Neuro-adaptive control of mobile manipulators based on compensation of approximation error

Lee, Choon-Young; Eom, Tae-Dok; Lee, Ju-Jang

doi:10.1049/el:20020605

Cited by 13 publications

(16 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The NNs, with the self-learning characteristic, good approximation capability [8,9], are the useful technique for robotic control systems [10][11][12][13]. Lin and Goldenberg [9] proposed an adaptive NNs controller to identify online the dynamics and eliminate disturbances of the MMR control system.…”

Section: Introductionmentioning

confidence: 99%

Adaptive-backstepping force/motion control for mobile-manipulator robot based on fuzzy CMAC neural networks

Long

Wang

2014

Control Theory Technol.

View full text Add to dashboard Cite

In this paper, an adaptive backstepping fuzzy cerebellar-model-articulation-control neural-networks control (ABFCNC) system for motion/force control of the mobile-manipulator robot (MMR) is proposed. By applying the ABFCNC in the tracking-position controller, the unknown dynamics and parameter variation problems of the MMR control system are relaxed. In addition, an adaptive robust compensator is proposed to eliminate uncertainties that consist of approximation errors, uncertain disturbances. Based on the tracking position-ABFCNC design, an adaptive robust control strategy is also developed for the nonholonomicconstraint force of the MMR. The design of adaptive-online learning algorithms is obtained by using the Lyapunov stability theorem. Therefore, the proposed method proves that it not only can guarantee the stability and robustness but also the tracking performances of the MMR control system. The effectiveness and robustness of the proposed control system are verified by comparative simulation results.

show abstract

Section: Introductionmentioning

confidence: 99%

Adaptive-backstepping force/motion control for mobile-manipulator robot based on fuzzy CMAC neural networks

Long

Wang

2014

Control Theory Technol.

View full text Add to dashboard Cite

show abstract

“…Due to its moving base and versatile manipulability, this robotic system has much more potential and flexibility than other types of robots (including fixed-base manipulator and mobile robots). Recently, mobile manipulators have attracted considerable attention in the robotic and control communities [1][2][3][4][5][6][7][8][9][10], and have also been successfully implemented in a variety of tasks [1,10], such as opening doors, handing objects, nursing care, etc. In general, mobile manipulators can be roughly categorized into three classes: (i) mobile manipulators with one arm, (ii) multiple mobile manipulators, and (iii) mobile manipulators with dual arms.…”

Section: Introductionmentioning

confidence: 99%

“…In general, mobile manipulators can be roughly categorized into three classes: (i) mobile manipulators with one arm, (ii) multiple mobile manipulators, and (iii) mobile manipulators with dual arms. Much effort has been focused on the first kind in dealing with the problems of system modeling [2,3], trajectory planning, motion control [4][5][6], etc. A few researchers have paid attention to the second class to investigate some key issues like cooperation [8] and coordination.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, many control strategies (including feedback linearization control (FLC) [3], computed torque control (CTC), robust adaptive control [4], neural network control (NNC) [5,6], etc.) have been developed to resolve the tracking control problems for one-arm mobile manipulators.…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, the main drawback of FLC and CTC is that precise system modeling is critical in the controller design to eliminate the nonlinear dynamic effects; this requirement is impractical because it is impossible to precisely predict the system's physical parameters and to stay away from system uncertainties and external disturbances. Since the uncertainties and time-varying factors have been proven to seriously degrade the system control performance, many control techniques like robust adaptive control [4] and NNC [5,6] have been presented to improve the shortcomings of the aforementioned control methods. In particular, some of the adaptive controllers [4] rely on accurately regressive matrices and also assume that the system uncertainties can be expressed as linear in parameters (LIP).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust Tracking Control For A Wheeled Mobile Manipulator With Dual Arms Using Hybrid Sliding‐Mode Neural Network

Tsai

Cheng

Lin

2007

Asian Journal of Control

View full text Add to dashboard Cite

In this paper, a robust tracking controller is proposed for the trajectory tracking problem of a dual-arm wheeled mobile manipulator subject to some modeling uncertainties and external disturbances. Based on backstepping techniques, the design procedure is divided into two levels. In the kinematic level, the auxiliary velocity commands for each subsystem are first presented. A sliding-mode equivalent controller, composed of neural network control, robust scheme and proportional control, is constructed in the dynamic level to deal with the dynamic effect. To deal with inadequate modeling and parameter uncertainties, the neural network controller is used to mimic the sliding-mode equivalent control law; the robust controller is designed to compensate for the approximation error and to incorporate the system dynamics into the sliding manifold. The proportional controller is added to improve the system's transient performance, which may be degraded by the neural network's random initialization. All the parameter adjustment rules for the proposed controller are derived from the Lyapunov stability theory and e-modification such that uniform ultimate boundedness (UUB) can be assured. A comparative simulation study with different controllers is included to illustrate the effectiveness of the proposed method.

show abstract

Intelligent tracking control of a dual‐arm wheeled mobile manipulator with dynamic uncertainties

Cheng

Tsai

et al. 2012

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

SUMMARYThis paper presents an intelligent control approach that incorporates sliding mode control (SMC) and fuzzy neural network (FNN) into the implementation of back‐stepping control for a path tracking problem of a dual‐arm wheeled mobile manipulator subject to dynamic uncertainties and nonholonomic constraints. By using the back‐stepping technique, the system equations are reformulated into two levels: the kinematic level and the dynamic level. A sliding manifold is constructed by considering the disturbance free kinematic level equations only. With all the system uncertainties concentrated in the dynamic level, an FNN controller associated with a switching type of control law is employed to enforce sliding mode on the prescribed manifold. All parameter adjustment rules for the proposed controller are derived from the Lyapunov theory such that uniform ultimate boundedness for both the tracking error and the FNN weighting updates is ensured. A simulation study, which compares different control design approaches, is included to illustrate the promise of the proposed SMC–FNN method. Copyright © 2012 John Wiley & Sons, Ltd.

show abstract

Neuro-adaptive control of mobile manipulators based on compensation of approximation error

Cited by 13 publications

References 2 publications

Adaptive-backstepping force/motion control for mobile-manipulator robot based on fuzzy CMAC neural networks

Adaptive-backstepping force/motion control for mobile-manipulator robot based on fuzzy CMAC neural networks

Robust Tracking Control For A Wheeled Mobile Manipulator With Dual Arms Using Hybrid Sliding‐Mode Neural Network

Intelligent tracking control of a dual‐arm wheeled mobile manipulator with dynamic uncertainties

Contact Info

Product

Resources

About