A Fuzzy Dynamic Uncertainty Compensator for Industrial Robots

Chen, Wuwei; Mills, James K.; Chu, Jiaxin; Sun, Dong

doi:10.1016/s1474-6670(17)32939-7

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…For this case, we use the adaptive control law defined by Eq. (15). Figure 4 shows the trajectory tracking of each joint.…”

Section: Resultsmentioning

confidence: 99%

“…An adaptive fuzzy controller with backstepping approach [2,14] is designed to deal with the trajectory tracking of WMR. To track the trajectory accurately, a fuzzy compensator [15,16] is added to counteract the modeling certainty such as friction and external disturbance, and a robust term is introduced to reduce the approximation error and ensure the system stability.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Trajectory Tracking of Wheeled Mobile Robot with a Manipulator Considering Dynamic Interaction and Modeling Uncertainty

Zhong¹,

Kobayashi²,

Emaru³

et al. 2012

Intelligent Robotics and Applications

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Abstract. This paper proposes an adaptive control strategy for trajectory tracking of a Wheeled Mobile Robot (WMR) which consists of a suspended platform and a manipulator. When the WMR moves in the presence of friction and external disturbance, the trajectory can hardly be tracked accurately by applying the backstepping approach. For addressing this problem, considering the dynamic interaction, a dynamic model of the system is constructed by using Direct Path Method (DPM). An adaptive fuzzy control combined with backstepping approach based on the dynamic model is proposed. To track the trajectory accurately, a fuzzy compensator is proposed to compensate modeling uncertainty such as friction and external disturbance. Moreover, to reduce the approximation error and ensure the system stability, a robust term is added to the adaptive control law. Simulation results show the effectiveness and merits of the proposed control strategy in the counteraction of modeling uncertainty and the trajectory tracking.

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“…For this case, we use the adaptive control law defined by Eq. (15). Figure 4 shows the trajectory tracking of each joint.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking of Wheeled Mobile Robot with a Manipulator Considering Dynamic Interaction and Modeling Uncertainty

Zhong¹,

Kobayashi²,

Emaru³

et al. 2012

Intelligent Robotics and Applications

View full text Add to dashboard Cite

show abstract

“…Although the fuzzy control part in the approach can approximate uncertainties in robotic manipulator, parameters in the fuzzy control part need to be determined and tuned by a complex Lyapunov equation, which will result in a complex controller design. Chen [18] combined the conventional computed-torque control with a fuzzy logic system to improve trajectory tracking performance of an industrial robot. However, because the fuzzy logic rules in the fuzzy control system are determined by some experimental data and designers' experiences rather than the fuzzy adaptive control law, it is very difficult to ensure stability for the general fuzzy control systems with uncertain dynamics.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Computed-Torque Control for Robot Manipulator with Uncertain Dynamics

Chen

Lin

et al. 2012

International Journal of Advanced Robotic Systems

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To overcome the disadvantages of conventional computed-torque control and fuzzy control, and to exploit their attractive features, this paper proposes two types of adaptive control scheme combining conventional computed-torque control and different fuzzy compensators for the robust tracking control of robotic manipulators with structured and unstructured uncertainties. Fuzzy compensators based on feed-forward and feed-back are developed to compensate these uncertain dynamics. On the basis of Lyapunov stability theory, a tracking error limit is derived for the closed-loop control system and the convergence and stability of the control schemes are proved. Comparisons of their performances with conventional computed-torque controllers under the condition of these uncertainties are carried out. The validity of the two types of adaptive control scheme is shown by numerical simulations of a three-link rotary robot manipulator.

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Near-Optimal Fuzzy Systems Using Polar Clustering: Application to Control of Vision-Based Arm-Robot

Kim

Lim

2005

Lecture Notes in Computer Science

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A Fuzzy Dynamic Uncertainty Compensator for Industrial Robots

Cited by 4 publications

References 7 publications

Trajectory Tracking of Wheeled Mobile Robot with a Manipulator Considering Dynamic Interaction and Modeling Uncertainty

Trajectory Tracking of Wheeled Mobile Robot with a Manipulator Considering Dynamic Interaction and Modeling Uncertainty

Adaptive Fuzzy Computed-Torque Control for Robot Manipulator with Uncertain Dynamics

Near-Optimal Fuzzy Systems Using Polar Clustering: Application to Control of Vision-Based Arm-Robot

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