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.