Robust Adaptive Fuzzy Control for Robot Manipulator with Uncertain Dynamics

2013 ◽  
Vol 415 ◽  
pp. 267-270 ◽  
Author(s):  
Yuan Chen ◽  
Yun Jun Bi ◽  
Jun Gao
Keyword(s):  
Fuzzy Control ◽  
Control Algorithm ◽  
Robot Manipulator ◽  
Adaptive Fuzzy Control ◽  
Control Model ◽  
Adaptive Fuzzy ◽  
Uncertain Dynamics ◽  
Control Scheme ◽  
Robust Adaptive ◽  
Fuzzy Control Algorithm

A robust adaptive fuzzy control scheme combined with an adaptive fuzzy control algorithm and H∞ control model is proposed for the trajectory tracking control of robot manipulator with structured and unstructured uncertainties. The adaptive fuzzy control algorithm is employed to approximate structured uncertainties, and the nonlinear robust H control model is designed to eliminate the effect of the unstructured uncertainties and approximation errors. The validity of the robust adaptive fuzzy control scheme is demonstrated by numerical simulations of a two-link rotary robot manipulator.

scholarly journals Robust Adaptive Fuzzy Control via State-Dependent Function for Nonlinear Stochastic Large-Scale Systems Subject to Dead Zones

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Chang-Qi Zhu ◽  
Lei Liu
Keyword(s):  
Fuzzy Control ◽  
Large Scale ◽  
Dead Zone ◽  
Adaptive Fuzzy Control ◽  
Closed Loop System ◽  
Large Scale Systems ◽  
Adaptive Fuzzy ◽  
State Dependent ◽  
Control Scheme ◽  
Robust Adaptive

This paper concentrates on the adaptive fuzzy control problem for stochastic nonlinear large-scale systems with constraints and unknown dead zones. By introducing the state-dependent function, the constrained closed-loop system is transformed into a brand-new system without constraints, which can realize the same control objective. Then, fuzzy logic systems (FLSs) are used to identify the unknown nonlinear functions, the dead zone inverse technique is utilized to compensate for the dead zone effect, and a robust adaptive fuzzy control scheme is developed under the backstepping frame. Based on the Lyapunov stability theory, it is proved ultimately that all signals in the closed-loop system are bounded and the tracking errors converge to a small neighborhood of the origin. Finally, an example based on an actual system is given to verify the effectiveness of the proposed control scheme.

scholarly journals Robust Adaptive Fuzzy Control for a Class of Uncertain MIMO Nonlinear Systems with Input Saturation

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Shenglin Wen ◽  
Ye Yan
Keyword(s):  
Nonlinear Systems ◽  
Fuzzy Control ◽  
Adaptive Fuzzy Control ◽  
Design Parameters ◽  
Tracking Errors ◽  
External Disturbances ◽  
Mimo Nonlinear Systems ◽  
Adaptive Fuzzy ◽  
Control Scheme ◽  
Robust Adaptive

This paper studies the robust adaptive fuzzy control design problem for a class of uncertain multiple-input and multiple-output (MIMO) nonlinear systems in the presence of actuator amplitude and rate saturation. In the control scheme, fuzzy logic systems are used to approximate unknown nonlinear systems. To compensate the effect of input saturations, an auxiliary system is constructed and the actuator saturations then can be augmented into the controller. The modified tracking error is introduced and used in fuzzy parameter update laws. Furthermore, in order to deal with fuzzy approximation errors for unknown nonlinear systems and external disturbances, a robust compensation control is designed. It is proved that the closed-loop system obtainsH∞tracking performance through Lyapunov analysis. Steady and transient modified tracking errors are analyzed and the bound of modified tracking errors can be adjusted by tuning certain design parameters. The proposed control scheme is applicable to uncertain nonlinear systems not only with actuator amplitude saturation, but also with actuator amplitude and rate saturation. Detailed simulation results of a rigid body satellite attitude control system in the presence of parametric uncertainties, external disturbances, and control input constraints have been presented to illustrate the effectiveness of the proposed control scheme.

