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Design and Real Time implementation of fmincon , MOGA tuned IO-PID and FO-P I λ D μ controllers for Stabilization of TRMS

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Procedia Computer Science 2020年 171卷 1241-1250页

作者： Sudarshan K. Valluru Rajul Kumar Rahul Kumar Center for Control of Dynamical Systems and Computation Dept. of Electrical Engineering Delhi Technological University Delhi-110042 India

The insistence of this paper is to design Fractional order-proportional Integral Derivative (FO-PID) and Integral order –Proportional Integral Derivative (IO-PID) controllers for trajectory tracking control of Twin rotor Multiple Input Multiple Output (MIMO) system (TRMS). The aim of these designed controllers is to move the main and tail actuator at the desired angles accurately to fulfill the demanding attributes so that TRMS should stabilize while hovering. The controller parameters of both the controllers are optimized using fmincon function as well as Multiobjective genetic algorithm. Both the controller are tested in real time and their performance are compared for control of yaw and pitch angles of TRMS. Manual disturbances are given at after 40 seconds of first given disturbance, and the designed controllers are highly capable in diminishing quickly the effect of manual perturbations given to the main and tail rotor of TRMS. From experiment it is observed from comparative performance that FO-PID controller tuned using multiobjective genetic algorithm give best performance in term of time taken to stabilization after the given disturbance.

关键词： FO-PID IO-PID fmincon MOGA TRMS

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26th Annual computational Neuroscience Meeting (CNS*2017) of the Organization for computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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PARALLEL AND DISTRIBUTED SIMULATION OF FRAME RELAY NETWORKS

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IFAC Proceedings Volumes 2007年第9期40卷 304-309页

作者： Andrzej Sikora Ewa Niewiadomska-Szynkiewicz Dept. of Control and Computation Engineering Warsaw University of Technology ul. Nowowiejska 15/19 00-665 Warsaw Poland Research and Academic Computer Network (NASK) ul. Wαwozowa 18 02-796 Warsaw Poland

This paper deals with the description of a software framework FR/ASimJava for large scale Frame Relay networks simulation and its application to the exampled networks simulation. FR/ASimJava is implemented based on ASimJava – a Java-based software package that can be used to develop parallel and distributed simulators of complex real-life systems. The focus is on scalability and efficiency of large scale networks simulation. We describe the functionality, design and performance of FR/ASimJava software. The case studies – simulations of six networks operating under Frame Relay are provided to illustrate the effectiveness of the presented software tool.

关键词： computer simulation communication protocols distributed simulation simulators software performance synchronization

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LANDING AN UNMANNED AIR VEHICLE: VISION BASED MOTION ESTIMATION AND NONLINEAR control

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Asian Journal of control 2008年第3期1卷

作者： Omid Shakernia Yi Ma T. John Koo Shankar Sastry Dept. of Electrical Engineering & Computer Science University of California at Berkeley Berkeley CA94720-1774 U.S.A. Tak-Kuen John Koo received the B.Eng. degree in 1992 in Electronic Engineering and the M.Phil. in 1994 in Information Engineering both from the Chinese University of Hong Kong. From 1994 to 1995 he was a graduate student in Signal and Image Processing Institute at the University of Southern California. He is currently a Ph.D. Candidate in Electrical Engineering and Computer Sciences at the University of California at Berkeley. His research interests include nonlinear control theory hybrid systems inertial navigation systems with applications to unmanned aerial vehicles. He received the Distinguished M.Phil. Thesis Award of the Faculty of Engineering The Chinese University of Hong Kong in 1994. He was a consultant of SRI International in 1998. Currently he is the team leader of the Berkeley AeRobot Team and a delegate of The Graduate Assembly University of California at Berkeley. He is a student member of IEEE and SIAM. S. Shankar Sastry received his Ph.D. degree in 1981 from the University of California Berkeley. He was on the faculty of MIT from 1980-82 and Harvard University as a Gordon McKay professor in 1994. He is currently a Professor of Electrical Engineering and Computer Sciences and Bioengineering and Director of the Electronics Research Laboratory at Berkeley. He has held visiting appointments at the Australian National University Canberra the University of Rome Scuola Normale and University of Pisa the CNRS laboratory LAAS in Toulouse (poste rouge) and as a Vinton Hayes Visiting fellow at the Center for Intelligent Control Systems at MIT. His areas of research are nonlinear and adaptive control robotic telesurgery control of hybrid systems and biological motor control. He is a coauthor (with M. Bodson) of “Adaptive Control: Stability Convergence and Robustness Prentice Hall 1989.” and (with R. Murray and Z. Li) of “A Mathematical Introduction to Robotic Manipulati

