Interaction measures for systems under decentralized control
Automatica (Journal of IFAC)
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
Multiple fuzzy model-based temperature predictive control for HVAC systems
Information Sciences: an International Journal
Brief paper: Decentralized switching control for hierarchical systems
Automatica (Journal of IFAC)
Adaptive fuzzy control for a class of uncertain nonaffine nonlinear systems
Information Sciences: an International Journal
Brief paper: Decentralized adaptive control for large-scale time-delay systems with dead-zone input
Automatica (Journal of IFAC)
Brief paper: Adaptive fuzzy decentralized control for interconnected MIMO nonlinear subsystems
Automatica (Journal of IFAC)
Observer-based fuzzy adaptive control for strict-feedback nonlinear systems
Fuzzy Sets and Systems
A combined backstepping and small-gain approach to robust adaptive fuzzy output feedback control
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A Survey on Analysis and Design of Model-Based Fuzzy Control Systems
IEEE Transactions on Fuzzy Systems
Brief Decentralized sliding mode control design using overlapping decompositions
Automatica (Journal of IFAC)
A heuristic approach to the design of linear multivariable interacting control systems
Automatica (Journal of IFAC)
Fuzzy basis functions, universal approximation, and orthogonal least-squares learning
IEEE Transactions on Neural Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper presents a loop pairing method for determining the control configuration for multi-input-multi-output (MIMO) processes represented by Takagi-Sugeno (T-S) fuzzy models. The method is simple with straightforward calculation and it provides more accurate results compared with existing fuzzy pairing approaches, since both steady-state and dynamic information for the system is utilized. Each individual loop in the MIMO process is represented by a T-S fuzzy model based on the data and the models are then assembled to form the MIMO model. Simple formulae are derived to calculate the steady-state and dynamic information for the loops. In this way, interactions among the loops can be assessed and loop pairing can be determined according to the relative normalized gain array (RNGA) criterion. Two examples are provided to show that loop pairing decisions obtained from T-S fuzzy models are the same as those obtained from precise mathematical models. This demonstrates the effectiveness of the proposed interaction measure and the loop pairing method.