Design of an Adaptive Algorithm for Feeding Volume–Traveling Speed Coupling Systems of Rice Harvesters in Southern China

Abstract

We developed an adaptive algorithm to reduce rice loss in harvesting, promote threshing and improve the quality and efficiency of small- and medium-sized rice harvesters operating in southern China’s hilly and mountainous areas. Using a fuzzy PID control algorithm, the harvester adapts to the rice harvesting conditions in southern China, and monitors rice feed volume changes and instantly adjust the traveling speed to optimize feed volume levels and threshing quality. We compared and analyzed the algorithm and the traditional PID control regulation effect in the simulation experiment. The algorithm had a quicker response speed and stable accuracy. In the field trial, the average error rate was 3.4%, and the maximum error rate was 5.1%, with most data points centered around the ideal feeding rate of 3.2 kg/s. Our results showed that the algorithm’s stability, accuracy, and real-time performance met the threshing loss reduction requirements of southern China’s rice harvesting operations.

An Improved Deviation Coupling Control Method for Speed Synchronization of Multi-Motor Systems

“…Each single-axis control module comprises controllers and motors, as illustrated in Figure 5, where the PMSM adopts a vector control strategy with id = 0. Within the controller part, based on relevant studies [27][28][29] and combined with several simulation The traditional PID control formula is as follows:…”

“…Each single-axis control module comprises controllers and motors, as illustrated in Figure 5, where the PMSM adopts a vector control strategy with i d = 0. Within the controller part, based on relevant studies [27][28][29] and combined with several simulation results, the parameters K P , K I , and K D are configured as 2.5, 0.3, and 0, respectively. The fuzzy theory domain of deviation e and the deviation rate of change ec are specified as [-10, 10], while the fuzzy theory domain of ∆K P and ∆K I are set to [-1, 1].…”

Special Issue on the Engineering of Smart Agriculture

“…The diverse range of matters covered in the monograph emphasizes the multidisciplinary nature of modern agricultural research and ongoing efforts to improve yield, quality, and sustainability. This monograph provides a comprehensive overview of the latest research and technological advances in the field of agriculture, offering valuable insights and practical solutions from image processing methods for precise area measurement [1] to the design of adaptive algorithms for efficient rice harvesting [2]. It also analyzes the generation of meteorological sequences for simulating the growth of biological systems [3] and analytical methods for assessing forklift stability [4].…”

Design and Testing of Bionic-Feature-Based 3D-Printed Flexible End-Effectors for Picking Horn Peppers

“…This model could assist pepper robots in maintaining robust perception in unstructured environments to help in picking the fruits [15]. A number of algorithms related to deep learning and adaptive tuning are driving the development of smart agricultural machinery [16][17][18][19].…”