City University of Industry

Ho Chi Minh City, Vietnam

City University of Industry

Ho Chi Minh City, Vietnam
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Long M.T.,Hunan University | Long M.T.,City University of Industry | Nan W.Y.,Hunan University
Journal of Intelligent and Robotic Systems: Theory and Applications | Year: 2015

In this paper, we propose an adaptive position tracking system and a force control strategy for nonholonomic mobile robot manipulators, which incorporate the merits of Fuzzy Wavelet Neural Networks (FWNNs). In general, it is not easy to adopt a model-based method to achieve this control object due to the uncertainties of mobile robot manipulators control system, such as unknown dynamics, disturbances and parameter variations. To solve this problem, an adaptive FWNNs control scheme with the online learning ability is utilized to approximate the unknown dynamics without the requirement of prior system information. In addition, an adaptive robust compensator is proposed to eliminate uncertainties that consist of approximation errors, disturbances, optimal parameters and higher order terms in Taylor series. According to adaptive position tracking control design, an adaptive robust control strategy is also considered for nonholonomic constraint force. The design of adaptive online learning algorithms is derived using Lyapunov stability theorem. Therefore, the proposed controllers prove that they not only can guarantee the stability of mobile robot manipulators control system but also guarantee tracking performance. The effectiveness and robustness of the proposed method are demonstrated by comparing simulations and experimental results that are implemented in an indoor cleaning crawler-type mobile robot manipulators system. © 2014, Springer Science+Business Media Dordrecht.

Mai L.T.,Hunan University | Mai L.T.,City University of Industry | Wang N.Y.,Hunan University
Kybernetes | Year: 2014

Purpose: The purpose of this paper is to improve the flexibility and tracking errors of the controllers-based neural networks (NNs) for mobile manipulator robot (MMR) in the presence of time-varying uncertainties. Design/methodology/approach: The conventional backstepping force/motion control is developed by the wavelet fuzzy CMAC neural networks (WFCNNs) (for mobile-manipulator robot). The proposed WFCNNs are applied in the tracking-position-backstepping controller to deal with the uncertain dynamics of the controlled system. In addition, an adaptive robust compensator is proposed to eliminate the inevitable approximation errors, uncertain disturbances, and relax the requirement for prior knowledge of the controlled system. Besides, the position tracking controller, an adaptive robust constraint-force is also considered. The online-learning algorithms of the control parameters (WFCNNs, robust term and constraint-force controller) are obtained by using the Lyapunov stability theorem. Findings: The design of the proposed method is determined by the Lyapunov theorem such that the stability and robustness of the control-system are guaranteed. Originality/value: The WFCNNs are more the generalized networks that can overcome the constant out-weight problem of the conventional fuzzy cerebellar model articulation controller (FCMAC), or can converge faster, give smaller approximation errors and size of networks in comparison with FNNs/NNs. In addition, an intelligent-control system by inheriting the advantage of the conventional backstepping-control-system is proposed to achieve the high-position tracking for the MMR control system in the presence of uncertainties variation. © Emerald Group Publishing Limited.

Ngo T.,Hunan University | Ngo T.,City University of Industry | Wang Y.,Hunan University | Wang Y.,City University of Industry | And 6 more authors.
International Journal of Computers, Communications and Control | Year: 2012

This paper presents a robust adaptive neural-fuzzy network control (RANFNC) system for an n-link robot manipulator to achieve the highprecision position tracking. Initially, the model dynamic of an n-link robot manipulator is introduced. However, it is difficult to design a conformable model-based control scheme, for instance, external disturbances, friction forces and parameter variations. In order to deal with this problem, the RANFNC system is investigated to the joint position control of an n-link robot manipulator. In this control scheme, a four-layer neural-fuzzy-network (NFN) is used for the main role, and the adaptive tuning laws of network parameters are derived in the sense of a projection algorithm and the Lyapunov stability theorem to ensure network convergence as well as stable control performance. The merits of this model-free control scheme are that not only the stable position tracking performance can be guaranteed but also unknown system information and auxiliary control design are required in the control process. The simulation results are provided to verify the effectiveness of the proposed RANFNC methodology. © 2006-2012 by CCC Publications.

