Fuzzy Control Systems: Past, Present and Future

Nguyen, Anh‐Tu; Taniguchi, Tsuyoshi; Eciolaza, Luka; Campos, Víctor Costa da Silva; Palhares, Reinaldo M.; Sugeno, Michio

doi:10.1109/mci.2018.2881644

Cited by 239 publications

(82 citation statements)

References 99 publications

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“…19 Fuzzy control theory combines traditional control algorithms with fuzzy mathematics to study inexact objects by simulating human thinking processes. 20 The basis of fuzzy control systems is the fuzzy set theory and fuzzy logic control. It uses fuzzy logic to imitate human thinking and controls those nonlinear, timevarying complex systems, and systems that cannot establish mathematical models.…”

Section: The Complements Of a And B Arementioning

confidence: 99%

RETRACTED: Design of basketball robot based on behavior-based fuzzy control

Zhi

Jiang

2020

International Journal of Advanced Robotic Systems

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Aiming at the strong dependence on environmental information in traditional algorithms, the path planning of basketball robots in an unknown environment, and improving the safety of autonomous navigation, this article proposes a path planning algorithm based on behavior-based module control. In this article, fuzzy control theory is applied to the behavior control structure, and these two path planning algorithms are combined to solve the path planning problem of basketball robots in an unknown environment. First, the data of each sensor of the basketball robot configuration are simply fused. Then, the obstacle distance parameters in the three directions of front, left, and right are simplified and fuzzified. Then combined with the target direction parameters, the speed, and steering of the basketball robot are controlled by fuzzy rule reasoning to realize path planning. The simulation results show that the basketball robot can overcome the uncertainty in the environment, effectively achieve good path planning, verify the feasibility of the fuzzy control algorithm, and demonstrate the validity and correctness of the path planning strategy.

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Section: The Complements Of a And B Arementioning

confidence: 99%

RETRACTED: Design of basketball robot based on behavior-based fuzzy control

Zhi

Jiang

2020

International Journal of Advanced Robotic Systems

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“…• Layer 1: The membership grade of x m on X r,m is computed, by (2). • Layer 2: The firing level of each rule Rule r is computed, by (3).…”

Section: E Comparison With Anfismentioning

confidence: 99%

Optimize TSK Fuzzy Systems for Regression Problems: Minibatch Gradient Descent With Regularization, DropRule, and AdaBound (MBGD-RDA)

Yuan

Huang

et al. 2020

IEEE Trans. Fuzzy Syst.

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Takagi-Sugeno-Kang (TSK) fuzzy systems are very useful machine learning models for regression problems. However, to our knowledge, there has not existed an efficient and effective training algorithm that ensures their generalization performance, and also enables them to deal with big data. Inspired by the connections between TSK fuzzy systems and neural networks, we extend three powerful neural network optimization techniques, i.e., mini-batch gradient descent (MBGD), regularization, and AdaBound, to TSK fuzzy systems, and also propose three novel techniques (DropRule, DropMF, and Drop-Membership) specifically for training TSK fuzzy systems. Our final algorithm, MBGD with regularization, DropRule and Ad-aBound (MBGD-RDA), can achieve fast convergence in training TSK fuzzy systems, and also superior generalization performance in testing. It can be used for training TSK fuzzy systems on datasets of any size; however, it is particularly useful for big datasets, on which currently no other efficient training algorithms exist.

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“…Modeling errors are unavoidable in real-world applications, especially when using PMA approximation [22]. Thus, a robust design is necessary to robustify the feedback linearization control scheme.…”

Section: Lmi-based Robust Control Designmentioning

confidence: 99%

“…Furthermore, the proposed control approach allows reducing the costly automotive sensors and/or observers/estimators design tasks by exploiting the maximum possible available offline information. The idea is to estimate all variables needed for control design by using piecewise multiaffine (PMA) modeling [21,22], represented in the form of static LUTs issued from the data of the test bench. The effectiveness of the proposed control strategy is illustrated through extensive AMESim/Simulink co-simulations with a high-fidelity AMESim engine model.…”

Section: Introductionmentioning

confidence: 99%

Robust Feedback Linearization Approach for Fuel-Optimal Oriented Control of Turbocharged Spark-Ignition Engines

Nguyen¹,

Guerra²,

Lauber³

2020

Intelligent and Efficient Transport Systems - Design, Modelling, Control and Simulation

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This chapter proposes a new control approach for the turbocharged air system of a gasoline engine. To simplify the control implementation task, static lookup tables (LUTs) of engine data are used to estimate the engine variables in place of complex dynamical observer and/or estimators. The nonlinear control design is based on the concept of robust feedback linearization which can account for the modeling uncertainty and the estimation errors induced by the use of engine lookup tables. The control feedback gain can be effectively computed from a convex optimization problem. Two control strategies have been investigated for this complex system: drivability optimization and fuel reduction. The effectiveness of the proposed control approach is clearly demonstrated with an advanced engine simulator.

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Fuzzy Control Systems: Past, Present and Future

Cited by 239 publications

References 99 publications

RETRACTED: Design of basketball robot based on behavior-based fuzzy control

RETRACTED: Design of basketball robot based on behavior-based fuzzy control

Optimize TSK Fuzzy Systems for Regression Problems: Minibatch Gradient Descent With Regularization, DropRule, and AdaBound (MBGD-RDA)

Robust Feedback Linearization Approach for Fuel-Optimal Oriented Control of Turbocharged Spark-Ignition Engines

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