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第三十二届中国控制会议

作者： CHI Ronghu HOU Zhongsheng JIN Shangtai WANG Danwei School of Automation & Electronic Engineering Qingdao University of Science & Technology Advanced Control Systems Lab School of Electronic ＆ Information EngineeringBeijing Jiaotong University EXQUISITUS Centre for E-CitySchool of Electrical & Electronic EngineeringNanyang Technological University

ISBN: (纸本)9781479900305

This paper presents a new data-driven optimal design framework of point-to-point iterative learning control (PTP-ILC),where the control signal is directly updated from the errors of given multiple intermediate pass points.A major contribution is that the presented optimal PTP-ILC mechanism only uses the real-time measured I/O data without any model information of the plant for the controller design,convergence analysis,and conduction,from which the distinctive 'data-driven' feature of the presented approaches is obvious and *** mathematical analysis is developed to illustrate the effecience of the proposed approach.

关键词： Data-driven control optimal ILC Point-to-point tracking tasks Nonlinear discrete-time systems

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On Three Discrete-time Adaptive ILCs for Different Nonlinear Uncertain systems

On Three Discrete-time Adaptive ILCs for Different Nonlinear...

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第25届中国控制与决策会议

作者： Chi Ronghu Zhang Dexia Hou Zhongsheng Wang Danwei School of Automation and Electronics Engineering Qingdao University of Science and Technology Advanced Control Systems Lab School of Electronics & Information Engineering Beijing Jiaotong University EXQUISITUS Centre for E-City School of Electrical & Electronic Engineering Nanyang Technological University

ISBN: (纸本)9781467355322

In this paper, three discrete-time adaptive ILC approaches are discussed fully. In parallel with the continuous-time systems, a discrete-time adaptive ILC with time-difference estimator is first proposed for a class of discrete-time systems with constant parametric uncertainties. And then, a discrete-time adaptive ILC with hybrid-difference estimator is proposed for the other class of discrete-time systems with a mixture of time-varying and time-invariant uncertain parameters. The existing discrete-time adaptive ILC with iteration-difference is also reviewed for the convenience of comparison. Three cases are considered in the simulation to show the effectiveness of the proposed adaptive ILCs and to answer the question that which one is most suitable for the different controlled systems.

关键词： Adaptive ILC, Time-difference estimator, Iteration-difference estimator, Hybrid-difference estimator, Discrete-time systems

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Model-Free based Optimal Iterative Learning control for a Class of Discrete-Time Nonlinear systems

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IFAC Proceedings Volumes 2008年第2期41卷 2814-2819页

作者： Shangtai Jin Zhongsheng Hou Ming Zhao Advanced Control Systems Lab Beijing Jiaotong University Beijing China State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China (Tel: 010-51688617 )

A pseudo-partial-derivative based dynamic linearization method is introduced, the method can transform general discrete-time nonlinear model into discrete-time time-varying linear model. Based on this discrete-time time-varying linear model, a novel norm-optimal iterative learning control (NOILC), called model-free based norm-optimal iterative learning control (MFNOILC), is proposed for a class of discrete-time nonlinear systems. Through rigorous analysis, the convergence of the proposed algorithm is proved. The simulation results show the effectiveness of the algorithm.

关键词： Iterative Learning control discrete-time nonlinear system pseudo-partial-derivative Freeway traffic control

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Self-supervised learning as an enabling technology for future space exploration robots: ISS experiments 67

Self-supervised learning as an enabling technology for futur...

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67th International Astronautical Congress, IAC 2016

作者： Van Hecke, Kevin De Croon, Guido C.H.E. Hennes, Daniel Setterfield, Timothy P. Saenz-Otero, Alvar Izzo, Dario MAV-Lab. Control and Simulation Faculty of Aerospace Engineering TU Delft Netherlands Advanced Concepts Team of the European Space Agency ESTEC Noordwijk Netherlands Space Systems Lab. Massachusetts Institute of Technology CambridgeMA United States

