Research of Robust Adaptive Fuzzy Control in Manipulator

There are friction characteristics, random disturbance, load variation and other nonlinear influencing factors in the multi-joint manipulator system generally. According to the problem that the traditional PID and fuzzy control are difficult to achieve rapid and high-precision control for this kind of system, a kind of robust adaptive fuzzy controller was designed based on fuzzy compensation under the circumstances that the fuzzy information can be known and all the state variables can be measured. Simultaneously, in order to reduce the computational load of fuzzy approximation and improve the efficiency of mathematical operation, a method that distinguishing different disturbance compensatory terms and approximating each of them respectively was adopted. The simulation results show that the robust adaptive fuzzy controller based on fuzzy compensation can restrain friction, disturbance, load variation and other nonlinear influencing factors.

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Application Research on H∞ Optimization Control of Permanent Magnet Linear Servo System Based on Adaptive Fuzzy Control Strategy

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.694-697.2185 ◽

2013 ◽

Vol 694-697 ◽

pp. 2185-2189

Author(s):

Xiao Ping Zhu ◽

Xiu Ping Wang ◽

Chun Yu Qu ◽

Jun You Zhao

Keyword(s):

Fuzzy Control ◽

Control Strategy ◽

Control Method ◽

Fuzzy Controller ◽

External Disturbance ◽

Adaptive Fuzzy Control ◽

Servo System ◽

Robust Controller ◽

Adaptive Fuzzy Controller ◽

Adaptive Fuzzy

In order to against the uncertain disturbance of AC linear servo system, an H mixed sensitivity control method based on adaptive fuzzy control was putted forward in the paper. The controller is comprised of an adaptive fuzzy controller and a H robust controller, the adaptive fuzzy controller is used to approximate this ideal control law, H robust controller is designed for attenuating the approximation errors and the influence of the external disturbance. The experimental results show that this control strategy not only has a strong robustness to uncertainties of the linear system, but also has a good tracking performance, furthermore the control greatly improves the robust tracking precision of the direct drive linear servo system.

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Observer-Based Robust Adaptive Fuzzy Control for MIMO Nonlinear Uncertain Systems with Delayed Output

Mathematical Problems in Engineering ◽

10.1155/2013/215201 ◽

2013 ◽

Vol 2013 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Chiang Cheng Chiang

Keyword(s):

Nonlinear System ◽

Fuzzy Control ◽

Robust Stability ◽

Uncertain Systems ◽

Fuzzy Controller ◽

Adaptive Fuzzy Control ◽

Controlled System ◽

Adaptive Fuzzy ◽

Nonlinear Uncertain Systems ◽

Robust Adaptive

An observer-based robust adaptive fuzzy control scheme is presented to tackle the problem of the robust stability and the tracking control for a class of multiinput multioutput (MIMO) nonlinear uncertain systems with delayed output. Because the nonlinear system functions and the uncertainties of the controlled system including structural uncertainties are supposed to be unknown, fuzzy logic systems are utilized to approximate these nonlinear system functions and the upper bounded functions of the uncertainties. Moreover, the upper bound of uncertainties caused by these fuzzy modeling errors is also estimated. In addition, the state observer based on state variable filters is designed to estimate all states which are not available for measurement in the controlled system. By constructing an appropriate Lyapunov function and using strictly positive-real (SPR) stability theorem, the proposed robust adaptive fuzzy controller not only guarantees the robust stability of a class of multivariable nonlinear uncertain systems with delayed output but also maintains a good tracking performance. Finally, some simulation results are illustrated to verify the effectiveness of the proposed control approach.

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Robust adaptive fuzzy controller with supervisory compensator for MEMS gyroscope sensor

Robotica ◽

10.1017/s026357471400294x ◽

2015 ◽

Vol 34 (10) ◽

pp. 2330-2343 ◽

Cited By ~ 1

Author(s):

Yunmei Fang ◽

Jian Zhou ◽

Juntao Fei

Keyword(s):

Fuzzy Inference ◽

Fuzzy Controller ◽

Adaptive Controller ◽

State Variables ◽

Inference System ◽

Mems Gyroscope ◽

Adaptive Fuzzy Controller ◽

Adaptive Fuzzy ◽

Robust Adaptive ◽

Gyroscope Sensor

SUMMARYIn this paper, a robust adaptive fuzzy controller is proposed to improve the robustness and position tracking of a MEMS gyroscope sensor. The proposed controller is designed as an indirect adaptive fuzzy controller with a supervisory compensator. It incorporates a fuzzy inference system with an adaptive controller in a unified Lyapunov framework, which can approximate and compensate for the unknown system dynamics and nonlinearities in the MEMS gyroscope. The parameters of the membership functions in the fuzzy controller can be adjusted online based on the Lyapunov method. Moreover, a supervisory controller is employed to guarantee the asymptotic stability of the closed-loop system and boundedness of the state variables in the MEMS gyroscope model. Numerical simulations demonstrate the proposed robust adaptive fuzzy controller has satisfactory tracking performance and robustness in the presence of external disturbances.

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Robust adaptive fuzzy control application to uncertain non-linear systems in robotics

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ◽

10.1177/095965180221600302 ◽

2002 ◽

Vol 216 (3) ◽

pp. 225-235 ◽

Cited By ~ 2

Author(s):

A Hamzaoui ◽

A Elkari ◽

J Zaytoon

Keyword(s):

Fuzzy Control ◽

High Performance ◽

Fuzzy Controller ◽

Adaptive Fuzzy Control ◽

Adaptive Fuzzy ◽

Non Linear ◽

Robust Adaptive ◽

The One ◽

Non Linear System ◽

Control Application

In this paper the problem of tracking of a non-linear system in the presence of external disturbances is analysed. An adaptive fuzzy controller is proposed to improve performance of an H∞ tracking problem. The proposed approach benefits from the high performance of H∞ on the one hand, and the intuitive ‘smart’ approach of fuzzy control on the other. An example of a two-link manipulator is used for illustration.

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