Flow Control Strategy for Novel Layered Synchronous Water Injection Technology in Oilfield

2012 ◽  
Vol 424-425 ◽  
pp. 737-741
Author(s):  
De Li Jia ◽  
Feng Shan Wang ◽  
Shu Jin Zhang ◽  
Ming Xin Zhao
Keyword(s):  
Fuzzy Control ◽  
Flow Control ◽  
Control Strategy ◽  
Control Algorithm ◽  
Water Injection ◽  
Adaptive Fuzzy Control ◽  
Adaptive Fuzzy ◽  
Variable Universe ◽  
Injection Technology ◽  
Fuzzy Control Algorithm

The layered synchronous water injection technology in oilfield has such characteristics as strong nonlinearity, interlayer interference, time variation and complex model. The traditional control strategy based on the model often causes system unstable and regulation time too long, thereby making control effect unsatisfactory. To satisfy the requirements of layered synchronous water injection technology, this paper designs a flow controller based on adaptive fuzzy control algorithm with variable universe. The universe will change with variables parameters by adjusting extension-contraction factor. The experimental verification show that the system has good control performance, strong adaptability and short measurement and regulation time with the introduction of variable universe adaptive fuzzy control algorithm into flow control system of layered synchronous water injection technology

scholarly journals A Novel Adaptive Fuzzy MPPT Algorithm under Changing Atmospheric Conditions

2021 ◽  
Vol 3 (4) ◽  
pp. 246-264
Author(s):  
Zhang Yan ◽  
Wang Ya-Jun ◽  
Chang Jia-Bao
Keyword(s):  
Fuzzy Control ◽  
Control Algorithm ◽  
Adaptive Fuzzy Control ◽  
Optimal Time ◽  
Atmospheric Conditions ◽  
Change Rate ◽  
Adaptive Fuzzy ◽  
Slope Change ◽  
Steady State Oscillation ◽  
Fuzzy Control Algorithm

The paper aims at the incompatibility between the speed and stability of the traditional MPPT algorithm and the imprecise search of the fuzzy control algorithm. An improved photovoltaic adaptive fuzzy control MPPT algorithm is proposed in this thesis. The solar irradiance changes dramatically and hence four kinds of fuzzy control algorithms with different input are modeled and simulated. The results indicate that the proposed fuzzy control algorithm using slope and slope change rate of P-U curve as input is the best. On this basis, dP/dU and duty cycle D(n-1) at n-1 moment are used as input to improve the tracking speed and optimal range. At the same time using shrinkage factor 1/I*|dP/dU| real-time adjustment of D(n-1) further shortens the optimal time of the algorithm. The algorithm is simulated and applied in a block. Simulation results show that the proposed algorithm is superior to the fuzzy control algorithm in steady-state oscillation rate, tracking speed and efficiency, and the algorithm is simple and easy to implement.

Adaptive Fuzzy Vibration Control of Smart Structure with VFIFE Modeling

2015 ◽  
Vol 31 (6) ◽  
pp. 671-682 ◽  
Author(s):  
R. Xu ◽  
D.-X. Li ◽  
J.-P. Jiang ◽  
W. Liu
Keyword(s):  
Fuzzy Control ◽  
Vibration Control ◽  
Control Algorithm ◽  
Adaptive Fuzzy Control ◽  
Smart Structure ◽  
Torsional Vibrations ◽  
Sensors And Actuators ◽  
Antenna Structure ◽  
Adaptive Fuzzy ◽  
Fuzzy Control Algorithm

ABSTRACTThe vibration control of smart structure is considered in this paper. Membrane SAR antenna structure with piezoelectric sensors and actuators is taken as an example. The dynamic model is build up based on vector form intrinsic finite element (VFIFE) method. The four nodes membrane element, sensor element and actuator element for VFIFE are presented. By decentralized control stratagem, the bending and torsional vibrations of the membrane SAR antenna can be decoupled on measurement and driving control. The fuzzy control and adaptive fuzzy control are applied to suppress the bending and torsional vibrations of the membrane SAR structure. In the numerical experiment section, form finding is first carried out, then vibration control simulations are studied. The results demonstrate that adaptive fuzzy control algorithm can suppress the vibrations more effectively than the fuzzy control algorithm.

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