Semi-decentralized adaptive fuzzy control for cooperative multirobot systems with H/sup ∞/ motion/internal force tracking performance

2002 ◽  
Vol 32 (3) ◽  
pp. 269-280 ◽  
Author(s):  
Kuang-Yow Lian ◽  
Chian-Song Chiu ◽  
P. Liu
Keyword(s):  
Fuzzy Control ◽  
Adaptive Fuzzy Control ◽  
Internal Force ◽  
Tracking Performance ◽  
Multirobot Systems ◽  
Adaptive Fuzzy ◽  
Force Tracking

H ∞ Based Adaptive Fuzzy Control of a Tower Crane System

2012 ◽  
Author(s):  
Mansour Karkoub ◽  
Tzu Sung Wu ◽  
Chien Ting Chen
Keyword(s):  
Fuzzy Control ◽  
Time Delays ◽  
Adaptive Fuzzy Control ◽  
Tracking Performance ◽  
Control Technique ◽  
Control Law ◽  
External Disturbances ◽  
Adaptive Fuzzy ◽  
Tower Crane ◽  
Highly Nonlinear

Tower cranes are very complex mechanical systems and have been the subject of research investigations for several decades. Research on tower cranes has focused on the development of dynamical models (linear and nonlinear) as well as control techniques to reduce the swaying of the payload. Inherently, the dynamical model of the tower crane is highly nonlinear and classified as under-actuated. The crane system has potentially six degrees of freedom but only three actuators. Also, the actuators are far from the payload which makes the system non-colocated. The dynamic model describing the motion of the payload from point to point is affected by uncertainties, time delays and external disturbances which may lead to inaccurate positioning, reduce safety and efficacy of the overall system. It is proposed here to use an H∞ based adaptive fuzzy control technique to control the swaying motion of a tower crane. This technique will overcome modeling inaccuracies, such as drag and friction losses, effect of time delayed disturbances, as well as parameter uncertainties. The proposed control law for payload positioning is based on indirect adaptive fuzzy control. A fuzzy model is used to approximate the dynamics of the tower crane; then, an indirect adaptive fuzzy scheme is developed for overriding the nonlinearities and time delays. The advantage of employing an adaptive fuzzy system is the use of linear analytical results instead of estimating nonlinear system functions with an online update law. The adaptive fuzzy scheme fuses a Variable Structure (VS) scheme to resolve the system uncertainties, and the external disturbances such that H∞ tracking performance is achieved. A control law is derived based on a Lyapunov criterion and the Riccati-inequality to compensate for the effect of the external disturbances on tracking error so that all states of the system are uniformly ultimately bounded (UUB). Therefore, the effect can be reduced to any prescribed level to achieve H∞ tracking performance. Simulations are presented here to illustrate the performance of the proposed control design.

scholarly journals Flatness-Based Adaptive Fuzzy Control Of Spark-Ignited Engines

2014 ◽  
Vol 4 (4) ◽  
pp. 231-242 ◽  
Author(s):  
Gerasimos G. Rigatos ◽  
P. Siano
Keyword(s):  
Fuzzy Control ◽  
Fuzzy Controller ◽  
Tracking Error ◽  
Adaptive Fuzzy Control ◽  
Performance Criterion ◽  
Tracking Performance ◽  
System Stability ◽  
Adaptive Fuzzy ◽  
Neuro Fuzzy ◽  
Control Scheme

Abstract An adaptive fuzzy controller is designed for spark-ignited (SI) engines, under the constraint that the system's model is unknown. The control algorithm aims at satisfying the H∞ tracking performance criterion, which means that the influence of the modeling errors and the external disturbances on the tracking error is attenuated to an arbitrary desirable level. After transforming the SI-engine model into the canonical form, the resulting control inputs are shown to contain nonlinear elements which depend on the system's parameters. The nonlinear terms which appear in the control inputs are approximated with the use of neuro-fuzzy networks. It is shown that a suitable learning law can be defined for the aforementioned neuro-fuzzy approximators so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis it is proven that the proposed adaptive fuzzy control scheme results in H∞ tracking performance. The efficiency of the proposed adaptive fuzzy control scheme is checked through simulation experiments.

Indirect adaptive fuzzy control of nonlinear descriptor systems

2020 ◽  
Vol 51 ◽  
pp. 30-38 ◽  
Author(s):  
Naeimeh Fakhr Shamloo ◽  
Ali Akbarzadeh Kalat ◽  
Luigi Chisci
Keyword(s):  
Fuzzy Control ◽  
Adaptive Fuzzy Control ◽  
Descriptor Systems ◽  
Adaptive Fuzzy
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