Enhancement of BP Neural Network PID Control Algorithm through the Utilization of the Artificial Bee Colony Algorithm

由于传统PID控制器面临参数调整繁琐、实时调适滞后、工况适应局限等挑战，本研究提出了一种以人工蜂群算法(Artificial Bee Colony, ABC)为核心的BP神经网络优化策略。研究表明，ABC算法对蜜蜂觅食行为的模拟机制，显著增强了BP神经网络在参数空间中的探索能力，有效维持了粒子群的多样性特征，构建起了高效的PID控制参数自适应调节框架，成功地克服了因参数失配而引发的控制效能递减难题，确保了控制系统在复杂工况下的稳定性与鲁棒性，为提升控制系统整体性能提供了坚实保障与有力支撑。ABC算法在提升BP神经网络性能上卓越可靠，为PID控制革新提供了依据与范式。Due to the challenges associated with traditional PID controllers, such as the complexity of parameter tuning, delayed real-time adjustment, and limitations in adapting to varying operating conditions, this study proposes a BP neural network optimization strategy based on the Artificial Bee Colony (ABC) algorithm. The research demonstrates that the ABC algorithm, which simulates the foraging behavior of bees, significantly enhances the BP neural network’s exploration capabilities in the parameter space. This approach effectively maintains the diversity of the particle swarm, establishing an efficient self-adaptive adjustment framework for PID control parameters. As a result, it successfully overcomes the performance degradation caused by parameter mismatch, ensuring the stability and robustness of the control system under complex operating conditions. This study provides a solid foundation and strong support for improving the overall performance of the control system. The ABC algorithm proves to be highly reliable in enhancing BP neural network performance and offers both a theoretical basis and a paradigm for the innovation of PID control.