In order to solve the problems of traditional seeders, such as low seeding efficiency, tangled straw, a large amount of clay, easy ridge breakage in sowing operations, low qualified rate of high-speed seeding, and poor uniformity, this paper takes the pneumatic corn planter as the research object, the Beidou automatic driving unit as the carrier, the CAN (Controller Area Network) bus as the communication medium, and the double closed-loop fuzzy PID (proportion-integral-derivative) algorithm as the control core and designs a high-speed precision corn seeding control system based on Beidou navigation. It solves the problems that exist in traditional planters. In the bench experiment, the stability of the system is judged by comparing the motor control accuracy with ordinary PID and measuring the motor response time of the system at different speeds. The bench test results show that when the theoretical seeding speed is 0~34 r · min−1, the response time of the motor is shortened by 0.51 s compared with the ordinary PID control, and the error between the actual speed and the target value is less than 0.35%. The field experiment results show that when the unit runs for 5~13 km · h−1, the qualified rate of average planting spacing is greater than 95.81%, the reseeding rate is less than 10.11%, and the coefficient of variation is less than 16.72%, which complies with the standard of a corn sowing operation.
In the process of automobile electronic accelerator pedal development, it is a critical and challenging issue to evaluate the rationality and comfort of the design of an automotive electronic accelerator pedal. Many factors influence the comfort of the accelerator pedal, such as the spatial layout, dynamic characteristics, and matching characteristics of the accelerator pedal and vehicle motion. Since comfort evaluation requires a lot of manpower and material resources, this paper proposes a prediction model based on support vector machine regression algorithm (SVR) for comprehensive evaluation of Chinese passenger car pedals. It uses the known evaluation results to predict the unknown evaluated accelerator pedal parameters to achieve a more efficient and accurate assessment of electronic accelerator pedal design. Firstly, the article performs pedal position scans, pedal static, and road tests to give criteria, limitations, and recommended design ranges for pedal operation. Then, the vehicle performance was predicted and evaluated using a support vector machine prediction model and back propagation (BP) neural network prediction model for comparison. The correlation coefficient for the prediction results of the SVR model was 0.9024 with a mean square error was 0.00195. The correlation coefficient for the BP neural network model prediction result was 0.8694 with a mean square error of 0.00582. Finally, the simulation results were analyzed, and the results showed that support vector regression outperformed the neural network in predicting the validity and reliability of pedal design and performance evaluation, and can facilitate automotive pedal design and development.
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