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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.