Robust robot control enhanced by a hierarchical adaptive fuzzy algorithm
01 Mar 2004-Engineering Applications of Artificial Intelligence (Pergamon)-Vol. 17, Iss: 2, pp 187-198
TL;DR: The main contribution of this work is the derivation of the hierarchical adaptive fuzzy algorithm to avoid the rule explosion phenomenon that characterizes traditional fuzzy systems.
About: This article is published in Engineering Applications of Artificial Intelligence.The article was published on 2004-03-01. It has received 23 citations till now. The article focuses on the topics: Adaptive control & Fuzzy control system.
Citations
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TL;DR: In this paper, a novel fuzzy structure termed as the decomposed fuzzy system (DFS) is proposed to act as the fuzzy approximator for adaptive fuzzy control systems and can be seen that the simplified DFS can perform fairly with a more concise decomposition structure.
Abstract: In this paper, a novel fuzzy structure termed as the decomposed fuzzy system (DFS) is proposed to act as the fuzzy approximator for adaptive fuzzy control systems. The proposed structure is to decompose each fuzzy variable into layers of fuzzy systems, and each layer is to characterize one traditional fuzzy set. Similar to forming fuzzy rules in traditional fuzzy systems, layers from different variables form the so-called component fuzzy systems. DFS is proposed to provide more adjustable parameters to facilitate possible adaptation in fuzzy rules, but without introducing a learning burden. It is because those component fuzzy systems are independent so that it can facilitate minimum distribution learning effects among component fuzzy systems. It can be seen from our experiments that even when the rule number increases, the learning time in terms of cycles is still almost constant. It can also be found that the function approximation capability and learning efficiency of the DFS are much better than that of the traditional fuzzy systems when employed in adaptive fuzzy control systems. Besides, in order to further reduce the computational burden, a simplified DFS is proposed in this paper to satisfy possible real time constraints required in many applications. From our simulation results, it can be seen that the simplified DFS can perform fairly with a more concise decomposition structure.
59 citations
Cites methods from "Robust robot control enhanced by a ..."
...Such a fuzzy system has also been employed in the adaptive fuzzy control system design as presented in [18]....
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TL;DR: In this article, a mixed fuzzy controller (MFC) is proposed to solve the problem of nonlinear dynamics coupling in MIMO systems and improve the control performance of the MFC.
31 citations
TL;DR: In this study, an exoskeleton type robot-assisted rehabilitation system, called RehabRoby, is developed for rehabilitation purposes and a control architecture, which contains a high- level controller and a low-level controller, is designed so that Rehab Roby can complete the given rehabilitation task in a desired and safe manner.
Abstract: In this study, an exoskeleton type robot-assisted rehabilitation system, called RehabRoby, is developed for rehabilitation purposes. A control architecture, which contains a high-level controller and a low-level controller, is designed so that RehabRoby can complete the given rehabilitation task in a desired and safe manner. A hybrid system modelling technique is used for the high-level controller. An admittance control with an inner robust position control loop is used for the low-level control of the RehabRoby. Real-time experiments are performed to evaluate the control architecture of the robot-assisted rehabilitation system, RehabRoby. Furthermore, the usability of RehabRoby is evaluated.
29 citations
Cites methods from "Robust robot control enhanced by a ..."
...Various methods have been used to estimate the disturbance in the position control of robotic systems such as adaptive hierarchical fuzzy algorithms [17] or model-based disturbance attenuation [24]....
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...Impedance control [7],[10],[15], position control [8], [16], [17]and admittance control [9],[10] have previously been used to control robotassisted rehabilitation systems....
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01 Aug 2010
TL;DR: Experimental and simulation results are presented that validate the proposed approach for neuro-fuzzy control of a single-link flexible robot manipulator that uses a computer-aided design (CAD) program.
Abstract: A modelling approach for neuro-fuzzy control of a single-link flexible robot manipulator that uses a computer-aided design (CAD) program is proposed. Initially, a CAD model of the flexible link is created using experimentally determined values of system parameters. This CAD model is then exported to MATLAB software and the Simulink/SimMechanics toolbox. An adaptive-network-based fuzzy logic controller is used for position and vibration control of the flexible link.Experimental and simulation results are presented that validate the proposed approach.
27 citations
TL;DR: In this paper, a hybrid fuzzy-logic and neural-network controller (HFNC) was developed for multiple-input multiple-output (MIMO) systems, which consists of a fuzzy logic controller (FLC) which was designed to control each degree of freedom (DOF) of a MIMO system individually and an additional coupling neural network which was incorporated into the FLC to compensate for the dynamic coupling effects between each DOF of the system.
20 citations
References
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"Robust robot control enhanced by a ..." refers methods in this paper
...If the function to be approximated is time varying or if the approximator needs training, adaptive fuzzy techniques may be used (Wang, 1994)....
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