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Gradient auto-tuned Takagi-Sugeno Fuzzy Forward control of a HVAC system using predicted mean vote index

Controllers of HVAC systems are expected to be able to manipulate the inherent nonlinear characteristics of these large scale systems that also have pure lag times, big thermal inertia, uncertain disturbance factors and constraints. In addition, indoor thermal comfort is affected by both temperature...

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Bibliographic Details
Main Authors: Homod R.Z., Sahari K.S.M., Almurib H.A.F., Nagi F.H.
Other Authors: 36994633500
Format: Article
Published: 2023
Subjects:
Building energy control
HVAC system
PMV signal reference
TS Fuzzy identification
TSFF control
Cascade control systems
Climate control
Control nonlinearities
Newton-Raphson method
Building energy
Cascade control
Control methods
Control objectives
Control strategies
Disturbance factors
Gauss-Newton methods
Gradient algorithm
Indoor thermal comfort
Lag time
Non-linear regression
Nonlinear characteristics
Offline
Predicted mean vote
Residential loads
Save energy
T S models
T-S fuzzy
Takagi-sugeno
Thermal inertia
Thermal parameters
Thermal sensitivity
Uncertainty effects
Weather conditions
Humidity control
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Summary:Controllers of HVAC systems are expected to be able to manipulate the inherent nonlinear characteristics of these large scale systems that also have pure lag times, big thermal inertia, uncertain disturbance factors and constraints. In addition, indoor thermal comfort is affected by both temperature and humidity, which are coupled properties. To control these coupled characteristics and tackle nonlinearities effectively, this paper proposes an online tuned Takagi-Sugeno Fuzzy Forward (TSFF) control strategy. The TS model is first trained offline using Gauss-Newton Method for Nonlinear Regression (GNMNR) algorithm with data collected from both building and HVAC system equipments. The model is then tuned online using the gradient algorithm to enhance the stability of the overall system and reject disturbances and uncertainty effects. As control objective, predicted mean vote (PMV) is adopted to avoid temperature-humidity coupling, thermal sensitivity and to save energy at the same time. The proposed TSFF control method is tested in simulation taking into account practical variations such as thermal parameters of buildings, weather conditions and other indoor residential loads. For comparison purposes, normal Takagi-Sugeno fuzzy and hybrid PID Cascade control schemes were also tested. The results demonstrated superior performance, adaptation and robustness of the proposed TSFF control strategy. � 2012 Elsevier B.V. All rights reserved.

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