Dynamic fuzzy temperature control with quasi-Newtonian particle swarm optimization for precise air conditioning

Abstract

Precise air conditioners are renowned for their ability to deliver accurate and stable air output. However, achieving precise temperature control can be challenging due to the complexity of the air conditioner. To address this issue, a dynamic fuzzy control method based on quasi-Newton particle swarm optimization (QNPSO) is put forward for precise air conditioning. The air conditioning dynamics is first modelled using the interconnection of the first-order delayed transfer functions. Incremental fuzzy controllers are then employed to track temperature changes and prevent steady-state errors for all internal components of the conditioner. By employing QNPSO to optimize all control parameters simultaneously, the present method effectively addresses the limitations of particle swarm optimization in poor local search capability, and the sensitivity of the quasi-Newton algorithm to initial values. Additionally, a dynamic adjustment optimization strategy is suggested to accommodate start-up dynamics and meet temperature change requirements. In this way, the precise air conditioning is achieved by regulating internal components in an integrated fashion. The superior performance of the proposed method is validated through model simulation evaluations and prototype conditioner experiments using a water-cooled precise air conditioner. The results demonstrate that the proposed method achieves faster stabilization time and higher control accuracy compared to the state-of-the-art peers.