Aiming at controlling indoor temperature of each air-conditioning room equipped with fan coil unit (FCU), this paper proposes a hybrid control system to deal with such problem, in which a fractional order PID controller for indoor temperature (IT-FOPIDC) and a three-position controller for supply air volume (SAV-TPC) are used to maintain steady indoor temperature, respectively. For the key problem of tuning parameters of IT-FOPIDC, a modified ant colony optimization algorithm (MACOA) is reconstructed, and it is tested to be feasible by the simulation results. Thus, an algorithm using this MACOA is designed to adaptively solve the satisfied values of five parameters of IT-FOPIDC to further upgrade the control quality of indoor temperature from software. In the meanwhile, SAV-TPC manipulates the corresponding flow rate of supply air discharged from FCU to provide the dynamic variation of cooling or heating load to an air-conditioning room. So these two controllers work together for achieving the desired indoor temperature. The corresponding numerical simulation of this hybrid control system is carried out and the results indicate that this algorithm using MACOA is reliable to tackle the key problem of tuning parameters of IT-FOPIDC so that the control quality of indoor temperature is obviously improved. Moreover, for the same IT-FOPIDC, the control performance of the other intelligent optimization algorithms is compared to that of the proposed algorithm using MACOA under the same scenario, simulation results also show the latter is superior to the former in terms of analyzing the dynamic responses of indoor temperature, which may offer the corresponding reference guides for regulating the key performance parameters in HVAC& R engineering.

针对每个装有风机盘管（FCU）的空调房间的室内温度控制，本文提出了一种混合控制系统来解决这个问题，其中的室内温度分数阶PID控制器（IT-FOPIDC）和一个混合控制系统来解决这个问题。三位置送风量控制器（SAV-TPC）分别用于保持室内温度稳定。针对IT-FOPIDC参数调整的关键问题，重构了改进的蚁群优化算法(MACOA)，并通过仿真结果验证了其可行性。因此，设计了一种使用该MACOA的算法来自适应求解IT-FOPIDC的五个参数的满足值，以进一步从软件上提升室内温度的控制质量。与此同时，SAV-TPC 控制从 FCU 排出的相应送风流量，为空调房提供冷热负荷的动态变化。因此，这两个控制器协同工作以实现所需的室内温度。对该混合控制系统进行了相应的数值仿真，结果表明，该算法采用MACOA，能够有效解决IT-FOPIDC参数整定的关键问题，显着提高了室内温度的控制质量。此外，对于相同的IT-FOPIDC，将其他智能优化算法的控制性能与相同场景下使用MACOA提出的算法的控制性能进行比较，