scholarly journals Design of Adaptive TSK Fuzzy Self-Organizing Recurrent Cerebellar Model Articulation Controller for Chaotic Systems Control

2021 ◽  
Vol 11 (4) ◽  
pp. 1567
Author(s):  
Shun-Yuan Wang ◽  
Chuan-Min Lin ◽  
Chen-Hao Li
Keyword(s):  
Chaotic Systems ◽  
Tracking Error ◽  
Extensive Study ◽  
Fuzzy Rules ◽  
Cerebellar Model Articulation Controller ◽  
Control Performance ◽  
Fuzzy Cmac ◽  
Structure Complexity ◽  
Takagi Sugeno ◽  
Self Organizing

The synchronization and control of chaos have been under extensive study by researchers in recent years. In this study, an adaptive Takagi–Sugeno–Kang (TSK) fuzzy self-organizing recurrent cerebellar model articulation controller (ATFSORC) is proposed, which is composed of a set of TSK fuzzy rules, a cerebellar model articulation controller (CMAC), a recurrent CMAC (RCMAC), a self-organizing CMAC (SOCMAC), and a compensation controller. Specifically, SOCMAC, RCMAC, and adaptive laws are adopted so that the association memory layers of ATFSORC can be modulated in accordance with the layer decision-making mechanism in order to reduce the structure complexity and improve the control performance of ATFSORC. Moreover, the Takagi–Sugeno–Kang fuzzy rules are introduced to increase the learning speed of ATFSORC, and the improved compensating controller is designed to dispel the errors between an ideal controller and the TFSORC. Moreover, the proposed ATFSORC is applied to chaotic systems in order to validate its performance and feasibility. Several simulation schemes are demonstrated to show the effectiveness of the proposed method. Simulation results show that the proposed ATFSORC can obtain a favorable control performance when the chaotic systems are operated at different parameters. Specifically, ATFSORC can achieve faster convergence of the tracking error than fuzzy CMAC (FCMAC) and CMAC.

scholarly journals Robust Adaptive Self-Organizing Wavelet Fuzzy CMAC Tracking Control for De-icing Robot Manipulator

2015 ◽  
Vol 10 (4) ◽  
pp. 567 ◽  
Author(s):  
ThanhQuyen Ngo ◽  
TaVan Phuong
Keyword(s):  
Robot Manipulator ◽  
Learning Ability ◽  
Cerebellar Model Articulation Controller ◽  
Robust Controller ◽  
Fast Learning ◽  
Self Organized ◽  
Fuzzy Cmac ◽  
Robust Adaptive ◽  
The Stability ◽  
Self Organizing

In this paper, a robust adaptive self-organizing control system based on a novel wavelet fuzzy cerebellar model articulation controller (WFCMAC) is developed for an n-link robot manipulator to achieve the high-precision position tracking. This proposed controller consists of two parts: one is the WFCMAC approach which is implemented to cope with nonlinearities, due to the novel WFCMAC not only incorporates the wavelet decomposition property with fuzzy CMAC fast learning ability but also it will be self-organized; that is, the layers of WFCMAC will grow or prune systematically. Therefore, dimension of WFCMAC can be simplified. The second is the order which is the adaptive robust controller which is designed to achieve robust tracking performance of the system. The adaptive tuning laws of WFCMAC parameters and error estimation of adaptive robust controller are derived through the Lyapunov function so that the stability of the system can be guaranteed. Finally, the simulation and experimental results of novel three-link deicing robot manipulator are applied to verify the effectiveness of the proposed control methodology.

scholarly journals Dynamic System Identification and Prediction Using a Self-Evolving Takagi–Sugeno–Kang-Type Fuzzy CMAC Network

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 631
Author(s):  
Cheng-Jian Lin ◽  
Cheng-Hsien Lin ◽  
Jyun-Yu Jhang
Keyword(s):  
Structure Learning ◽  
Gaussian Function ◽  
Cerebellar Model Articulation Controller ◽  
Fuzzy Cmac ◽  
Proposed Model ◽  
Firing Strength ◽  
Gradient Descent Algorithm ◽  
Combination Function ◽  
Takagi Sugeno ◽  
Prediction Problems

