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Lyapunov-based Stability of Feedback Interconnections of Negative Imaginary systems

IFAC-PapersOnLine 2017年第1期50卷 3424-3428页

作者： Ghallab A.G. Mabrok M.A. Petersen I.R. School of Engineering and Information Technology University of New South Wales at the Australian Defence Force Academy Canberra ACT 2600 Australia Robotics Intelligent Systems and Control Lab at Computer Electrical and Mathematical Science and Engineering King Abdullah University of Science and Technology (KAUST) Saudi Arabia Research School of Engineering The Australian National University Canberra ACT 2601 Australia

Feedback control systems using sensors and actuators such as piezoelectric sensors and actuators, micro-electro-mechanical systems (MEMS) sensors and opto-mechanical sensors, are allowing new advances in designing such high precision technologies. The negative imaginary control systems framework allows for robust control design for such high precision systems in the face of uncertainties due to unmodelled dynamics. The stability of the feedback interconnection of negative imaginary systems has been well established in the literature. However, the proofs of stability feedback interconnection which are used in some previous papers have a shortcoming due to a matrix inevitability issue. In this paper, we provide a new and correct Lyapunov-based proof of one such result and show that the result is still true. © 2017

关键词： Lyapunov stability Negative imaginary lemma Negative imaginary systems Strictly negative imaginary systems

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Model-predictive control with reference input tracking for tensegrity spine robots

arXiv

引用

arXiv 2018年

作者： Sabelhaus, Andrew P. Zhao, Huajing Zhu, Edward L. Agogino, Adrian K. Agogino, Alice M. Department of Mechanical Engineering University of California Berkeley BerkeleyCA94720 United States Department of Mechanical Engineering Department of Robotics University of Michigan Ann ArborMI48109 United States US Army Research Lab Vehicle Technology Directorate AberdeenMD21005 United States Intelligent Systems Divison NASA Ames Research Center Moffet FieldCA94035 United States

Robots with flexible spines based on tensegrity structures have potential advantages over traditional designs with rigid torsos. However, these robots can be difficult to control due to their high-dimensional nonlinear dynamics. To overcome these issues, this work presents two controllers for tensegrity spine robots, using model-predictive control (MPC), and demonstrates the first closed-loop control of such structures. The first of the two controllers is formulated using only state tracking with smoothing constraints. The second controller, newly introduced in this work, tracks both state and input reference trajectories without smoothing. The reference input trajectory is calculated using a rigid-body reformulation of the inverse kinematics of tensegrity structures, and introduces the first feasible solutions to the problem for certain tensegrity topologies. This second controller significantly reduces the number of parameters involved in designing the control system, making the task much easier. The controllers are simulated with 2D and 3D models of a particular tensegrity spine, designed for use as the backbone of a quadruped robot. These simulations illustrate the different benefits of the higher performance of the smoothing controller versus the lower tuning complexity of the more general input-tracking formulation. Both controllers show noise insensitivity and low tracking error, and can be used for different control goals. The reference input tracking controller is also simulated against an additional model of a similar robot, thereby demonstrating its generality. Copyright © 2018, The Authors. All rights reserved.

关键词： Controllers

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Research on Position Sensorless Control of PMSM Based on Improved SMO

引用

IOP Conference Series: Materials Science and engineering 2019年第1期533卷

作者： Zhang Guoqing Xiong Xinhong Zheng Yousheng Zhang Yanhui Chen Lianbo Wu Xinyu Feng Wei Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China CAS Key Laboratory of Human-Machine-Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Shenzhen 518055 China School of Logistics Engineering Wuhan University of Technology Wuhan 430070 China

The sliding mode observer (SMO) can effectively achieve the permanent magnet synchronous motor (PMSM) to perform position sensorless control, but there are still drawbacks of system chattering and high complexity. In this paper, an improved SMO is established based on hyperbolic tangent function and Kalman filter. The system chattering is effectively suppressed, and the accuracy of position and speed observation error is significantly improved without phase compensation. It is found that the convergence rate of the hyperbolic tangent function has a strong correlation with the system chattering degree and the speed observation error. When a is between 3 and 4, the speed error accuracy of the improved SMO is the optimum situation.

