A motor control algorithm is proposed for a 6 wheel independent motor drive electric vehicle. An adaptive PD control is used to generate the direct yaw moment to compensate the yaw rate error. To calculate the yaw rat...
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ISBN:
(纸本)9788995003848
A motor control algorithm is proposed for a 6 wheel independent motor drive electric vehicle. An adaptive PD control is used to generate the direct yaw moment to compensate the yaw rate error. To calculate the yaw rate error, an imaginary steering angle is introduced with yaw rate gain. In order to evaluate the controlalgorithm, a ADAMS vehicle model is developed and ADAMS and MATLAB co-simulation is performed using the motor control algorithm. It is found that the vehicle performance can be improved by the independent motorcontrol.
This paper examines the reliability of the Software-in-the-Loop (SiL) testing environment precisely to test embedded system algorithms. The integrated algorithms system was developed around a BLDC motor based on a rea...
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ISBN:
(纸本)9781728133300
This paper examines the reliability of the Software-in-the-Loop (SiL) testing environment precisely to test embedded system algorithms. The integrated algorithms system was developed around a BLDC motor based on a real-time operating system. The main features and functions of the controlalgorithms developed and implemented in an embedded system is to control the BLDC motor speed. The integration of the controlalgorithms of a BLDC motor model from the Model-in-the-Loop (MiL) process into the SiL process comes precisely from the increasing need to reduce the amount of prototype testing and to check the resistance of controlalgorithms to conditions errors that are not practical to test on the hardware. To prove the efficiency of the SIL strategy, three types of algorithms have been developed and implemented for controlling such an electric motor by applying optimization techniques based on closed-loop regulators. The code generated after these models is tested and validated through different test scenarios according to the source model, creating a test report after which we can validate the correctness of the code generated in the SiL process.
To address several challenges, including low efficiency, significant damage, and high costs, associated with the manual harvesting of Agaricus bisporus, in this study, a machine vision-based intelligent harvesting dev...
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To address several challenges, including low efficiency, significant damage, and high costs, associated with the manual harvesting of Agaricus bisporus, in this study, a machine vision-based intelligent harvesting device was designed according to its agronomic characteristics and morphological features. This device mainly comprised a frame, camera, truss-type robotic arm, flexible manipulator, and control system. The FES-YOLOv5s deep learning target detection model was used to accurately identify and locate Agaricus bisporus. The harvesting control system, using a Jetson Orin Nano as the main controller, adopted an S-curve acceleration and deceleration motor control algorithm. This algorithmcontrolled the robotic arm and the flexible manipulator to harvest Agaricus bisporus based on the identification and positioning results. To confirm the impact of vibration on the harvesting process, a stepper motor drive test was conducted using both trapezoidal and S-curve acceleration and deceleration motor control algorithms. The test results showed that the S-curve acceleration and deceleration motor control algorithm exhibited excellent performance in vibration reduction and repeat positioning accuracy. The recognition efficiency and harvesting effectiveness of the intelligent harvesting device were tested using recognition accuracy, harvesting success rate, and damage rate as evaluation metrics. The results showed that the Agaricus bisporus recognition algorithm achieved an average recognition accuracy of 96.72%, with an average missed detection rate of 2.13% and a false detection rate of 1.72%. The harvesting success rate of the intelligent harvesting device was 94.95%, with an average damage rate of 2.67% and an average harvesting yield rate of 87.38%. These results meet the requirements for the intelligent harvesting of Agaricus bisporus and provide insight into the development of intelligent harvesting robots in the industrial production of Agaricus bisporus.
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