Application of Takagi–Sugeno fuzzy model optimized with an improved Free Search algorithm to industrial polypropylene melt index prediction

Wang, Wenchuan; Chen, Hongmei; Zhang, Miao; Liu, Xinggao; Zhang, Zeyin; Sun, Youxian

doi:10.1177/0142331216644040

Cited by 3 publications

(4 citation statements)

References 57 publications

(56 reference statements)

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“…An interval type-2 Takagi–Sugeno–Kang fuzzy if–then rule R ij is employed in this study (Minjie et al, 2018; Wang et al, 2017). The premise parts are type-2 fuzzy functions while the consequent parts are crisp numbers.…”

Section: Sliding Model Learning Control Structurementioning

confidence: 99%

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Sliding mode learning control of uncertain nonlinear systems with Lyapunov stability analysis

Kayacan

2018

Transactions of the Institute of Measurement and Control

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This paper addresses the Sliding Mode Learning Control (SMLC) of uncertain nonlinear systems with Lyapunov stability analysis. In the control scheme, a conventional control term is used to provide the system stability in compact space while a type-2 neuro-fuzzy controller (T2NFC) learns system behaviour so that the T2NFC completely takes over overall control of the system in a very short time period. The stability of the sliding mode learning algorithm has been proven in the literature; however, it is restrictive for systems without overall system stability. To address this shortcoming, a novel control structure with a novel sliding surface is proposed in this paper, and the stability of the overall system is proven for nth-order uncertain nonlinear systems. To investigate the capability and effectiveness of the proposed learning and control algorithms, the simulation studies have been carried out under noisy conditions. The simulation results confirm that the developed SMLC algorithm can learn the system behaviour in the absence of any mathematical model knowledge and exhibit robust control performance against external disturbances.

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Section: Sliding Model Learning Control Structurementioning

confidence: 99%

“…where γ k is the coefficient of the adaption for the controller gain and positive, i.e., γ k > 0. An interval type-2 Takagi-Sugeno-Kang fuzzy if-then rule R i j is employed in this study Wang et al (2017); Minjie et al (0). The premise parts are type-2 fuzzy functions while the consequent parts are crisp numbers.…”

Section: Control Schemementioning

confidence: 99%

Sliding mode learning control of uncertain nonlinear systems with Lyapunov stability analysis

Kayacan

2018

Transactions of the Institute of Measurement and Control

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“…Due to the superiority of the Takagi-Sugeno (T-S) fuzzy model (Takagi and Sugeno, 1985) to approximate any smooth nonlinear function with preciseness, a lot of works on T-S fuzzy systems have been applied in various engineering applications (Choi et al, 2012; Wang and Chen, 2017; Wang W et al, 2017). For the EPS system, the T-S fuzzy representation has been elaborated in Li et al (2009) and Saifia et al (2015), where authors have reported interesting results of fuzzy control for EPS systems.…”

Section: Introductionmentioning

confidence: 99%

H∞ static output feedback control for electrical power steering subject to actuator saturation via fuzzy Lyapunov functions

Nasri

Saifia

Chadli

et al. 2019

Transactions of the Institute of Measurement and Control

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This paper presents an H∞ static output-feedback control of electrical power steering (EPS) subject to actuator saturation. It deals with different practical problems in designing control of EPS systems, such as unavailability for measurement of the sideslip angle, friction effect, disturbances and the assist motor input current optimization. In order to guarantee good and stable driving, the nonlinear model of the EPS combined with bicycle model of electrical vehicles is used. Firstly, a new Takagi-Sugeno model is established, then using a fuzzy Lyapunov function, an H∞ static output-feedback is designed in terms of linear matrix inequalities. Finally, the proposed control schemes are applied to an EPS system. Simulation results and comparison with previous works show the effectiveness of the proposed control methods.

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“…Among them is the fuzzy modelling which is widely used to mathematically represent real and complex processes. In particular, the well-known Takagi–Sugeno (T-S) fuzzy systems are considered to be a very efficient tool to describe a large class of nonlinear systems (Takagi and Sugeno, 1985; Wang et al, 2016). Since their appearance, T-S fuzzy systems have enjoyed a great success not only in terms of theoretical analysis but also in practical applications (Azimi et al, 2013, 2015).…”

Section: Introductionmentioning

confidence: 99%

An improved heterogeneous multi-swarm PSO algorithm to generate an optimal T-S fuzzy model of a hydraulic process

Chrouta

Zaafouri

Jemli

2017

Transactions of the Institute of Measurement and Control

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In this paper, a new methodology to develop an Optimal Fuzzy model (OptiFel) using an improved Multi-swarm Particle Swarm Optimization (MsPSO) algorithm is proposed with a new adaptive inertia weight based on Grey relational analysis. Since the classical MsPSO suffers from premature convergence and can be trapped into local optima, which significantly affects the model accuracy, a modified MsPSO algorithm is presented here. The most important advantage of the proposed algorithm is the adjustment of fewer parameters in which the main parameter is the inertia weight. In fact, the control of this parameter could facilitate the convergence and prevent an explosion of the swarm. The performance of the proposed algorithm is evaluated by adopting standard tests and indicators which are reported in the specialized literature. The proposed Grey MsPSO is first applied to solve the optimization problems of six benchmark functions and then, compared with the other nine variants of particle swarm optimization. In order to demonstrate the higher search performance of the proposed algorithm, the comparison is then made via two performance tests such as the standard deviation and central processing unit time. To further validate the generalization ability of the Improved OptiFel approach, the proposed algorithm is secondly applied on the Box–Jenkins Gas Furnace system and on a irrigation station prototype. A comparative study based on Mean Square Error is then performed between the proposed approach and other existing methods. As a result, the improved Grey MsPSO is well adopted to find an optimal model for the real processes with high accuracy and strong generalization ability.

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Application of Takagi–Sugeno fuzzy model optimized with an improved Free Search algorithm to industrial polypropylene melt index prediction

Cited by 3 publications

References 57 publications

Sliding mode learning control of uncertain nonlinear systems with Lyapunov stability analysis

Sliding mode learning control of uncertain nonlinear systems with Lyapunov stability analysis

H∞ static output feedback control for electrical power steering subject to actuator saturation via fuzzy Lyapunov functions

An improved heterogeneous multi-swarm PSO algorithm to generate an optimal T-S fuzzy model of a hydraulic process

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