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Parameter Tuning of PID Controller of Servo System Based on Particle Swarm Optimization Algorithm

引用

IOP Conference Series: Materials Science and engineering 2019年第1期533卷

作者： Chen Lianbo Xiong Xinhong Zheng Yousheng Zhang Yanhui Zhang Guoqing Wu Xinyu Feng Wei Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China CAS Key Laboratory of Human-Machine-Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Shenzhen 518055 China School of Logistics Engineering Wuhan University of Technology Wuhan 430070 China

In order to improve the control precision of permanent magnet synchronous motor servo drive, a Proportional-Integral-Differential (PID) control method based on particle swarm optimization (PSO) algorithm is proposed. Firstly, the mathematical model of the permanent magnet synchronous motor was established. Then the PSO algorithm was used to adjust the parameters of the PID controller. Finally, the simulation experiment was carried out under the MATlab/Simulink environment to test its performance. Compared with the traditional manual parameters tuning, the PID controller based on PSO algorithm had better performance and better stability.

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Why is the Winner the Best?

Why is the Winner the Best?

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Metal–Organic Frameworks: In-Flow MOF Lithography (Adv. Mater. Technol. 6/2019)

引用

Advanced Materials Technologies 2019年第6期4卷

作者： Semih Sevim Carlos Franco Hongjun Liu Hervé Roussel Laetitia Rapenne Juan Rubio-Zuazo Xiang-Zhong Chen Salvador Pané David Muñoz-Rojas Andrew J. deMello Josep Puigmartí-Luis Institute of Chemical and Bioengineering ETH Zurich Vladimir Prelog Weg 1 8093 Zurich Switzerland Institute of Engineering Univ. Grenoble Alpes CNRS Grenoble INP LMGP F-38000 Grenoble France Spanish CRG BM25-SpLine Beamline at the ESRF 71 Av des Martyrs CS 40220 38043 Grenoble Cedex 9 France Instituto de Ciencia de Materiales de Madrid—ICMM/CSIC Cantoblanco E-28049 Madrid Spain Multi-Scale Robotics Lab (MSRL) Institute of Robotics and Intelligent Systems (IRIS) ETH Zurich Tannenstrasse 3 8092 Zurich Switzerland

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Hybrid control trajectory optimization under uncertainty

arXiv

引用

arXiv 2017年

作者： Pajarinen, Joni Kyrki, Ville Koval, Michael Srinivasa, Siddhartha Peters, Jan Neumann, Gerhard labs TU Darmstadt Germany Department of Electrical Engineering and Automation Aalto University Finland Robotics Institute Carnegie Mellon University United States IAS lab TU Darmstadt Germany Max Planck Institute for Intelligent Systems Tuebingen Germany Lincoln Centre for Autonomous Systems University of Lincoln United Kingdom

— Trajectory optimization is a fundamental problem in robotics. While optimization of continuous control trajectories is well developed, many applications require both discrete and continuous, i.e. hybrid controls. Finding an optimal sequence of hybrid controls is challenging due to the exponential explosion of discrete control combinations. Our method, based on Differential Dynamic Programming (DDP), circumvents this problem by incorporating discrete actions inside DDP: we first optimize continuous mixtures of discrete actions, and, subsequently force the mixtures into fully discrete actions. Moreover, we show how our approach can be extended to partially observable Markov decision processes (POMDPs) for trajectory planning under uncertainty. We validate the approach in a car driving problem where the robot has to switch discrete gears and in a box pushing application where the robot can switch the side of the box to push. The pose and the friction parameters of the pushed box are initially unknown and only indirectly observable. Copyright © 2017, The Authors. All rights reserved.

关键词： Aerodynamics

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Real-time perception meets reactive motion generation

arXiv

引用

arXiv 2017年

作者： Kappler, Daniel Meier, Franziska Issac, Jan Mainprice, Jim Cifuentes, Cristina Garcia Wüthrich, Manuel Berenz, Vincent Schaal, Stefan Ratliff, Nathan Bohg, Jeannette Autonomous Motion Dept. at the MPI for Intelligent Systems Germany CLMC lab Univ. of Southern California United States Lula Robotics Inc. United States Univ. of Stuttgart Germany Dept. of Computer Science & Engineering Univ. of Washington United States Dept. of Computer Science Stanford Univ United States

We address the challenging problem of robotic grasping and manipulation in the presence of uncertainty. This uncertainty is due to noisy sensing, inaccurate models and hard-to-predict environment dynamics. We quantify the importance of continuous, real-time perception and its tight integration with reactive motion generation methods in dynamic manipulation scenarios. We compare three different systems that are instantiations of the most common architectures in the field: (i) a traditional sense-plan-act approach that is still widely used, (ii) a myopic controller that only reacts to local environment dynamics and (iii) a reactive planner that integrates feedback control and motion optimization. All architectures rely on the same components for real-time perception and reactive motion generation to allow a quantitative evaluation. We extensively evaluate the systems on a real robotic platform in four scenarios that exhibit either a challenging workspace geometry or a dynamic environment. In 333 experiments, we quantify the robustness and accuracy that is due to integrating real-time feedback at different time scales in a reactive motion generation system. We also report on the lessons learned for system building. Copyright © 2017, The Authors. All rights reserved.

