Research on Motor Optimization Method for Seeder System Based on IWMA-RBF

To address the issues of nonlinearity and strong coupling characteristics in seeder motor systems, and the insufficient dynamic response and weak anti-disturbance capability of traditional control methods. This study proposes an active disturbance rejection control ADRC method based on an improved whale migration algorithm IWMA optimized radial basis function RBF neural network. First, building upon the whale migration algorithm WMA , a leader proportion dynamic adjustment mechanism is introduced to optimize the population structure through a nonlinear attenuation function. Second, the global search capability is enhanced by integrating a hybrid guidance strategy and Lévy flight perturbation mechanism, thereby constructing the IWMA algorithm with high-efficiency optimization performance. Third, the IWMA is combined with the RBF neural net-work to collaboratively optimize the RBF network’s center vectors, kernel width, and output weights, forming an IWMA-RBF parameter self-tuning framework. Furthermore, an IWMA-RBF-based ADRC controller is designed. The dynamic compensation capability for disturbances such as sudden soil resistance changes is strengthened through an improved extended state observer ESO , and multi-objective optimization of nonlinear state error feedback NLSEF gain parameters is achieved using the IWMA-RBF algorithm. Simulation experiments demonstrate that compared to traditional PID, ADRC, and RBF-ADRC controllers, the IWMA-RBF-ADRC controller significantly improves control accuracy, response speed, and robustness in the motor control system. Field seeding trials verify the superior stability and response speed of this method in complex seeding environments, providing effective technical support for practical applications.