基于BLF多约束反演控制的电动收获机分布式驱动控制方法研究

Distributed Drive Control Method of Electric Harvester Based on BLF Multi-constraint Inversion Control

摘要:

针对电动收获机分布式驱动面对喂入扰动时控制的实时性、准确性和稳定性问题，以电动收获机为平台，建立部件负载模型，结合部件转速约束，设计BLF（Barrier Lyapunov function）多约束反演控制算法；利用AMEsim与Matlab/Simulink平台搭建联合仿真模型验证控制算法性能。结果表明，与传统PID控制相比本文算法在面对不同喂入扰动时，割台电机、输送电机、脱粒滚筒电机转速控制超调量分别减少4%、34%、92%；调节时间分别减少34%、54%、72%；各部件电机最大转速误差上，本文算法维持在预设转速的3%内，而PID则在8%内。验证了本文算法具有控制迅速、转速跟踪误差小、抗扰能力强的特点，能将部件转速约束在边界内，可实现不同喂入扰动下鲁棒控制，有利于稳定整机作业质量。

Abstract:

Current combine harvesters faced challenges such as low levels of automation, environmental pollution, high costs, and complex operations, requiring operators to possess advanced driving skills. During harvesting, operators needed to adjust the harvester’s parameters based on prevailing field conditions. However, reliance on operators’ subjective experience for assessing field conditions and adjusting harvesting parameters impeded the intelligent development of combine harvesters. Consequently, electric harvesters, with their promising prospects for intelligent development and variable transmission ratios of working components, emerged as a new trend.The issues of real-time control, accuracy, and stability in the distributed drive system of electric harvesters under feed-in disturbances were primarily addressed. Using an electric harvester as the platform, a component load model was established, and a multiconstraint inverse control algorithm based on the barrier Lyapunov function (BLF) was designed. Joint simulation models were developed by using the AMEsim and Matlab/Simulink platforms to validate the performance of the control algorithm.The results demonstrated that compared with traditional PID control, this algorithm significantly reduced overshoot in the cutter platform, conveyor, and threshing drum motor speed control by 4%, 34%, 92%, respectively, when facing various feedin disturbances. The adjustment times were reduced by 34%, 54%, 72%, respectively. The maximum motor speed error for each component was maintained within 3% of the preset speed, and that was 8% for PID control. This validated the algorithm’s rapid control response, minimal speed tracking error, and strong disturbance rejection capabilities. It effectively constrained component speeds within their boundaries, enabling robust control under different feeding disturbances and thereby contributing to the stability of the overall machine’s operational quality.