Observer-based adaptive fractional-order control of flexible-joint robots using the Fourier series expansion: theory and experiment

Kheirkhahan, Payam; Izadbakhsh, Alireza

doi:10.1007/s40430-020-02590-1

Cited by 21 publications

(16 citation statements)

References 44 publications

(69 reference statements)

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“…If the performance is satisfactory, the number is reduced; otherwise, it is increased. After several adjustments, they are determined based on some compromise between the computation efficiency and tracking performance 35–37 …”

Section: Simulation Resultsmentioning

confidence: 99%

“…After several adjustments, they are determined based on some compromise between the computation efficiency and tracking performance. [35][36][37] To investigate the efficiency of the proposed scheme, its performance is compared with other estimator. For this purpose, the RBF neural network is integrated into the presented controller to estimate the external disturbances and the uncertainties.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Observer‐based adaptive control of cooperative multiple manipulators using the Mastroianni operators as uncertainty approximator

Deylami

Izadbakhsh

2022

Intl J Robust & Nonlinear

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This article addresses the multi‐objective problem of pose and force control in a cooperative system comprised of multiple n‐degree‐of‐freedom (n‐DOF) robotic arms that move a payload. The proposed controller should be capable of maintaining the position and orientation of the payload in the desired path. In addition, the force exerted by robot end‐effectors on the object must remain limited. The system has unmodeled dynamics, and measuring the robot joint velocities is impossible. In other words, joint positions are the only measured states. Therefore, a Mastroianni operator‐based observer is first designed to estimate the uncertainty and velocities based on its universal approximation property. Then, using these estimations, a feedback controller is proposed to establish a robust and precise tracking performance. The stability of this system is confirmed based on Lyapunov's stability theorem. This article is the first study on utilizing the adaptive form of Mastroianni operators in robust adaptive controller design. Moreover, the introduced structure is regressor‐free and does not depend on velocity measurements. Finally, the proposed adaptive observer‐controller is applied on two 3‐DOF cooperative robotic arms carrying a payload, and the results are precisely analyzed. The results of the proposed approach are also compared with a state‐of‐art powerful approximation method.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

Observer‐based adaptive control of cooperative multiple manipulators using the Mastroianni operators as uncertainty approximator

Deylami

Izadbakhsh

2022

Intl J Robust & Nonlinear

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“…with Φ(q l , ql , qld ) ∈ IR n×p the system regressor, 𝜽 ∈ IR p is the lumped parameter vector, n is the number of links, and p the number of parameters. Notice that equation ( 41) is obtained by using the linear in the parameter assumption (12). The desired rotor position q md proposed to stabilize the internal dynamics ( 40)-( 42) is…”

Section: Desired Rotor Position (Adaptive Internal Dynamics Stabilizer)mentioning

confidence: 99%

Combined adaptive neural network and regressor‐based trajectory tracking control of flexible joint robots

Montoya–Cháirez¹,

Moreno–Valenzuela²,

Santibáñez

et al. 2021

IET Control Theory & Appl

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By relying on the input-output feedback linearization approach, a novel adaptive controller for flexible joint robots is proposed in this work. First, a model-based controller is developed to get a structure that is useful in the development of the adaptive controller. The adaptive version is developed by using two techniques. To stabilize the output function, an adaptive neural network controller is used, which approximates the non-linear function that contains the uncertainties. The desired rotor position required by the input-output feedback linearization controller is defined with the structure of a link dynamics adaptive regressor-based controller. The main reason to adopt the mentioned structure in the definition of the desired rotor link position is to guarantee its differentiability. Real-time experiment comparisons among the model-based controller, a model-based controller with desired compensation, an adaptive controller based on joint torque feedback, and an adaptive neural network-based controller are carried out. Experimental results support the theory reported in this document and the accuracy of the proposed approach.

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“…In this regard, the function approximation technique (FAT) is one of the practical approaches. FAT-based observers are mainly developed based upon Fourier series expansion [22][23][24], differential equations [25,26], and Baskakov operator [27]. In this article, an adaptive FAT-based controller-observer structure will be introduced for robotic manipulators.…”

Section: Introductionmentioning

confidence: 99%

Robust adaptive controller–observer scheme for robot manipulators: a Bernstein–Stancu approach

Izadbakhsh

Nikdel

2021

Robotica

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This article introduces a robust adaptive controller–observer structure for robotic manipulators such that the need for joints speed measurement is removed. Besides, it is presumed that the system model has uncertainties and is subject to disturbances, and the proposed method must eliminate the impact of these factors on the system response. According to this, for the first time in the robotics field, a model-free scheme is developed based on the Bernstein–Stancu polynomial. The universal approximation property of the Bernstein–Stancu polynomial enables it to accurately estimate the lumped uncertainty, including unmodeled dynamics and disturbances. Moreover, to increase the efficiency of the controller–observer structure, adaptive rules have been proposed to update polynomial coefficients. The boundedness of all system errors is proven using the Lyapunov theorem. Finally, the proposed robust Adaptive controller–observer is applied on a planer robot, and the results are precisely analyzed. The results of the proposed approach are also compared with two state-of-art powerful approximation methods.

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Observer-based adaptive fractional-order control of flexible-joint robots using the Fourier series expansion: theory and experiment

Cited by 21 publications

References 44 publications

Observer‐based adaptive control of cooperative multiple manipulators using the Mastroianni operators as uncertainty approximator

Observer‐based adaptive control of cooperative multiple manipulators using the Mastroianni operators as uncertainty approximator

Combined adaptive neural network and regressor‐based trajectory tracking control of flexible joint robots

Robust adaptive controller–observer scheme for robot manipulators: a Bernstein–Stancu approach

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