Adaptive Fuzzy Dynamic Surface Sliding Mode Position Control for a Robot Manipulator with Friction and Deadzone

Cheong, Jeong Yun; Han, Seong-Ik; Lee, Jang Myung

doi:10.1155/2013/161325

Cited by 11 publications

(9 citation statements)

References 37 publications

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“…From [8–10, 12, 13, 34–36], the actuators of robotic systems are symmetric which is denoted as

n_{normalr} = n_{normall}

in (5); consequently, this behaviour can be rewritten as follows:

\begin{matrix} right left right left right left right left right left right left0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em3pt \\ \begin{matrix} v = D Z false(w false) = ({, \begin{array}{cc} n_{normalr} ()(, w - a_{normalr}) & w \geq a_{normalr} \\ 0 & a_{normall} < w < a_{normalr} \\ n_{normalr} ()(, w - a_{normall}) & w \leq a_{normall} \end{array}), \\ false⟹ v = ({, \begin{array}{cc} n_{normalr} ()(, w - a_{normalr}) & w \geq a_{normalr} \\ n_{normalr} ()(, w - w) & a_{normall} < w < a_{normalr} \\ n_{normalr} ()(, w - a_{normall}) & w \leq a_{normall} \end{array}), \\ false⟹ v = ({, \begin{array}{cc} n_{normalr} w & w \geq a_{normalr} \\ n_{normalr} w & a_{normall} < w < a_{normalr} \\ n_{normalr} w & w \leq a_{normall} \end{array}) + ({, \begin{array}{cc} - n_{normalr} a_{normalr} & w \geq a_{normalr} \\ - n_{norm...} \end{array}) \end{matrix} \end{matrix}

…”

Section: Sliding Mode Control For the Regulation Of Robotic Arms Wimentioning

confidence: 99%

See 1 more Smart Citation

Sliding mode control of robotic arms with deadzone

Rubio

2017

IET Control Theory & Applications

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“…From [8–10, 12, 13, 34–36], the actuators of robotic systems are symmetric which is denoted as

n_{normalr} = n_{normall}

in (5); consequently, this behaviour can be rewritten as follows:

\begin{matrix} right left right left right left right left right left right left0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em3pt \\ \begin{matrix} v = D Z false(w false) = ({, \begin{array}{cc} n_{normalr} ()(, w - a_{normalr}) & w \geq a_{normalr} \\ 0 & a_{normall} < w < a_{normalr} \\ n_{normalr} ()(, w - a_{normall}) & w \leq a_{normall} \end{array}), \\ false⟹ v = ({, \begin{array}{cc} n_{normalr} ()(, w - a_{normalr}) & w \geq a_{normalr} \\ n_{normalr} ()(, w - w) & a_{normall} < w < a_{normalr} \\ n_{normalr} ()(, w - a_{normall}) & w \leq a_{normall} \end{array}), \\ false⟹ v = ({, \begin{array}{cc} n_{normalr} w & w \geq a_{normalr} \\ n_{normalr} w & a_{normall} < w < a_{normalr} \\ n_{normalr} w & w \leq a_{normall} \end{array}) + ({, \begin{array}{cc} - n_{normalr} a_{normalr} & w \geq a_{normalr} \\ - n_{norm...} \end{array}) \end{matrix} \end{matrix}

…”

Section: Sliding Mode Control For the Regulation Of Robotic Arms Wimentioning

confidence: 99%

“…In [7], the adaptive control of a system in the presence of deadzone is described. Tracking positioning in the presence of the deadzone of a robot manipulator actuator is studied in [8]. In [9], a robust compensation scheme of a robot manipulator with deadzone is designed.…”

Section: Introductionmentioning

confidence: 99%

Sliding mode control of robotic arms with deadzone

Rubio

2017

IET Control Theory & Applications

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“…As the recurrence relationship is not established between multiple links, it cannot be applied easily to the whole dynamic modeling for robotic manipulators. To overcome inaccuracy of the dynamical model of robotic manipulator, friction, clearance, and external disturbance were considered, and intelligent control strategies have been developed by many researchers for the uncertain manipulator [12][13][14], for example. In this paper, the dynamic equation with recurrence method by using Lagrange energy method is provided as an accurate mathematical model for precision trajectory tracking.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Backstepping Sliding Mode Control of Trajectory Tracking for Robotic Manipulators

Zhu

Puchen

et al. 2020

Complexity

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To achieve precise trajectory tracking of robotic manipulators in complex environment, the precise dynamic model, parameters identification, nonlinear characteristics, and disturbances are the factors that should be solved. Although parameters identification and adaptive estimate method were proposed for robotic control in many literature studies, the essential factors, such as coupling and friction, are rarely mentioned as it is difficult to build the precise dynamic model of the robotic manipulator. An adaptive backstepping sliding mode control is proposed to solve the precise trajectory tracking under external disturbances with complex environment, and the dynamic response characteristics of a two-link robotic manipulator are described in this paper. First, the Lagrange kinetic method is used to derive the precise dynamic model which includes the nonlinear factor with friction and coupling. Moreover, the dynamic model of two-link robotic manipulator is built. Second, the estimate function for the nonlinear part is selected, and backstepping algorithm is used for analyzing the stabilities of the sliding mode controller by using Lyapunov theory. Furthermore, the convergence of the proposed controller is verified subject to the external disturbance. At last, numerical simulation results are reported to demonstrate the effectiveness of the proposed method.

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“…In [20,21], the robust adaptive control methods were used for nonlinear systems with parametric uncertainties subject to the input deal-zone, and the systems must satisfy linear parameterized condition. Recently, in order to deal with unknown nonlinear systems with input dead-zone when the knowledge of system functions is unavailable, many adaptive controllers have been proposed by utilizing universal approximation capability of neural networks or some fuzzy logic systems [21,22]. A robust adaptive NN control design method was proposed in [23] for a kind of strict-feedback nonlinear systems with uncertainties and input dead-zone.…”

Section: Introductionmentioning

confidence: 99%

Neural Prescribed Performance Control for Uncertain Marine Surface Vessels without Accurate Initial Errors

Si¹,

Dong²

2017

Mathematical Problems in Engineering

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This paper deals with the problems concerned with the trajectory tracking control with prescribed performance for marine surface vessels without velocity measurements in uncertain dynamical environments, in the presence of parametric uncertainties, unknown disturbances, and unknown dead-zone. First, only the ship position and heading measurements are available and a high-gain observer is used to estimate the unmeasurable velocities. Second, by utilizing the prescribed performance control, the prescribed tracking control performance can be ensured, while the requirement for the initial error is removed via the preprocessing. At last, based on neural network approximation in combination with backstepping and Lyapunov synthesis, a robust adaptive neural control scheme is developed to handle the uncertainties and input dead-zone characteristics. Under the designed adaptive controller for marine surface vessels, all the signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB), and the prescribed transient and steady tracking control performance is guaranteed. Simulation studies are performed to demonstrate the effectiveness of the proposed method.

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Adaptive Fuzzy Dynamic Surface Sliding Mode Position Control for a Robot Manipulator with Friction and Deadzone

Cited by 11 publications

References 37 publications

Sliding mode control of robotic arms with deadzone

Sliding mode control of robotic arms with deadzone

Adaptive Backstepping Sliding Mode Control of Trajectory Tracking for Robotic Manipulators

Neural Prescribed Performance Control for Uncertain Marine Surface Vessels without Accurate Initial Errors

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