Future space missions will increasingly rely on the help of robotic systems and in some cases even be performed purely by fully autonomous robots. Currently, robots lack the ability to learn from their environment. Learning is advantageous, since it decreases engineering effort and allows the robot to adapt to specific properties of its potentially yet unknown environment. However, in space robotics machine learning is not yet used, as it introduces extra risk for safety-critical missions. In this talk, we will introduce self-supervised learning as a reliable learning method that allows robots to adapt to their environment. We have previously successfully tested this method for the navigation of autonomous drones on Earth, which showed its potential in terms of reliability for application in space. Here, we will present preliminary results from an ISS test performed with the MIT/NASA SPHERES VERTIGO satellite, which is equipped with a stereo vision system that allows it to see distances and navigate by itself in the ISS. The presented preliminary experiments were performed on October 8th 2015 on board the ISS. In the experiments, the satellite learned by itself how to see distances with a single camera. It did so by using the distance estimates from its stereo vision process as supervised learning targets. The main advantage of this learning method is that the robot becomes robust against losing the images from one of the cameras - it can then still continue to function. The main goals of the experiment were (1) data gathering, and (2) navigation on the basis of stereo vision. First the astronaut Kimya Yui moved the satellite around the Japanese Experiment Module to gather stereo vision data for learning. Subsequently, the satellite freely explored the space in the module based on its (trusted) stereo vision system and a pre-programmed exploration behavior, while simultaneously performing the self-supervised learning on board in the background. The two main goals we

关键词： Satellites

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Output feedback adaptive robust learning control of a class of nonlinear systems with periodic disturbances

Output feedback adaptive robust learning control of a class ...

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作者： Liu, Xiangbin Hou, Zhongsheng Yao, Bin Su, Hongye Sun, Mingxuan Advanced Control Systems Laboratory School of Electronics and Information Engineering Beijing Jiaotong University Beijing 100044 China School of Mechanical Engineering Purdue University West Lafayette IN 47907 United States State Key Lab. of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou 310027 China College of Information Engineering Zhejiang University of Technology Hangzhou 310032 China

ISBN: (纸本)9783902661937

In this paper, a discontinuous projection-based output feedback adaptive robust learning control (OARLC) scheme is constructed for a class of nonlinear systems in a semi-strict feedback form by incorporating an observer and a dynamic normalization signal. Since only output signal is available for measurement, an observer is firstly designed to provide exponentially convergent estimates of the unmeasurable states. Using certain known basis functions to capture the characteristics of unknown general periodic disturbances, the discontinuous projection type adaptation law can then be used to tune the amplitudes of those basis functions on-line to recover the unknown general periodic disturbances asymptotically. The estimation errors due to the unknown initial states, uncompensated disturbances, and the uncertain nonlinearities are also effectively dealt with via certain robust feedback at each step of the proposed OARLC backstepping design. The resulting controller achieves a guaranteed transient and a prescribed final tracking accuracy for output tracking performance. In addition, when the general periodic disturbances fall within the approximation ranges of the periodic basis functions, asymptotic output tracking performance is achieved as well. © 2011 IFAC.

关键词： Feedback

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MRI image segmentation using information granules and MRFs

MRI image segmentation using information granules and MRFs

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International Conference on Electronics, InformationTechnology and Intellectualization, EITI 2014

作者： Xie, Shuang Yun Dang, Ya Nan Wang, Fang College of Information Engineering Taiyuan University of Technology TaiyuanShanxi China Key Lab of Advanced Transducers and Intelligent control Systems Ministry of Education and Shanxi Province Taiyuan University of Technology Ministry of Education Taiyuan China

ISBN: (纸本)9781138027411

In order to obtain an accurate segmentation of spinal marrowin an MRI image, a Markov random field image segmentation algorithm based on an information granule is proposed. The information granule is established at first, then the region of the image is also created for the epitaxy of the granule, and the region features are extracted both internally and externally to be the connotation of the granule. The internal region features are denoted by second-order moment and the average value of pixels intra-regionally, and the external features by a region adjacency matrix. By combining the region features and information granule with the MRF, the image segmentation is finished, so a wrong segmentation caused by single pixel is avoided. Finally, the rule of maintaining big granule is used to correct the segmentation result. Compared with the result of the Markov random field, the effectiveness and veracity of this method are verified by the simulation experiment. © 2015 Taylor & Francis Group, London.

