Adaptive Parameter Estimation and Control Design for Robot Manipulators With Finite-Time Convergence

Yang, Chenguang; Jiang, Yiming; He, Wei; Na, Jing; Li, Zhijun; Xu, Bin

doi:10.1109/tie.2018.2803773

Cited by 375 publications

(159 citation statements)

References 29 publications

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“…When the tracking run out of the domain U = X | X ≤ α ρ , the updated laws in control (18) will pull the states back to the sliding surface s, and then the proposed switching controls (18) and (22) can guarantee that all the signals in the closedloop systems are to be GUUB, and all the tracking errors z 1i will went to a small field…”

Section: Theoremmentioning

confidence: 99%

“…For the unknown parameters of robotic systems, adaptive estimation design methods are developed in [16,17]. For uncertain multiagent systems, adaptive NN synchronization controls by using the information of the neighboring agents are established in [18]. A new idea of finite-time quantized feedback control with NNs is addressed for quantized nonlinear systems with unknown states in [19].…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Adaptive Switching Control for an Uncertain Robot Manipulators with Time‐Varying Output Constraint

et al. 2018

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An adaptive control strategy based on a fuzzy logic system by introducing a nonzero time-varying parameter is studied for an n-link manipulator system with the condition of a complex environment. At the beginning, a universal approximator with a one timevarying parameter is proposed based on the analysis of the fuzzy logic system, which is utilized to equalize uncertainties in robot manipulators with time-varying output constraints. The novel design method is used to reduce greatly the online learning computation burden compared with traditional fuzzy adaptive control. The output and the position of robotic manipulators are constrained with time-varying, a good tracking performance can be guaranteed with the condition of unknown dynamics of robot manipulators, and the violation of constraints can be conquered by the analysis based on the barrier Lyapunov function. A switching adaptive control is proposed to extend the semiglobal stability to global stability. Effectiveness of the approach is demonstrated by simulation results.

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Section: Theoremmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy Adaptive Switching Control for an Uncertain Robot Manipulators with Time‐Varying Output Constraint

et al. 2018

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“…Parameters identifications are also be populated for frequently usage in various engineering such as hovercraft system, robot system, vehicle engines, and Wiener system . However, parameters identifications only be suited to some class of known structured model systems in other works . Although some novel adaptive parameter estimation control schemes were constructed for the unknown robotic system in the work of Na et al, the assumption of persistent excitation condition is necessary to be known beforehand.…”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3] For example, in the work of Yang et al, 4 an intelligence shared control system with brain computer interface was developed to achieve accurate object for robot manipulator system, in which the user's mind is regarded as a commander. Parameters identifications are also be populated for frequently usage in various engineering such as hovercraft system, 5 robot system, 6,7 vehicle engines, 8 and Wiener system. 9 However, parameters identifications only be suited to some class of known structured model systems in other works.…”

Section: Introductionmentioning

confidence: 99%

“…9 However, parameters identifications only be suited to some class of known structured model systems in other works. 5,6,8,9 Although some novel adaptive parameter estimation control schemes were constructed for the unknown robotic system in the work of Na et al, 7 the assumption of persistent excitation condition is necessary to be known beforehand. In many real applications, unknown nonlinearities and unstructured uncertainties in complex dynamical systems are inevitable existed; neural network space (NNs) and fuzzy logic systems (FLS) have been received much more attention since they are the two powerful artificial intelligent instruments to deal with uncertainties or unknown model in nonlinear complex dynamic systems.…”

Section: Introductionmentioning

confidence: 99%

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Neural adaptive global stability control for robot manipulators with time‐varying output constraints

Fan

Kang

Wang

et al. 2019

Intl J Robust & Nonlinear

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Summary In this paper, a novel adaptive control scheme is proposed based on radial basis function neural network (RBFNN). The considered system is deduced by the structure of RBFNN with nonzero time‐varying parameter that installed in the fore‐end and terminal of RBFNN. With this structure and the Taylor expansion of any smooth continuous nonlinear function, a universal approximation of RBFNN is addressed according to the analysis of the character of continuous homogenous function and the Euler's theorem. The approximation accuracies can be adjusted online by the nonzero time‐varying parameter in the device with the degree of continuous homogenous function, which expand the semiglobally stability to global stability over conventional neural controller design approaches. Based on the theory analysis of barrier Lyapunov function, the violation of time‐varying constraints can be subjugated without wrecked. Finally, simulation results are carried out to verify the effectiveness by the design methods.

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A method for dynamic parameter identification of an industrial robot based on frequency response function

Li,

Zhao,

Miao

et al. 2024

Int Journal of Mech Sys Dyn

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Having accurate values of the dynamic parameters is necessary to characterize the dynamic behaviors of mechanical systems and for the prediction of their responses. To accurately describe the dynamic characteristics of industrial robots (IRs), a new method for dynamic parameter identification is proposed in this study with the goal of developing a real IR dynamics model that combines the multibody system transfer matrix method (MSTMM) and the nondominated sorting genetic algorithm‐II (NSGA‐II). First, the multibody dynamics model of an IR is developed using the MSTMM, by which its frequency response function (FRF) is calculated numerically. Then, the experimental modal analysis is conducted to measure the IR's actual FRF. Finally, the objective function of the minimum errors between the calculated and measured eigenfrequencies and FRFs are constructed to identify the dynamic parameters of the IR by the NSGA‐II algorithm. The simulated and experimental results illustrate the effectiveness of the methodology presented in this paper, which provides an alternative to the identification of IR dynamic parameters.

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Adaptive Parameter Estimation and Control Design for Robot Manipulators With Finite-Time Convergence

Cited by 375 publications

References 29 publications

Fuzzy Adaptive Switching Control for an Uncertain Robot Manipulators with Time‐Varying Output Constraint

Fuzzy Adaptive Switching Control for an Uncertain Robot Manipulators with Time‐Varying Output Constraint

Neural adaptive global stability control for robot manipulators with time‐varying output constraints

A method for dynamic parameter identification of an industrial robot based on frequency response function

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