关键词： Feedback

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Sampled-data robust feedback linearization using estimator

Sampled-data robust feedback linearization using estimator

引用

International Workshop on Advanced Motion Control (AMC)

作者： Asim Zaheer Yasar Ayaz Momena Hasan Muhammad Salman Robotics and Intelligent Systems Engineering Lab National University of Sciences and Technology Islamabad Pakistan Department of Electrical Engineering National University of Sciences and Technology Islamabad Pakistan

In this paper, robust control schemes are presented to achieve sampled-data output feedback tracking, for the cases of unknown and known nonlinear minimum phase second order plant (system) models. For known system model case, system output tracks reference trajectory using Extended Kalman Filter (EKF), Unscented Kalman filter (UKF), and Cubature Kalman Filter (CKF). Whereas, for unknown system model case; EKF, UKF and CKF cannot be utilized. For this case, in this paper; State-Space Recursive Least Squares (SSRLS) and Sliding Mode observer (SMO) are employed. SSRLS uses constant velocity model, whereas, SMO requires information about input function only, to track the reference signal. Emulation Design based discrete feedback linearization controller utilizes estimated states to generate control input for plant. The robustness of these sampled-data output feedback control schemes (using estimators) against disturbance and parameter perturbation is demonstrated. It is presented via simulations for magnetic levitation system, that robust tracking is achieved on using estimators (Kalman filters and SMO) in sampled-data output feedback configuration as compared to performing tracking using sampled-data state feedback scheme. Simulation results show that SMO based output feedback tracking is most robust, followed by CKF and EKF based output feedback scheme. UKF based output feedback scheme is robust against external disturbance force, but for case of system parameter perturbation, UKF tracking error takes longer time to converge. SSRLS based scheme behaves poorly in presence of external disturbance force, as SSRLS estimation is based on constant velocity model and not on actual nonlinear system model.

关键词： Output feedback State feedback Kalman filters Force Emulation Robustness Magnetic levitation

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Integrated mechatronics design of flexure joint and controller in dual-drive gantry: a constrained H2 optimization approach

Integrated mechatronics design of flexure joint and controll...

引用

2017 IEEE International Conference on Advanced intelligent Mechatronics, AIM 2017

作者： Ma, Jun Chen, Si-Lu Kamaldin, Nazir Teo, Chek Sing Tay, Arthur Mamun, Abdullah Al Tan, Kok Kiong Department of Electrical and Computer Engineering SIMTech-NUS Joint Lab on Precision Motion Systems National University of Singapore 117582 Singapore Department of Electrical and Computer Engineering National University of Singapore 117576 Singapore Mechatronics Group Singapore Institute of Manufacturing Technology 138634 Singapore Zhejiang Provincial Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo Institute of Industrial Technology Chinese Academy of Sciences Ningbo Zhejiang315121 China NUS Grad. School for Inte. Sci. and Eng. and Department of Electrical Computer Engineering National University of Singapore 119077 Singapore

ISBN: (纸本)9781509059980

The dual-drive H gantry is widely used in many industrial processes that require high precision Cartesian motion. The conventional rigid-link version suffers breaking down of joints if any de-synchronization between the two carriages occurs. To prevent above potential risk, a novel biaxial gantry with flexure-links is designed to allow a small rotation angle of the cross-Arm. Nevertheless, the chattering of control signals and inappropriate design of the flexure joint will possibly induce resonant modes of the end effector. Thus, in this work, the design requirements in terms of tracking accuracy, biaxial synchronization, and resonant mode suppression are achieved by integrated optimization of the stiffness of flexures and PID controller parameters for a class of point-To-point trajectories with same dynamics but different steps. From here, an H2 optimization with defined constraints is formulated, and an efficient iterative solver is proposed by hybridizing direct computation of constrained projection gradient and line search of optimal steps. Comparative experiments results obtained on the testbed are presented to verify the effectiveness of proposed method. © 2017 IEEE.

关键词： Constrained optimization

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