Vibration Control of a Flexible Robotic Manipulator in the Presence of Input Deadzone

He, Wei; Ouyang, Yuncheng; Hong, Jin‐Woo

doi:10.1109/tii.2016.2608739

Cited by 343 publications

(97 citation statements)

References 31 publications

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“…By using Newton-Leibniz formula and introducing Finsler's Lemma, the obtained synchronization criteria are divided in terms of LMI inequalities, and such method has not been obtained until now. On the other hand, in the studies of network-based motion control in actual models [31][32][33][34], a novel integral barrier function is first employed for control design of the constrained distributed parameter system modeled as PDEs [35][36][37][38], fault tolerance control in complex systems [39], have been hot topics in recent time. Therefore, how to extend our results into these control fields is still a challenging problem.…”

Section: Resultsmentioning

confidence: 99%

Pinning Synchronization for Complex Networks with Interval Coupling Delay by Variable Subintervals Method and Finsler’s Lemma

et al. 2017

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The pinning synchronous problem for complex networks with interval delays is studied in this paper. First, by using an inequality which is introduced from Newton-Leibniz formula, a new synchronization criterion is derived. Second, combining Finsler's Lemma with homogenous matrix, convergent linear matrix inequality (LMI) relaxations for synchronization analysis are proposed with matrix-valued coefficients. Third, a new variable subintervals method is applied to expand the obtained results. Different from previous results, the interval delays are divided into some subdelays, which can introduce more free weighting matrices. Fourth, the results are shown as LMI, which can be easily analyzed or tested. Finally, the stability of the networks is proved via Lyapunov's stability theorem, and the simulation of the trajectory claims the practicality of the proposed pinning control.

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Section: Resultsmentioning

confidence: 99%

Pinning Synchronization for Complex Networks with Interval Coupling Delay by Variable Subintervals Method and Finsler’s Lemma

et al. 2017

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“…In [92], the NN was applied for the estimation of the unknown model parameters of a marine surface vessel and in [93] the full-state constraint of an n-link robotic manipulator was achieved by using the NN control. The NN controller was also constructed for flexible robotic manipulators to deal with the vibration suppression based on a lumped spring-mass model [94] while in [95], two RBFNNs were constructed for flexible robot manipulators to compensate for the unknown dynamics and the dead-zone effect, respectively.…”

Section: Nn Based Robot Control With Input Nonlinearitiesmentioning

confidence: 99%

A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots

Jiang

Yang

et al. 2017

Complexity

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As an imitation of the biological nervous systems, neural networks (NNs), which have been characterized as powerful learning tools, are employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification, and patterns recognition. This article aims to bring a brief review of the state-of-the-art NNs for the complex nonlinear systems by summarizing recent progress of NNs in both theory and practical applications. Specifically, this survey also reviews a number of NN based robot control algorithms, including NN based manipulator control, NN based human-robot interaction, and NN based cognitive control.

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“…Obviously, there is need to partition the movement data collected from patients in a way that is intuitive and commensurate with how the task both performed (by the patient) and observed (by the clinician); a substantial portion of the robot's utility is in providing accurate quantitative support to guiding therapy in real-time and supporting offline analysis. Moreover, considering that in adaptive robotics, early-session data are used to train the robot's force-impedance settings for optimized re-training (Patton and Mussa-Ivaldi, 2004;Xu et al, 2011;Song et al, 2015), the ongoing inquiry into movement decomposition (modeling the speed profile as a series of log-normal curves) (Rohrer et al, 2002;Dipietro et al, 2009;Balasubramanian et al, 2012Balasubramanian et al, , 2015, or the robotic manipulandums prevalent in industrial applications (Gao and Zhang, 2015;Chen et al, 2016;He et al, 2017). These arenas are just a few examples of where boundary conditions are critically important parameters.…”

Section: Importance Of the Problemmentioning

confidence: 99%

A Partitioning Algorithm for Extracting Movement Epochs from Robot-Derived Kinematic Data

et al. 2017

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Point-to-point exercising of the upper-limb, as elicited through the presentation of visual targets on a computer screen, is a ubiquitous paradigm in the robot-assisted rehabilitation of motor-impaired individuals. Kinematic data collected from the robot's sensors can be used to assess motor function; these data allow objective quantification of motor performance, an approach that shows promise both for guiding therapy and documenting patient progress. It is imperative that these datasets be fully understood and that tools be continually developed to support analysis and proper interpretation of robot-generated data. It is our experience that data collected from kinematic robots and partitioned according to target achievement may be prone to errors in analysis and interpretation because the movements of highly spastic individuals rarely stop within the target. Here, we propose that it is preferable to partition serial movement data based on local minima in velocity rather than target achievement; this design reflects the convention that movement epochs start and end at low or zero velocity, an assumption that is prevalent even in severely impaired individuals. Using a commercially available robot (MIT-Manus, Interactive Motion Technologies), we recorded movements from 16 moderate to severely impaired chronic stroke patients. Data partitioned according to target presentation typically interrupted movements in mid-motion: velocity at file start was 32.6 ± 26.4% of the overall velocity range. By re-apportioning, we obtained velocity at file start of 7.4 ± 9.5% of total range. Across 3,200 movements, 12.4 ± 10.4% of data points were re-allocated on average. Thus, our routine is capable of re-partitioning to more accurately reflect observed behavior. Our study is thus the first to identify and propose a solution to the problem of high relevance to the community of robot-aided rehabilitation specialists, i.e., suboptimal partitioning according to target achievement. Through the algorithm described in this paper, we were able to re-partition the data so that movement epochs were properly demarcated at velocity minima, thus adhering to the fundamental assumptions of human motor behavior and facilitating analysis of patient performance on a per-movement basis.

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Vibration Control of a Flexible Robotic Manipulator in the Presence of Input Deadzone

Cited by 343 publications

References 31 publications

Pinning Synchronization for Complex Networks with Interval Coupling Delay by Variable Subintervals Method and Finsler’s Lemma

Pinning Synchronization for Complex Networks with Interval Coupling Delay by Variable Subintervals Method and Finsler’s Lemma

A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots

A Partitioning Algorithm for Extracting Movement Epochs from Robot-Derived Kinematic Data

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