Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy Modeling for Control
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Model Predictive Control in the Process Industry
Model Predictive Control in the Process Industry
Fuzzy Control
Multi-model predictive control based on the Takagi-Sugeno fuzzy models: a case study
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
Model predictive control using fuzzy decision functions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An approach to fuzzy control of nonlinear systems: stability and design issues
IEEE Transactions on Fuzzy Systems
Fuzzy model predictive control
IEEE Transactions on Fuzzy Systems
Information Sciences: an International Journal
Identification of different stages of diabetic retinopathy using retinal optical images
Information Sciences: an International Journal
Transformation of Fuzzy Takagi-Sugeno Models into Piecewise Affine Models
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Automatic identification of cardiac health using modeling techniques: A comparative study
Information Sciences: an International Journal
Fuzzy adaptive control for the actuators position control and modeling of an expert system
Expert Systems with Applications: An International Journal
Application of Higher Order Spectra to Identify Epileptic EEG
Journal of Medical Systems
Interaction analysis and loop pairing for MIMO processes described by T--S fuzzy models
Fuzzy Sets and Systems
A study towards applying thermal inertia for energy conservation in rooms
ACM Transactions on Sensor Networks (TOSN)
Expert Systems with Applications: An International Journal
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In this paper, a multiple model predictive control (MMPC) strategy based on Takagi--Sugeno (T-S) fuzzy models for temperature control of air-handling unit (AHU) in heating, ventilating, and air-conditioning (HVAC) systems is presented. The overall control system is constructed by a hierarchical two-level structure. The higher level is a fuzzy partition based on AHU operating range to schedule the fuzzy weights of local models in lower level, while the lower level is composed of a set of T-S models based on the relation of manipulated inputs and system outputs correspond to the higher level. Following this divide-and-conquer strategy, the complex nonlinear AHU system is divided into a set of T S models through a fuzzy satisfactory clustering (FSC) methodology and the global system is a fuzzy integrated linear varying parameter (LPV) model. A hierarchical MMPC strategy is developed using parallel distribution compensation (PDC) method, in which different predictive controllers are designed for different T S fuzzy rules and the global controller output is integrated by the local controller outputs through their fuzzy weights. Simulation and real process testing results show that the proposed MMPC approach is effective in HVAC system control applications.