Hilly and mountainous areas serve as important production bases for grain, oil, sugar and distinctive agricultural products in China. However, restricted by complex topography and landforms, the level of agricultural mechanization in these regions is roughly 20% lower than the national average, posing a huge challenge to the promotion of agricultural mechanization. At present, agricultural production in hilly and mountainous areas still relies mainly on manual labor, which is inconsistent with the actual demand due to population aging and the loss of young and middle-aged labor force. As a power platform in hilly and mountainous areas, electric tracked vehicles feature excellent trafficability and environmental friendliness. Nevertheless, the existing motor control theories are difficult to meet the operational requirements of tracked vehicles in such scenarios. Specifically, permanent magnet synchronous motors (PMSM), which is viewed as power core of agricultural tracked vehicle, exhibit unsatisfactory control performance in the zero and low-speed range, making it difficult for agricultural tracked vehicles to achieve precise operation in hilly and mountainous areas. To address this issue, a hybrid control strategy combining the high-frequency signal injection method and a nonlinear observer is proposed based on existing research foundations and the actual operating conditions of hilly and mountainous regions. Through a systematic analysis of the impacts of inverter dead time and nonlinear factors on the motor operating characteristics, a corresponding compensation scheme is designed to effectively attenuate the adverse effects caused by the aforementioned factors. The mathematical model of a PMSM is established, and the three-phase current waveforms of the motor under the non-compensation scheme and the hybrid compensation strategy for dead time and nonlinear factors are compared. The results demonstrate that the dead-time effect and chattering phenomenon in the motor current are significantly suppressed after compensation, and the stability of the current waveform is greatly improved. An experimental platform is built, and motor starting and operating condition switching tests are carried out under set load conditions. The experimental results verify the effectiveness of the proposed hybrid control strategy; the system can maintain stable operation even during the switching process between the high-frequency signal injection method and the flux observer. Data comparison reveals that compared with non-compensation control, the total harmonic distortion (THD) of the motor current is reduced by 6.34 percentage points under no-load conditions and by 5.26 percentage points under load conditions after adopting the hybrid compensation strategy. Meanwhile, a comparative test of super-twisting sliding mode active disturbance rejection control shows that the control strategy can basically eliminate the speed overshoot during motor operation and further enhance the control precision of the system. It should be acknowledged that satisfactory progress has been made in the present research. However, limited by experimental conditions, only simulations and tests under partial operating conditions have been completed. Further optimization of the switching strategy and improvement of the field test scheme are required in future work, so as to fully verify the robustness of the hybrid algorithm.

Field roads have been the most important transportation ways for commercial grain and agricultural products in hilly and mountainous regions in China. Especially, the youth labor can rapidly migrate from the rural to the urban areas against ever-increasing urbanization. Current manual production cannot fully meet the large-scale and precision agriculture in recent years, owing to the protracted yield cycle and low rate of return. Therefore, it is urgent to implement the mechanization and intelligent agriculture for the national food security in hilly and mountainous regions. Among them, the autonomous and safe operation of intelligent machinery can be critical to navigate or circumvent obstacles on field roads. However, the conventional machine vision cannot accurately and rapidly construct the visual guidance lines in such terrains. In this study, the extraction was proposed to enhance the visual guidance and obstacle avoidance using millimeter-wave radar and vision fusion. The state information was also detected from the target objects on the field road. The specific steps were as follow. Preprocessing techniques were employed to filter the portion of the radar object data. A multi-target tracking was utilized to eliminate the interference data for the continuous tracking of dynamic objects. Accurate radar object data was then obtained for subsequent data fusion. A semantic segmentation network was created using Deeplabv3+, and then leveraged a dataset of the adjacent field roads. The millimeter-wave radar and vision data were synchronized in both time and space via the timestamp alignment and least squares-based coordinate transformation. Extraction approach was then established for the visual guidance lines, particularly for the scenarios where dynamic target state was both available and unavailable. A series of experiments were carried out to validate the extraction of visual guide line. The average errors of detection were ranged from 1.60 to 9.20 pixels at the real road midpoints in the scenes without dynamic targets. Moreover, the visual guidance lines were successfully extracted for the obstacle avoidance proactively when encountering dynamic targets. Evidently, the integrated approach was effectively overcome the constraints of traditional machine vision, thereby enhancing the safety and reliability of machinery operation in hilly and mountainous terrains. Meanwhile, the efficacy was depended mainly on the precise projection of millimeter-wave radar data onto the visual plane. Simultaneously, the variables were markedly introduced the positional discrepancies, such as the posture of platform (including pitch and roll). Furthermore, the driveability of the operational area was effectively evaluated in the forthcoming regions. At the same time, the real-world conditions were more complex in the frequent presence of common elements like livestock, vehicles, and tricycles. The resilience of approach can be expected to improve the radar data processing for the detection of static objects. Moreover, positional deviations can also be mitigated from mechanical movements. As such, the precise visual guidance lines were established in the hilly and mountainous terrains. Therefore, the radar data processing can be focused on the dataset expanding and position deviation that induced by various factors. The intelligent level of agricultural machinery can be improved in hilly and mountainous area. The finding is of great significance to consolidate national food security.