A Precise Neural-Disturbance Learning Controller of Constrained Robotic Manipulators

Xuan, Dang; Bae, Joonbum

doi:10.1109/access.2021.3069229

Cited by 18 publications

(9 citation statements)

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“…Based on the constraint (15), there always exist arbitrarily small new constants (β tj|j=1..4 ) that simplify the inequality (14) as follows:…”

Section: A Time-based Pd Control Signalmentioning

confidence: 99%

“…In practice, applicability of such conventional approaches is limited for general robots. By utilizing universal approximation properties, neural-network-based control methods are growingly employed in servo systems [14], [15], [16]. Direct and indirect adaptation laws have been successfully adopted to activate neural networks in automatic control systems [10], [17].…”

mentioning

confidence: 99%

“…The systematic behaviors could be well captured by neural networks, and their results could be then employed to compensate for unknown dynamics in control processes. Such design could be applied to divergent types of neural network such as Radial-basis function (RBF) networks [14], [18], [19] or Fuzzy-hybrid-networks [20], [21]. Remarkable control outcomes have been delivered by the intelligent controllers.…”

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confidence: 99%

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A Novel Iterative Second-Order Neural-Network Learning Control Approach for Robotic Manipulators

Thien²,

Bae

2023

IEEE Access

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Iterative Learning Control (ILC) is known as a high-accuracy control strategy for repetitive control missions of mechatronic systems. However, applying such learning controllers for robotic manipulators to result in excellent control performances is now a challenge due to unstable behaviors coming from nonlinearities, uncertainties and disturbances in the system dynamics. To tackle this challenge, in this paper, we present a novel proportional-derivative iterative second-order neural-network learning control (PDISN) method for motion-tracking control problems of robotic manipulators. The control framework is structured from time-and iterative-base control layers. First of all, the total systematic dynamics are concretely stabilized by a conventional Proportional-Derivative (PD) control signal in the time domain. The control objective is then accomplished by using an intelligent ILC decision generated in the second layer to compensate for other nonlinear uncertainties and external disturbances in the dynamics. The iterative signal is flexibly composed from various information on the iterative axis. On one hand, the previous iterative control signal is inherently reused in the current iteration but with an appropriate portion based on reliability of the current control performance. On the other hand, the iterative-based modeling deviation remaining is treated by a functional neural network that is specially activated by a second-order learning law and information synthesized from the current and previous iterations. Stabilities of the time-based nonlinear subsystem and overall system are rigorously analyzed using extended Lyapunov theories and high-order regression series criteria. Effectiveness of the proposed controller was intensively verified by the extensive comparative simulation results. Key advantages of the proposed control method are chattering-free, universal, adaptive, and robust.INDEX TERMS Iterative learning control, motion control, neural networks, robotic manipulators. I. INTRODUCTION Recently, robots play an indispensable role in industry and day-life activities [1], [2], [3]. Furthermore, they can cowork with humans to increase working efficiency [2], [4]. As a result, such modern robots have to possess high control accuracies, adaptation, robustness and reliability [1], [3].The associate editor coordinating the review of this manuscript and approving it for publication was Wei Liu.

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“…Based on the constraint (15), there always exist arbitrarily small new constants (β tj|j=1..4 ) that simplify the inequality (14) as follows:…”

Section: A Time-based Pd Control Signalmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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A Novel Iterative Second-Order Neural-Network Learning Control Approach for Robotic Manipulators

Thien²,

Bae

2023

IEEE Access

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“…The number of iterations before convergence should be cut down. Additionally, the uncertainty bounds are evaluated by mathematical formulas for both inner and outer sets [28][29][30][31][32].…”

Section: Background Workmentioning

confidence: 99%

Enhancement of the Tracking Performance for Robot Manipulator by Using the Feed-forward Scheme and Reasonable Switching Mechanism

Ngo

Nguyеn

2022

Journal of Robotics and Control

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Robot manipulator has become an exciting topic for many researchers during several decades. They have investigated the advanced algorithms such as sliding mode control, neural network, or genetic scheme to implement these developments. However, they lacked the integration of these algorithms to explore many potential expansions. Simultaneously, the complicated system requires a lot of computational costs, which is not always supported. Therefore, this paper presents a novel design of switching mechanisms to control the robot manipulator. This investigation is expected to achieve superior performance by flexibly adjusting various strategies for better selection. The Proportional-Integral-Derivative (PID) scheme is well-known, easy to implement, and ensures rapid computation while it might not have much control effect. The advanced interval type-2 fuzzy sliding mode control properly deals with nonlinear factors and disturbances. Consequently, the PID scheme is switched when the tracking error is less than the threshold or is far from the target. Otherwise, the interval type-2 fuzzy sliding mode control scheme is activated to cope with unknown factors. The main contributions of this paper are (i) the recommendation of a suitable switching mechanism to drive the robot manipulator, (ii) the successful integration of the interval type-2 fuzzy sliding mode control to track the desired trajectory, and (iii) the launching of several tests to validate the proposed controller with robot model. From these achievements, it would be stated that the proposed approach is effective in tracking performance, robust in disturbance-rejection, and feasible in practical implementation.

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“…In [10], an adaptive neural gain scheduling sliding mode control method was presented to minimize the forces and it was applied to a quadcopter with external disturbances. In [11,12], adaptive robust controllers were introduced for high-precision tracking control problems of robots with output constraints. In [13,14], model-free finite-time terminal sliding mode control schemes of uncertain robots were discussed.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Sliding Mode Control to Save Energy in a SCARA Robot

et al. 2021

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Sliding mode control is a robust technique that is used to overcome difficulties such as parameter variations, unmodeled dynamics, external disturbances, and payload changes in the position-tracking problem regarding robots. However, the selection of the gains in the controller could produce bigger forces than are required to move the robots, which requires spending a large amount of energy. In the literature, several approaches were used to manage these features, but some proposals are complex and require tuning the gains. In this work, a sliding mode controller was designed and optimized in order to save energy in the position-tracking problem of a two-degree-of-freedom SCARA robot. The sliding mode controller gains were optimized usinga Bat algorithm to save energy by minimizing the forces. Finally, two controllers were designed and implemented in the simulation, and as a result, adequate controller gains were found that saved energy by minimizing the forces.

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A Precise Neural-Disturbance Learning Controller of Constrained Robotic Manipulators

Cited by 18 publications

References 50 publications

A Novel Iterative Second-Order Neural-Network Learning Control Approach for Robotic Manipulators

A Novel Iterative Second-Order Neural-Network Learning Control Approach for Robotic Manipulators

Enhancement of the Tracking Performance for Robot Manipulator by Using the Feed-forward Scheme and Reasonable Switching Mechanism

Optimization of Sliding Mode Control to Save Energy in a SCARA Robot

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