Type-1 and Type-2 effective Takagi-Sugeno fuzzy models for decentralized control of multi-input-multi-output processes

Abstract

Effective Takagi-Sugeno (T-S) fuzzy model Type-2 fuzzy system Decentralized control a b s t r a c t Effective model is a novel tool for decentralized controller design to handle the interconnected interactions in a multi-input-multi-output (MIMO) process. In this paper, Type-1 and Type-2 effective Takagi-Sugeno fuzzy models (ETSM) are investigated. By means of the loop pairing criterion, simple calculations are given to build Type-1/Type-2 ETSMs which are used to describe a group of non-interacting equivalent single-input-single-output (SISO) systems to represent an MIMO process, consequently the decentralized controller design can be converted to multiple independent single-loop controller designs, and enjoy the well-developed linear control algorithms. The main contributions of this paper are: i) Compared to the existing T-S fuzzy model based decentralized control methods using extra terms to characterize interactions, ETSM is a simple feasible alternative; ii) Compared to the existing effective model methods using linear transfer functions, ETSM can be carried out without requiring exact mathematical process functions, and lays a basis to develop robust controllers since fuzzy system is powerful to handle uncertainties; iii) Type-1 and Type-2 ETSMs are presented under a unified framework to provide objective comparisons. A nonlinear MIMO process is used to demonstrate the ETSMs' superiority over the effective transfer function (ETF) counterparts as well as the evident advantage of Type-2 ETSMs in terms of robustness. A multi-evaporator refrigeration system is employed to validate the practicability of the proposed methods.

Sparse and Decoupling Control Strategies Based on Takagi–Sugeno Fuzzy Models

“…These extra terms may not be always obtainable, especially in a complex MIMO system. An alternative, called "effective model" [12]- [15], is proposed that the coefficients of the isolated paired channels' models are revised to express the coupled results. In [15], the effective Takagi-Sugeno (T-S) fuzzy models (ETSMs) is presented, where the coefficients of the T-S fuzzy model are revised according to the coupling effects measured by the relative normalized gain array (RNGA) based criterion [2].…”

“…An alternative, called "effective model" [12]- [15], is proposed that the coefficients of the isolated paired channels' models are revised to express the coupled results. In [15], the effective Takagi-Sugeno (T-S) fuzzy models (ETSMs) is presented, where the coefficients of the T-S fuzzy model are revised according to the coupling effects measured by the relative normalized gain array (RNGA) based criterion [2]. Unlike the effective transfer functions in [12]- [14], ETSM [15] can be used when the exact mathematical system functions are not available, and is more robust against the uncertainties.…”

Multilateral Teleoperation With New Cooperative Structure Based on Reconfigurable Robots and Type-2 Fuzzy Logic

“…Compared to the other structure called Mamdani, T-S fuzzy model requires less fuzzy rules and offers a platform to apply the powerful conventional linear algorithms on nonlinear systems [31]. Currently, Type-2 fuzzy logic in the forms of both Mamdani and T-S can be found in a number of studies for control algorithm [44]- [49]. However, few papers use Type-2 T-S fuzzy logic to estimate the robotic uncertainty issues.…”

Modeling and control of dehumidifier in distributed operating liquid desiccant dehumidification system

“…Many mythologies have been studied to obtain or optimize the fuzzy membership functions [136][137][138] , and this study utilizes Gustafson-Kessel (G-K) clustering algorithm [139] to cluster the collected data. The fuzzy membership coefficients here are obtained as explained in [140,141].…”

“…Using the method explained in Appendix D [140,141], the parameters of this fuzzy-PID controller are obtained and listed in Table 6.4 and 6.5. Within a fixed integrals time and same simulation conditions, three parameters described in Eqs.…”