Development an Adaptive Incremental Fuzzy PI Controller for a HVAC System

Abstract

This paper presents an adaptive incremental fuzzy PI controller (AIFPI) for a heating, ventilating, and air conditioning (HVAC) system capable of maintaining comfortable conditions under varying thermal loads. The HVAC system has two subsystems and is used to control indoor temperature and humidity in a thermal zone. As the system has strong-coupling and non-linear characteristics, fixed PI controllers have poor control performance and more energy consumption. Aiming to solve the problem, fuzzy control and PI control are combined together organically. In the proposed control scheme, the error of the system output and its derivative are taken as two parameters necessary to adapt the proportional (P) and integral (I) gains of the PI controller based on fuzzy reasoning according to practical control experiences. To evaluate the effectiveness of the proposed control methods in the HVAC system, it is compared with a fixed well-tuned PI controller. The results demonstrate that the AIFPI controller has more superior performance than the latter.

A Dynamic Fuzzy Controller to Meet Thermal Comfort by Using Neural Network Forecasted Parameters as the Input

“…In order to validate the proposed approach, a simulation is carried out using MATLAB, and the results are promising, as they show the benefits introduced by the application of an FLC in an HVAC system. The author of [40] presents an adaptive incremental fuzzy PI controller for an HVAC system capable of maintaining comfortable conditions under varying thermal loads. Since the HVAC systems have strong-coupling and non-linear characteristics, fixed PI controllers have poor control performance and more energy consumption.…”

A Redundant Recurrent Cerebellar Model Articulation Control System for Industrial Applications

“…Because of the effects of the uncertainties due to dynamic perturbations and signal noises, the dynamic model of these systems cannot be completely obtained [4][5][6][7]. Consequently, model-based controllers cannot achieve the desired performances [8][9][10]. To overcome these disadvantages, advanced controllers such as fuzzy logic controller (FLC) [11,12], sliding mode control (SMC) [13,14], neural networks (NN) [15,16], and wavelet neural network (WNN) [17][18][19][20][21] have been developed and have achieved impressive results in dealing with uncertain systems.…”