针对变风量空调送风温度控制系统存在非线性、大迟延、模型参数不确定且抗干扰能力弱等问题，设计了基于自抗扰控制器（active disturbance rejection control, ADRC）的送风温度闭环控制系统。为了克服自抗扰控制器在参数调整上的困难，提出一种改进灰狼优化算法（improved grey wolf optimization, IGWO）优化控制器参数，通过在灰狼优化算法（grey wolf optimization, GWO）中引入混沌映射、非线性收敛因子、动态权重及维度学习策略，增加了种群多样性并兼顾搜索和开发的平衡。通过对不同的单峰、多峰函数测试，证明了IGWO算法具有更好的寻优精度、收敛速度和稳定性。通过MATLAB仿真验证了所提算法的优势和可行性；随后通过实验进一步证明：与传统比例-积分-微分（proportional-integral-derivative, PID）控制器、传统灰狼算法优化的自抗扰控制器相比，采用IGWO算法优化的自抗扰控制器使系统送风温度超调量缩短45.3%和8.9%，调整时间减少34.8%和11.2%，稳态误差更小，系统更节能。

Aiming at the problems of nonlinearity, large delay, uncertain model parameters and weak anti-interference ability in the temperature control system of variable air volume air conditioning supply air, a closed-loop control system of supply air temperature based on active disturbance rejection control (ADRC) was designed. In order to overcome the difficulty in parameter adjustment of active disturbance rejection controller, an improved grey wolf optimization (IGWO) algorithm was proposed to optimize controller parameters. By introducing chaotic mapping, nonlinear convergence factors, dynamic weights and dimensional learning strategies into grey wolf optimization (GWO), the population diversity was increased and the balance between search and exploitation was taken into account. The advantages and feasibility of the proposed algorithm were verified by MATLAB simulation. The experiment further proves that compared with the traditional proportional integral derivative (PID) controller and the traditional gray wolf algorithm, the IGWO algorithm can shorten the supply air temperature overshoot by 45.3% and 8.9%. The adjustment time is reduced by 34.8% and 11.2%, the steady-state error is smaller, and the system is more energy efficient.