关键词： Image segmentation

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An accurate tuning model of spintronic memristor with different switching thresholds by logic judgment of domain wall motion 41

An accurate tuning model of spintronic memristor with differ...

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第41届中国控制会议

作者： Kun Wang Yajuan Hui Zhong Yang Xiangshui Miao School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan National Lab for Optoelectronics Huazhong University of Science and Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Spintronic memristor possessing extraordinary advantages such as high-speed,low-power,highly scalable,and capable of constructing dynamic complex systems,which is a potential candidate for the next-generation memory *** many excellent models of spintronic memristor have been proposed,nonlinear model is not suitable for application analysis because of its complex *** this paper,an accurate tuning model of spintronic memristor with different switching thresholds by logic judgment of domain-wall motion is *** switching thresholds of current density are introduced to drive the domain-wall movement by judging the position of state value and real-time current density,which solves the general problems of boundary ***,by the simulation and verification via PSpice modeling,an accurate regulation of the resistance by voltage excitation is *** results pave the way for the applications of diversified circuit design for spintronic memristor.

关键词： Spintronic memristor Different switching thresholds SPICE model

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An Optimal Data-Driven Method for controlling Epileptic Seizures

An Optimal Data-Driven Method for Controlling Epileptic Seiz...

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Iranian Conference of Biomedical Engineering (ICBME)

作者： Siavash Shams Sana Motallebi Mohammad Javad Yazdanpanah Advanced Control Systems Lab School of ECE College of Engineering University of Tehran Tehran Iran

The regions of the brain may be viewed as nodes in a complex network where information is dynamically transferred through synchronization. Synchronization plays an important role in learning, emotions, and motion. However, neurological disorders such as epilepsy are known to result from abnormal brain synchronization. Coupled Kuramoto model with a little integration of the neurological factors can be a suitable model of the brain network. In this paper, we present an open-loop data-driven control strategy to effectively desynchronize the activity of brain regions during a simulated seizure episode without making any assumptions about the dynamics of the brain. In order to quantify the significance of network nodes, we used an energy-based optimization problem. Then, we evaluated our control methods using a genuine connectome with 80 regions and demonstrated that our approach remarkably decreased synchrony between phases of the oscillations of the brain during the epileptic seizure. Finally, we conclude that brain epilepsy synchronization can be controlled by applying external inputs to the chosen optimal set of driver nodes.

关键词： Neurological diseases Biological system modeling Epilepsy Complex networks Brain modeling Synchronization Oscillators

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Model-Free Indirect Adaptive Decoupling control for Nonlinear Discrete-Time MIMO systems

Model-Free Indirect Adaptive Decoupling Control for Nonlinea...

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Joint 48th IEEE Conference on Decision and control and 28th Chinese control Conference

作者： Weihong Wang Zhongsheng Hou Shangtai Jin Advanced Control Systems Lab. School of Electronics and Information EngineeringBeijing Jiaotong University

In this paper,an indirect adaptive decoupling controller with feedforward compensation is presented for nonlinear multivariable discrete-time dynamical *** design is model-free,based directly on some new dynamical linearization method and the resulting so called pseudo Jacobi matrix which derived on-line from the input and output information of the *** feedforward decoupling strategy is employed to achieve static decoupling and then to obtain the improved performance and stability *** analysis and simulation results show that the model-free indirect adaptive decoupling control system is stable and convergent.

关键词： Jacobi Model-Free Indirect Adaptive Decoupling control for Nonlinear Discrete-Time MIMO systems

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Iterative learning control based ramp metering tracking various trajectories

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IFAC Proceedings Volumes 2008年第2期41卷 2826-2831页

作者： Jingwen Yan Zhongsheng Hou Ming Zhao Advanced Control Systems Lab Beijing Jiaotong University Beijing China State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China (Tel: 86-10-51688617

In this work, we apply the iterative learning control approach to address the traffic density control via ramp metering in a macroscopic level freeway environment. The traffic density control problem is formulated into an output tracking problem and the tracking trajectories are variable with time and iteration change. Rigorous analyses and intensive simulations show that the iterative learning control method we proposed can deal with this kind of problem successfully.

关键词： Iterative Learning control ramp metering tracking various trajectories

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