Ngo T.,City University of Industry | Nguyen M.H.,City University of Industry | Wang Y.,Hunan University | Ge J.,Hunan University | And 2 more authors.
International Journal of Computers, Communications and Control | Year: 2012

In this paper, adaptive iterative learning control (AILC) of uncertain robot manipulators in task space is considered for trajectory tracking in an iterative operation mode. The control scheme incluces a PD controller with a gain switching technique plus a learning feedforward term, is exploited to predict the desired actuator torque. By using Lyapunov method, an adaptive iterative learning control scheme is presented for robotic system with both structured and unstructured uncertainty, and the overall stability of the closed-loop system in the iterative domain is established. The validity of the scheme is confirmed through a numerical simulation. © 2006-2012 by CCC Publications.

Ngo T.,Hunan University | Ngo T.,City University of Industry | Wang Y.,Hunan University
International Journal of Advanced Robotic Systems | Year: 2011

This paper represents a self-structured organizing single-input control system based on differentiable cerebellar model articulation controller (CMAC) for an n-link robot manipulator to achieve the high-precision position tracking. In the proposed scheme, the single-input CMAC controller is solely used to control the plant, so the input space dimension of CMAC can be simplified and no conventional controller is needed. The structure of single-input CMAC will also be self-organized; that is, the layers of single-input CMAC will grow or prune systematically and their receptive functions can be automatically adjusted. The online tuning laws of single-input CMAC parameters are derived in gradient-descent learning method and the discrete-type Lyapunov function is applied to determine the learning rates of proposed control system so that the stability of the system can be guaranteed. The simulation results of robot manipulator are provided to verify the effectiveness of the proposed control methodology. © SAGE Publications Ltd, unless otherwise noted. Manuscript content on this site is licensed under Creative Commons Licenses.

Le-Tien T.,Ho Chi Minh City University of Technology | Le-Cao K.,Ho Chi Minh City University of Technology | Bui-Thu C.,City University of Industry
International Conference on Advanced Technologies for Communications | Year: 2013

This paper copes with the problem of improving the quality of the curvelet interpolation in super-resolution image reconstruction. The curvelet interpolation has been proposed by some authors, however the quality of reconstructed images from their implementation is not as high as expected and the processing time is also not efficient. To improve the curvelet interpolation, a 2-stage interpolation algorithm in the curvelet domain combined to a filtering step for the reconstruction is proposed. The interpolated images are compared with images provided by other previous High-Resolution reconstruction methods and to the ideal interpolation. The experiments in Matlab and hardware based approach show the appropriate improvements of PSNR and MSE in comparison with the other previous methods. © 2013 IEEE.

Luy N.T.,City University of Industry | Thanh N.T.,City University of Technology | Tri H.M.,Ho Chi Minh City University of Technology
Proceedings of the 2013 IEEE Workshop on Robotic Intelligence in Informationally Structured Space, RiiSS 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 | Year: 2013

This paper proposes a new method based on reinforcement learning to design robust adaptive tracking control laws with optimality for multi-wheeled mobile robots synchronization in communication graph without requiring knowledge of drift tracking terms in node dynamics. Wheeled mobile robots are controlled by integrated kinematic and dynamic laws. Actor critic structures in the control scheme for every node is proposed such as only single NN is used to reduce computational cost and storage resources, but parameters of critic and actors are updated synchronously. Novel tuning laws for the NNs are designed not only to learn online adaptive solutions of cooperative Hamilton-Jacobi-Isaacs (HJI) equation on purpose of approximating optimal cooperative tracking performance index functions and robust direct adaptive tracking control inputs as well as worst case disturbances but also to guarantee closed-loop stability in real-time. The convergence and stability of the overall system are proven by Lyapunov techniques. The simulation results on multi-wheeled mobile robots systems verify the effectiveness of the proposed controller. © 2013 IEEE.

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