This study proposes a Self-evolving Takagi-Sugeno-Kang-type Fuzzy Cerebellar Model Articulation Controller (STFCMAC) for solving identification and prediction problems. The proposed STFCMAC model uses the hypercube firing strength for generating external loops and internal feedback. A differentiable Gaussian function is used in the fuzzy hypercube cell of the proposed model, and a linear combination function of the model inputs is used as the output of the proposed model. The learning process of the STFCMAC is initiated using an empty hypercube base. Fuzzy hypercube cells are generated through structure learning, and the related parameters are adjusted by a gradient descent algorithm. The proposed STFCMAC network has some advantages that are summarized as follows: (1) the model automatically selects the parameters of the memory structure, (2) it requires few fuzzy hypercube cells, and (3) it performs identification and prediction adaptively and effectively.

Design of a Hybrid Adaptive CMAC Tracking Control for a Class of Uncertain Chaotic Systems with only Output Measurement

2011 ◽  
Vol 383-390 ◽  
pp. 5363-5368
Author(s):  
Chun Sheng Chen
Keyword(s):  
Chaotic Systems ◽  
Sliding Mode ◽  
Tracking Error ◽  
Approximation Error ◽  
Lyapunov Stability Theory ◽  
Cerebellar Model Articulation Controller ◽  
Output Measurement ◽  
Supervised Control ◽  
Uncertain Chaotic Systems ◽  
Hybrid Adaptive Control

This paper presents an observer-based hybrid adaptive cerebellar model articulation controller (CMAC) with a supervisory controller for uncertain chaotic systems, in which the hybrid adaptive control composed of a direct adaptive CMAC and an indirect adaptive CMAC control is performed as the sliding mode control (SMC). The total states of the chaotic system are not assumed to be available for measurement. A state observer is used to estimate unmeasured states of the systems. The supervised control is appended to assure that the hybrid adaptive CMAC controller achieve a stable closed-loop system through Lyapunov stability theory. Finally, simulation results show that the effect of the approximation error on the tracking error can be attenuated efficiently.

Data-Tuned Fuzzy Logic Controller Applied to a Horizontal Axis Wind System

2020 ◽  
Author(s):  
Lorenzo Dambrosio
Keyword(s):  
Fuzzy Logic ◽  
Wind Turbine ◽  
Control Algorithm ◽  
Fuzzy Logic Controller ◽  
Tracking Error ◽  
Synchronous Generator ◽  
Horizontal Axis ◽  
Fuzzy Rules ◽  
Wind System ◽  
Horizontal Axis Wind Turbine

Abstract This paper deals with the control problem concerning the output voltage frequency and amplitude regulation of a wind system power plant not connected to the supply grid. The wind system configuration includes a horizontal-axis wind-turbine which drives a synchronous generator. An appropriate modeling approach has been adopted for both the wind-turbine and the synchronous generator. The proposed controller makes use of the fuzzy logic environment in order to take advantage of the wind plant system informations integrated into a limited number of equilibrium condition points (input variable - output variable pairs). The fuzzy logic controller described in the present paper merges the most appropriate fuzzy rules clusters, based on the steady state working conditions. Then, thanks to a Least Square Estimator algorithm, the proposed control algorithm evaluates, for each sample time, the linear relation between control law correction and control tracking error levels. In order to demonstrate robustness of the suggested fuzzy control algorithm, two sets of results have been provided: the first one consider a fuzzy base with equally spaced rules, whereas, in the second set results, the number of fuzzy rules is reduced by a 25%.

scholarly journals A stable proportional–proportional integral tracking controller with self-organizing fuzzy-tuned gains for parallel robots

2019 ◽  
Vol 16 (1) ◽  
pp. 172988141881995
Author(s):  
Francisco G Salas ◽  
Jorge Orrante-Sakanassi ◽  
Raymundo Juarez-del-Toro ◽  
Ricardo P Parada
Keyword(s):  
Stability Analysis ◽  
Performance Index ◽  
Closed Loop ◽  
Tracking Error ◽  
Parallel Robots ◽  
Control Loop ◽  
Closed Loop System ◽  
Proportional Integral ◽  
The Stability ◽  
Self Organizing

