition, changes in the vehicle speed would often lead to changes in the vehicle model parameters, and this requires the corresponding control parameters in the driver control model to be changed.
Based on the above content, we established the driver direction and longitudinal speed integrated control model, as shown in Figure It is known that the coupling between the parameters representing the lateral and longitudinal characteristics of the vehicle affects the parameters of the direction control model or the longitudinal speed control model [ 25 , 26 ].
Owing to the difficulty in controlling the coupling system, it was desirable to decouple the coupling system into multiple independent single-input and single-output subsystems by realising that a certain output was only controlled by a certain input [ 27 ]. Researchers have previously pointed out [ 18 ] that in a shorter prediction time interval, because of smaller changes in the vehicle status, the influence of the steering wheel on the longitudinal characteristics was small and could be ignored.
The longitudinal velocity of the vehicle had a large effect on the lateral characteristics of the vehicle. The lateral gain could be fitted to the function of longitudinal steady-state gain on the steering wheel angle. The above-mentioned parameter identification method was used to conduct an equivalent 2-DOF vehicle model parameter identification. These identification results were transformed into the lateral acceleration on the steering wheel steady-state gain, the functional relationship between the vehicle dynamic parameters, and , and longitudinal velocity.
The fitting results are shown in Figure Therefore, a compensation gain was added to correct the result, as shown in Figure 17 d. Finally, our aim was to use the idea of ensuring local optimal optimisation to achieve the global optimum as far as possible to enable the vehicle on a local path to attain a high speed to suit the characteristics of racing. This process was repeated to achieve the shortest travel time of the vehicle for the overall path. For this research, we chose the durable track located in Xiangyang, China, which is one of the most complex racing tracks in China, as a typical working condition.
The simulation results are shown in Figure The electric racing car was rapidly accelerated from stationary to the highest speed and then decelerated accelerated as the curvature increased decreased until the mission was completed.
The result in Figure 18 a indicates that the model could track the large curvature path well, and the tracking deviation was very small. In Figure 18 d , it can be seen that, at the larger curvature, the required steering wheel angle calculated theoretically exceeded the maximum value of the HRTE steering system; as a result, the saturation value was selected as the actual steering wheel input.
The simulation results of the durability track in Xiangyang confirmed that the final model of the racing driver could successfully complete the tracking task of a track with a large amount of curvature. The model could be further used for the development of an electric racing chassis control system.
Based on the preview-follower theory, the improved preview point search algorithm is used to establish a driver direction control model suitable for racing cars.
The tracking simulation of an figure-eight-shaped continuously looping track was used to prove that the driver direction control model can successfully track a path with a large amount of curvature. On the basis of the power drive system model, the driver longitudinal speed control model was established by implementing feedforward-PID feedback. Simulation under the same step signal proved that the longitudinal velocity control model has a smaller response time and overshoot, which is more accurate than the model without improvement.
The driver model presented in this paper was constructed by combining the two driver models and a decoupling system, after which this combination was integrated with the fastest speed control driver model, which achieves the shortest global travel time. Simulation of the durable track in Xiangyang, China, proved that the final model is excellent for completing a track with considerable curvature. Thus, the model can be effectively used in the development of an electric racing chassis control system.
The authors declare that there are no conflicts of interest regarding the publication of this paper. This is an open access article distributed under the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Article of the Year Award: Outstanding research contributions of , as selected by our Chief Editors.
Read the winning articles. Journal overview. Special Issues. Academic Editor: Onome E. Received 19 Oct Accepted 01 Apr Published 08 May Introduction The study of driver behaviour is an important part of vehicle dynamics and control research. Figure 1. Figure 2. Figure 3. Figure 4. Figure 5. Identification results of the Carsim vehicle model.
Figure 6. Figure 7. Method of tracking a path with a large amount of curvature. Figure 8. Simulation result of improved prediction point search algorithm. This system combines mechanical rear steering with a fully independent rear suspension. This feature will be the specification for all Dallas tandem axle aerials going forward.
A Harrison Model The aerial is a rear-mount, heavy-duty, foot stick constructed of steel with a steel 1,GPM waterway and Stokes box on the right side.
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