Computed-torque plus robust adaptive compensation control for robot manipulator with structured and unstructured uncertainties

This article presents a computed-torque controller plus adaptive fuzzy trajectory feedforward compensator suitable for the trajectory tracking control of uncertain underwater vehicle. To address the issue of unavailable normalization factor, an adaptive fuzzy trajectory feedforward compensator is proposed and assembled at the input trajectory level of the computed-torque controller rather than at the joint drive torque position. The compensator serving as a low-pass filter is implemented outside the inner control loop by adjusting the desired characteristic depth. Due to the nearly unchanged internal control algorithm, the adaptive fuzzy compensator is feasible to implement and is robust when varying the feedback gain in the inner control loop. Moreover, an adaptive dead zone fuzzy compensator is designed to reduce the effect of the dead zone on the actuators of underwater vehicles according to the unknown input dead zone characteristics. To validate the effectiveness of the proposed controller, simulations are conducted for a desired characteristic depth, and the performance of the proposed controller has been compared with conventional controllers to illustrate the usefulness and efficiency of the proposed controller.

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“…B ÀB, and the state vector x = ½ e r _ e r T , the closed-loop error dynamic equation (20) can be rewritten as the following compact form…”

Section: Convergence Proofmentioning

confidence: 99%

“…where Q ø 0. Differentiating V with regard to the closed-loop error dynamic equation (20) results in…”

Section: Proof a Positive Definite Candidate Lyapunov Function Is Dementioning

confidence: 99%

Adaptive fuzzy depth control with trajectory feedforward compensator for autonomous underwater vehicles

Chen

Wang

Chen

2019

Advances in Mechanical Engineering

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“…Other approaches include: Artificial intelligence based position controller (HASAN, 2012;WAI et al, 2010), optimal feedback-linearization control (CHA-TRAEI; ZÁDA, 2012), computed-torque based controller (CHEN et al, 2014;JAHED et al, 2013), adaptive PID controller (DELAVARI et al, 2012), robust control (FATEH; AZARGOSHASB, 2015; PARK; HAN, 2011) and optimal control (OLIVARES; STAFFETTI, 2015).…”

Section: Related Workmentioning

confidence: 99%

Optimal control of a planar robot manipulator based on the Linear Quadratic Inverse-Dynamics design

Mosconi

Siqueira

Fonseca

2019

jme

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To ensure the correct positioning of the end-effector of robot manipulators is one of the most important objectives of the robotic systems control. Lack of reliability in tracking the reference trajectory, as well as in the desired final positioning compromises the quality of the task to be performed, even causing accidents. The purpose of this work was to propose an optimal controller with an inner loop based on the dynamic model of the manipulator and a feedback loop based on the Linear Quadratic Regulator, in order to ensure that the end effector is in the right place, at the right time. The controller was compared to the conventional PID, presenting better performance, both in the transient response, eliminating overshoot, and steady-state, eliminating the stationary error.

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“…The estimation errorx ij ¼ x ij Àx ij , j ¼ 2; 3, is defined. From equations (12), (13), and (15), the estimation error can be presented as…”

Section: Design Of the Meso Compensatormentioning

confidence: 99%

“…The robotic manipulator system (1) with uncertainties is considered. If hðtÞ is bounded, then the control law (8) that combines the CTC and MESO, with equations (13) and (15), can guarantee the GUUB of the closed-loop system.…”

Section: Stability Analysismentioning

confidence: 99%

Model-assisted extended state observer-based computed torque control for trajectory tracking of uncertain robotic manipulator systems

Chen

Zhang

et al. 2018

International Journal of Advanced Robotic Systems

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Computed torque control is an effective control scheme for trajectory tracking of robotic manipulators. However, computed torque control requires precise dynamic models of robotic manipulators and is severely affected by uncertain dynamics. Thus, a new scheme that combines a computed torque control and a novel model-assisted extended state observer is developed for the robust tracking control of robotic manipulators subject to structured and unstructured uncertainties to overcome the disadvantages of computed torque control and exploit its merits. The model-assisted extended state observer is designed to estimate and compensate these uncertain dynamics as a lumped disturbance online, which further improves the disturbance rejection property of a robotic system. Global uniform ultimate boundedness stability with an exponential convergence of a closed-loop system is verified through Lyapunov method. Simulations are performed on a two degree-of-freedom manipulator to verify the effectiveness and superiority of the proposed controller.

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Computed-torque plus robust adaptive compensation control for robot manipulator with structured and unstructured uncertainties

Cited by 12 publications

References 0 publications

Adaptive fuzzy depth control with trajectory feedforward compensator for autonomous underwater vehicles

Adaptive fuzzy depth control with trajectory feedforward compensator for autonomous underwater vehicles

Optimal control of a planar robot manipulator based on the Linear Quadratic Inverse-Dynamics design

Model-assisted extended state observer-based computed torque control for trajectory tracking of uncertain robotic manipulator systems

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