针对电动调节阀控制系统在实际生产过程中存在的非线性、多扰动等问题，提出一种基于改进蚁群算法优化单神经元PID（proportional integral derivative）的控制方法并将其应用于阀门开度控制中。该方法利用单神经元网络的自学习和自适应能力，实现PID控制参数的在线整定，并采用改进的蚁群优化算法优化单神经元PID中的学习速率和神经元比例系数，有效克服了单神经元PID中的学习速率和神经元比例系数因经验设定而无法达到预期控制效果的不足。仿真对比结果显示，相比于传统PID、单神经元PID以及基于蚁群优化算法优化单神经元PID 3种控制方法，本文提出的控制方法超调量分别减少了10.2%、6.1%和1.8%，同时调节时间也相应缩短了0.22、0.07、0.03 s，并且表现出更强的自适应和抗干扰能力，能够使阀门开度控制更加稳定可靠。

Aiming at the nonlinearity and multi-disturbance problems of the electric regulating valve control system in the actual production process, a control method based on the improved ant colony algorithm to optimize the single neuron PID (proportional integral derivative) was proposed and applied to the valve opening control. The self-learning and self-adaptive ability of the single-neuron network was used to achieve the online tuning of PID control parameters. The improved ant colony optimization algorithm was adopted to optimize the learning rate and neuron ratio coefficients in the single-neuron PID, which effectively overcomed the shortcomings of the single-neuron PID where the learning rate and neuron ratio coefficients could not achieve the expected control effect due to the empirical setting. The simulation comparison results show that, compared with the traditional PID, single neuron PID, and single neuron PID based on ant colony optimization algorithm optimization of the three control methods, the control method proposed overshoots the amount of reduction of 10.2%, 6.1%, and 1.8%, respectively. At the same time, the regulation time is correspondingly shortened by 0.22s, 0.07s, and 0.03s. It shows a stronger adaptive and anti-interference ability, which can make the valve opening control more stable and reliable.