Neural Network Control of a Two-Link Flexible Robotic Manipulator Using Assumed Mode Method

Gao, Hejia; He, Wei; Zhou, Chen; Sun, Changyin

doi:10.1109/tii.2018.2818120

Cited by 184 publications

(83 citation statements)

References 37 publications

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“…Therefore, ∆q is UUB with the ultimate bound result shown in (19). In this way, the practical deliverable input torque in (12) yields an accurate tracking control for the robot joints.…”

Section: Control Approach Based On the Bounded-disturbance: First Conmentioning

confidence: 86%

“…The validity of the designed τ in (12) can be investigated by substituting (12) into (10) and then (10) into (7), which giveṡ…”

Section: Control Approach Based On the Bounded-disturbance: First Conmentioning

confidence: 99%

“…This controller does not require an off-line training-phase, as compared with other neural network approaches. Gao, H. et al [12] developed a neural network controller for vibration suppression of a two-link flexible robotic manipulator and they showed UUB results. The mentioned works require velocity measurements to design the controllers.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dedicated Nonlinear Control of Robot Manipulators in the Presence of External Vibration and Uncertain Payload

2020

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Robot manipulators are often tasked with working in environments with vibrations and are subject to load uncertainty. Providing an accurate tracking control design with implementable torque input for these robots is a complex topic. This paper presents two approaches to solve this problem. The approaches consider joint space tracking control design in the presence of nonlinear uncertain torques caused by external vibration and payload variation. The properties of the uncertain torques are used in both approaches. The first approach is based on the boundedness property, while the second approach considers the differentiability and boundedness together. The controllers derived from each approach differ from the perspectives of accuracy, control effort, and disturbance properties. A Lyapunov-based analysis is utilized to guarantee the stability of the control design in each case. Simulation results validate the approaches and demonstrate the performance of the controllers. The derived controllers show stable results at the cost of the mentioned properties.

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“…Therefore, ∆q is UUB with the ultimate bound result shown in (19). In this way, the practical deliverable input torque in (12) yields an accurate tracking control for the robot joints.…”

Section: Control Approach Based On the Bounded-disturbance: First Conmentioning

confidence: 86%

“…The validity of the designed τ in (12) can be investigated by substituting (12) into (10) and then (10) into (7), which giveṡ…”

Section: Control Approach Based On the Bounded-disturbance: First Conmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dedicated Nonlinear Control of Robot Manipulators in the Presence of External Vibration and Uncertain Payload

2020

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“…Examples include approaches that are based on: artificial force field [22]; optimization methods [23]; vision-based control (cameraspace manipulation) [24]; rapidly-exploring random trees and probabilistic roadmaps [25]; screw theory [26]; and recurrent neural network [27]. Control of robotic manipulators have also been widely studied [28,29,30]. However, in this paper, the multi-AIR path planning problem (stage 2) is solved with a particular focus on the fiber placement task.…”

Section: Related Workmentioning

confidence: 99%

A Two-Stage Approach to Collaborative Fiber Placement through Coordination of Multiple Autonomous Industrial Robots

Hassan

Liu

2018

J Intell Robot Syst

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The use of multiple Autonomous Industrial Robots (AIRs) as opposed to a single AIR to perform fiber placement brings about many challenges which have not been addressed by researchers. These challenges include optimal division and allocation of the work and performing path planning in a coordinated manner while considering the requirements and constraints that are unique to the fiber placement task. To solve these challenges, a two-stage approach is proposed in this paper. The first stage considers multiple objectives to optimally allocate each AIR with surface areas, while the second stage aims to generate coordinated paths for the AIRs. Within each stage, mathematical models are developed with several unique objectives and constraints that are specific to the multi-AIR collaborative fiber placement. Several case studies are presented to validate the approach and the proposed mathematical models. Comparison studies using a different number of AIRs and variations of the developed mathematical models are also presented.

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“…Based on the dynamic model of flexible manipulators, researchers have made many studies on the topic of flexible manipulator control. On the one hand, some effective control strategies are investigated based on the coupling system model, such as the traditional PID control [5,6], optimal control [7,8], sliding mode control [9,10], H ∞ control [11], robust control [12][13][14], boundary control [15], and neural network control [16,17]. On the other hand, taking the two-timescale characteristics into account, the SP approach is successfully introduced into the modeling and control of the complex flexible manipulator systems [18,19].…”

Section: Introductionmentioning

confidence: 99%

Model‐Free Composite Control of Flexible Manipulators Based on Adaptive Dynamic Programming

Yang

Zhou

et al. 2018

Complexity

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This paper studies the problems of tip position regulation and vibration suppression of flexible manipulators without using the model. Because of the two-timescale characteristics of flexible manipulators, applying the existing model-free control methods may lead to ill-conditioned numerical problems. In this paper, the dynamics of a flexible manipulator is decomposed into two subsystems which are linear and controllable at different timescales by singular perturbation (SP) theory and a model-free composite controller is designed to alleviate the ill-conditioned numerical problems. To do this, a model-free composite control strategy is constructed which facilitates in designing the controller in slow and fast timescales. In the slow timescale, the slow subsystem controller is designed by adaptive dynamic programming (ADP) based on the measurements of the slow inputs and the position, while the vibration in the slow timescale is estimated by the least square method. In the fast timescale, the vibration is reconstructed based on the measurements of vibration and its estimate in the slow timescale, by which the fast controller is designed using ADP. Stability of the closed-loop system is proved by SP theory. Finally, simulations are given to show the feasibility and effectiveness of the proposed methods.

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Neural Network Control of a Two-Link Flexible Robotic Manipulator Using Assumed Mode Method

Cited by 184 publications

References 37 publications

Dedicated Nonlinear Control of Robot Manipulators in the Presence of External Vibration and Uncertain Payload

Dedicated Nonlinear Control of Robot Manipulators in the Presence of External Vibration and Uncertain Payload

A Two-Stage Approach to Collaborative Fiber Placement through Coordination of Multiple Autonomous Industrial Robots

Model‐Free Composite Control of Flexible Manipulators Based on Adaptive Dynamic Programming

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