Parallel robots are nowadays used in many high-precision tasks. The dynamics of parallel robots is naturally more complex than the dynamics of serial robots, due to their kinematic structure composed by closed chains. In addition, their current high-precision applications demand the innovation of more effective and robust motion controllers. This has motivated researchers to propose novel and more robust controllers that can perform the motion control tasks of these manipulators. In this article, a two-loop proportional–proportional integral controller for trajectory tracking control of parallel robots is proposed. In the proposed scheme, the gains of the proportional integral control loop are constant, while the gains of the proportional control loop are online tuned by a novel self-organizing fuzzy algorithm. This algorithm generates a performance index of the overall controller based on the past and the current tracking error. Such a performance index is then used to modify some parameters of fuzzy membership functions, which are part of a fuzzy inference engine. This fuzzy engine receives, in turn, the tracking error as input and produces an increment (positive or negative) to the current gain. The stability analysis of the closed-loop system of the proposed controller applied to the model of a parallel manipulator is carried on, which results in the uniform ultimate boundedness of the solutions of the closed-loop system. Moreover, the stability analysis developed for proportional–proportional integral variable gains schemes is valid not only when using a self-organizing fuzzy algorithm for gain-tuning but also with other gain-tuning algorithms, only providing that the produced gains meet the criterion for boundedness of the solutions. Furthermore, the superior performance of the proposed controller is validated by numerical simulations of its application to the model of a planar three-degree-of-freedom parallel robot. The results of numerical simulations of a proportional integral derivative controller and a fuzzy-tuned proportional derivative controller applied to the model of the robot are also obtained for comparison purposes.

Comments on “Takagi–Sugeno fuzzy control for a wide class of fractional-order chaotic systems with uncertain parameters via linear matrix inequality”

2019 ◽  
Vol 26 (9-10) ◽  
pp. 643-645
Author(s):  
Xuefeng Zhang
Keyword(s):  
Fuzzy Control ◽  
Linear Matrix Inequality ◽  
Fractional Order ◽  
Wide Class ◽  
Chaotic Systems ◽  
Sufficient Conditions ◽  
Linear Matrix ◽  
Uncertain Parameters ◽  
Matrix Inequality ◽  
Takagi Sugeno

This article shows that sufficient conditions of Theorems 1–3 and the conclusions of Lemmas 1–2 for Takasi–Sugeno fuzzy model–based fractional order systems in the study “Takagi–Sugeno fuzzy control for a wide class of fractional order chaotic systems with uncertain parameters via linear matrix inequality” do not hold as asserted by the authors. The reason analysis is discussed in detail. Counterexamples are given to validate the conclusion.

FUZZY CHAOS SYNCHRONIZATION VIA SAMPLED DRIVING SIGNALS

2004 ◽  
Vol 14 (08) ◽  
pp. 2721-2733 ◽  
Author(s):  
JUAN GONZALO BARAJAS-RAMÍREZ ◽  
GUANRONG CHEN ◽  
LEANG S. SHIEH
Keyword(s):  
Continuous Time ◽  
System Approach ◽  
Chaos Synchronization ◽  
Chaotic Systems ◽  
Disturbance Attenuation ◽  
Fuzzy Observer ◽  
Master System ◽  
Error Dynamics ◽  
Takagi Sugeno ◽  
Driving Signal

In this paper, a methodology to design a system that robustly synchronizes a master chaotic system from a sampled driving signal is developed. The method is based on the fuzzy Takagi–Sugeno representation of chaotic systems, from which a continuous-time fuzzy observer is designed as the solution of an LMI minimization problem such that the error dynamics have H∞disturbance attenuation performance. Then, from the dual-system approach, the fuzzy observer is digitally redesigned such that the performance is maintained for the sampled master system. The effectiveness of the proposed synchronization methodology is finally illustrated via numerical simulations of the chaotic Chen's system.

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