Stable fuzzy logic control of a general class of chaotic systems

Precup, Radu‐Emil; Tomescu, Marius-Lucian

doi:10.1007/s00521-014-1644-7

Cited by 112 publications

(36 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The scale of adjusted control parameters of FA-PID are wider than F-PID. 3Reference [34] shows a serious advantage with respect to LMI-based fuzzy logic control system design approaches that involve control laws, which are sensitive to all process parameters. The above idea is adopted to this study, and the improved methods can be also applied to wide practical applications because the classical PID method is widely used.…”

Section: Results Analysis Of Unsaturated Simulationmentioning

confidence: 99%

“…3A new approach to the stable design of Takagi-Sugeno-Kang fuzzy logic control systems was supported by a novel stability analysis theorem. This theorem is based on previous results concerning the application of Lyapunov's direct method for a general class of chaotic systems [34]. (4) A hybrid controller was designed based on Magneto-rheological damper lookup table for quarter car suspension [35].…”

Section: A Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Fuzzy Controllers With Neural Network Predictor for Second-Order Linear Systems With Time Delay

Liu

Tao

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Artificial intelligence methods are widely applied in advanced control practices such as fuzzy calculation-based intelligent control method, neural network-based predictive control method, etc. Control performances are expected to be improved such as: faster control speed, smaller steady-state error, and less repeated manual tuning workloads in harmful environments for engineers. Main works are as follows: Firstly, change ratio-based fuzzy adjusted PID (FA-PID) method is improved. The adjusted parameters of FA-PID are the multiplication result of the change ratio at the current control cycle and the control parameters at the time of previous adjustment cycle. Secondly, prediction of back propagation neural networks-based fuzzy-PID (BPNN-F-PID) and prediction of back propagation neural networks-based FA-PID (BPNN-FA-PID) are improved, in which the adjusted control parameters are calculated according to the predicted output of control system. Thirdly, comparative simulations of all the above methods are implemented, a series of better effects are found. Effects are as follows: The improved controllers have better performance such as faster control speed, better ability of anti-interference, restrained overshoot and smaller steady-state error.

show abstract

Section: Results Analysis Of Unsaturated Simulationmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Fuzzy Controllers With Neural Network Predictor for Second-Order Linear Systems With Time Delay

Liu

Tao

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Such a robust control term can be conceived using a sliding mode control [27][28][29]33], an H?-based robust control [26,30,31] and a quasi-sliding mode control [32]. However, it is should be mentioned that the above results [26][27][28][29][30][31][32][33][34][35][36] are only applicable to chaotic systems with integer order.…”

Section: Introductionmentioning

confidence: 90%

“…Based on the universal approximation capability of the fuzzy systems [25], numerous adaptive fuzzy control schemes [26][27][28][29][30][31][32][33][34][35][36] have been developed for a class of uncertain chaotic systems but with integer order. In these schemes, the adaptive fuzzy systems are used to estimate the model uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Projective synchronization of two different fractional-order chaotic systems via adaptive fuzzy control

Bouzeriba

Boulkroune

Bouden

2015

Neural Comput & Applic

View full text Add to dashboard Cite

In this paper, the projective synchronization problem of two fractional-order different chaotic (or hyperchaotic) systems with both uncertain dynamics and external disturbances is considered. More particularly, a fuzzy adaptive control system is investigated for achieving an appropriate projective synchronization of unknown fractional-order chaotic systems. The adaptive fuzzy logic systems are used to approximate some uncertain nonlinear functions appearing in the system model. These latter are augmented by a robust control term to compensate for the unavoidable fuzzy approximation errors and external disturbances as well as residual error due to the use of the socalled e-modification in the adaptive laws. A Lyapunov approach is adopted for the design of the parameter adaptation laws and the proof of the corresponding stability as well as the asymptotic convergence of the underlying synchronization errors towards zero. The effectiveness of the proposed synchronization system is illustrated through numerical experiment results.

show abstract

“…Haidegger et al [18] used PID-fuzzy controllers to improve the control system performance and proposed a cascade loop solution to support future teleoperation missions. Precup et al [19] proposed a new approach to the stable design of fuzzy logic control systems that deal with a general class of chaotic processes by employing Lyapunov's direct method and the separate stability analysis of each rule in the fuzzy logic controller (FLC) on the basis of a stability analysis theorem. Dong et al [20] and Jiang et al [21] also made some studies about nonlinear control, such as proposing the robust adaptive fuzzy neural network control (RAFNNC) algorithm to control the heading of unmanned marine vehicles, in which an improved gray model is combined with the nonlinear robust controller to design the ship course-keeping controller.…”

Section: Introductionmentioning

confidence: 99%

Tank-Level Control of Liquefied Natural Gas Carrier Based on Gaussian Function Nonlinear Decoration

Zhao

Zhang

2020

JMSE

View full text Add to dashboard Cite

This paper introduces a novel controller into the tank level control of liquefied natural gas (LNG) carrier. The proposed controller can simplify the design process and save energy output of the controller under the premise of achieving a good control effect of the liquid level of a liquefied natural gas (LNG) carrier tank. Moreover, Gaussian function is adopted to decorate nonlinearly the output of the linear controller designed by using the second-order closed loop gain-shaping algorithm, and then the final controller is obtained. The effectiveness of the controller is verified by simulation experiments on the LNG carrier ‘‘Da-Peng Sun’’ No.2 liquid tank. The simulation results show that the comprehensive control performance index J of the second-order closed loop gain-shaping controller decorated by Gaussian function reduces by 11.8% and 95.3%, respectively, compared with the controller without nonlinear decoration and the linear proportional–integral–differential (PID) controller, meeting the requirement of energy saving and realizing a good control result of the tank level of the LNG carrier.

show abstract

Stable fuzzy logic control of a general class of chaotic systems

Cited by 112 publications

References 48 publications

Fuzzy Controllers With Neural Network Predictor for Second-Order Linear Systems With Time Delay

Fuzzy Controllers With Neural Network Predictor for Second-Order Linear Systems With Time Delay

Projective synchronization of two different fractional-order chaotic systems via adaptive fuzzy control

Tank-Level Control of Liquefied Natural Gas Carrier Based on Gaussian Function Nonlinear Decoration

Contact Info

Product

Resources

About