An adaptive fuzzy controller based on sliding mode for robot manipulators

1999 ◽  
Vol 29 (5) ◽  
pp. 661-667 ◽  
Author(s):  
F.C. Sun ◽  
Z.Q. Sun ◽  
G. Feng
Keyword(s):  
Fuzzy Controller ◽  
Sliding Mode ◽  
Robot Manipulators ◽  
Adaptive Fuzzy Controller ◽  
Adaptive Fuzzy

scholarly journals Development of C-Means Clustering Based Adaptive Fuzzy Controller for a Flapping Wing Micro Air Vehicle

2019 ◽  
Vol 9 (2) ◽  
pp. 99-109 ◽  
Author(s):  
Md Meftahul Ferdaus ◽  
Sreenatha G. Anavatti ◽  
Matthew A. Garratt ◽  
Mahardhika Pratama
Keyword(s):  
Clustering Algorithm ◽  
Fuzzy Controller ◽  
Sliding Mode ◽  
Flapping Wing ◽  
Micro Air Vehicle ◽  
Wind Gust ◽  
Adaptive Fuzzy Controller ◽  
Adaptive Fuzzy ◽  
Air Vehicle ◽  
Fcm Clustering

Abstract Advanced and accurate modelling of a Flapping Wing Micro Air Vehicle (FW MAV) and its control is one of the recent research topics related to the field of autonomous MAVs. Some desiring features of the FW MAV are quick flight, vertical take-off and landing, hovering, and fast turn, and enhanced manoeuvrability contrasted with similar-sized fixed and rotary wing MAVs. Inspired by the FW MAV’s advanced features, a four-wing Nature-inspired (NI) FW MAV is modelled and controlled in this work. The Fuzzy C-Means (FCM) clustering algorithm is utilized to construct the data-driven NIFW MAV model. Being model free, it does not depend on the system dynamics and can incorporate various uncertainties like sensor error, wind gust etc. Furthermore, a Takagi-Sugeno (T-S) fuzzy structure based adaptive fuzzy controller is proposed. The proposed adaptive controller can tune its antecedent and consequent parameters using FCM clustering technique. This controller is employed to control the altitude of the NIFW MAV, and compared with a standalone Proportional Integral Derivative (PID) controller, and a Sliding Mode Control (SMC) theory based advanced controller. Parameter adaptation of the proposed controller helps to outperform it static PID counterpart. Performance of our controller is also comparable with its advanced and complex counterpart namely SMC-Fuzzy controller.

scholarly journals Uncertain Fractional Order Chaotic Systems Tracking Design via Adaptive Hybrid Fuzzy Sliding Mode Control

2011 ◽  
Vol 6 (3) ◽  
pp. 418 ◽  
Author(s):  
Tsung-Chih Lin ◽  
Chia-Hao Kuo ◽  
Valentina E. Balas
Keyword(s):  
Sliding Mode Control ◽  
Fractional Order ◽  
Chaotic Systems ◽  
Fuzzy Controller ◽  
Sliding Mode ◽  
Tracking Error ◽  
Adaptive Fuzzy Controller ◽  
Adaptive Fuzzy ◽  
Mode Control ◽  
And Control

In this paper, in order to achieve tracking performance of uncertain fractional order chaotic systems an adaptive hybrid fuzzy controller is proposed. During the design procedure, a hybrid learning algorithm combining sliding mode control and Lyapunov stability criterion is adopted to tune the free parameters on line by output feedback control law and adaptive law. A weighting factor, which can be adjusted by the trade-off between plant knowledge and control knowledge, is adopted to sum together the control efforts from indirect adaptive fuzzy controller and direct adaptive fuzzy controller. To confirm effectiveness of the proposed control scheme, the fractional order chaotic response system is fully illustrated to track the trajectory generated from the fractional order chaotic drive system. The numerical results show that tracking error and control effort can be made smaller and the proposed hybrid intelligent control structure is more flexible during the design process.

Sign in / Sign up

Export Citation Format

Share Document