In this paper, we use computer vision as a feedback sensor in a control loop for landing an unmanned air vehicle (UAV) on a landing pad. The vision problem we address here is then a special case of the classic ego-motion estimation problem since all feature points lie on a planar surface (the landing pad). We study together the discrete and differential versions of the ego-motion estimation, in order to obtain both position and velocity of the UAV relative to the landing pad. After briefly reviewing existing algorithm for the discrete case, we present, in a unified geometric framework, a new estimation scheme for solving the differential case. We further show how the obtained algorithms enable the vision sensor to be placed in the feedback loop as a state observer for landing control. These algorithms are linear, numerically robust, and computationally inexpensive hence suitable for real-time implementation. We present a thorough performance evaluation of the motion estimation algorithms under varying levels of image measurement noise, altitudes of the camera above the landing pad, and different camera motions relative to the landing pad. A landing controller is then designed for a full dynamic model of the UAV. Using geometric nonlinear control theory, the dynamics of the UAV are decoupled into an inner system and outer system. The proposed control scheme is then based on the differential flatness of the outer system. For the overall closed-loop system, conditions are provided under which exponential stability can be guaranteed. In the closed-loop system, the controller is tightly coupled with the vision based state estimation and the only auxiliary sensor are accelerometers for measuring acceleration of the UAV. Finally, we show through simulation results that the designed vision-in-the-loop controller generates stable landing maneuvers even for large levels of image measurement noise. Experiments on a real UAV will be presented in future work.

关键词： Motion estimation computer vision based control unmanned air vehicle differential flatness

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Dynamic neural networks for actuator fault diagnosis: Application to the DAMADICS benchmark problem 5

Dynamic neural networks for actuator fault diagnosis: Applic...

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5th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, Safeprocess 2003

作者： Patan, Krzysztof Parisini, Thomas Institute of Control and Computation Engineering University of Zielona Góra ul. Podgórna 50 Zielona Góra65-246 Poland Dept. of Electrical Electronic and Computer Engineering DEEI-University of Trieste Via Valerio 10 Trieste34127 Italy

The paper presents results achieved during realization of the international project DAMADICS (Development and Application of Methods for Actuator Diagnosis in Industrial control Systems). The proposed fault detection and isolation system is designed using a bank of dynamic neural networks. Each network is trained using a stochastic approximation method, which can be viewed as a fast alternative to back-propagation based algorithm. Simulation results are carried out using the real process data recorded at the Lublin Sugar Factory, Poland. © 2003 International Federation of Automatic control.

关键词： Failure analysis

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Dynamic Neural Networks for Actuator Fault Diagnosis: Application to the DAMADICS Benchmark Problem

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IFAC Proceedings Volumes 2003年第5期36卷 975-980页

作者： Krzysztof Patan Thomas Parisini Institute of Control and Computation Engineering University of Zielona Góra ul. Podgórna 50 65-246 Zielona Góra Poland Dept. of Electrical Electronic and Computer Engineering DEEI-University of Trieste Via Valerio 10 34127 Trieste Italy

The paper presents results achieved during realization of the international project DAMADICS ( Development and Application of Methods for Actuator Diagnosis in Industrial control Systems ). The proposed fault detection and isolation system is designed using a bank of dynamic neural networks. Each network is trained using a stochastic approximation method, which can be viewed as a fast alternative to back-propagation based algorithm. Simulation results are carried out using the real process data recorded at the Lublin Sugar Factory, Poland.

关键词： Actuators benchmark examples fault diagnosis dynamic models neural networks stochastic approximation performance indexes

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Stochastic approaches to dynamic neural network training. Actuator fault diagnosis study 15th

Stochastic approaches to dynamic neural network training. Ac...

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15th World Congress of the International Federation of Automatic control, 2002

作者： Patan, Krzysztof Parisini, Thomas Institute of Control and Computation Engineering University of Zielona Góra ul. Podgórna 50 Zielona Córa65-246 Poland Dept. of Electrical Electronic and Computer Engineering DEEI-University of Trieste via Valeno 10 Trieste34127 Italy

ISBN: (纸本)9783902661746

A paper deals with application of stochastic methods for dynamic neural network training. The considered network is composed of dynamic neurons, which contain inner feedbacks. This network can be used as a part of a fault diagnosis system to generate residuals. Up-to-date training algorithms, based on the classical back propagation, suffer from entrapment in local minima of an error function. Two stochastic algorithms are tested as training algorithms to overcome these difficulties. Efficiency of the proposed learning methods is checked using data recorded at Lublin Sugar Factory, Poland. Copyright © 2002 IFAC.

关键词： Fault detection

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Neuro-fuzzy structures in FDI system 15th

Neuro-fuzzy structures in FDI system

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15th World Congress of the International Federation of Automatic control, 2002

作者： Mendes, M.J.G.C. Kowal, M. Korbicz, J. Sá Da Costa, J.M.G. IDMEC/ISEL - Instituto Superior de Engenharia de Lisboa Polytechnic Institute of Lisbon Dept. of Mechanical Engineering Rua Conselheiro Emidio Navarro Lisboa1949-014 Portugal University of Zielona Góra Institute of Control and Computation Engineering ul. Podgórna 50 Zielona Góra65-246 Poland Technical University of Lisbon Instituto Superior Técnico Dept. of Mechanical Engineering GCAR/IDMEC Av. Rovisco Pais Lisboa1049-001 Portugal

ISBN: (纸本)9783902661746

Fault diagnosis systems have an important role in industrial plants because the early fault detection and isolation (FDI) can minimize damages in the plants. The main aim of this work is to propose a two-stage neuro-fuzzy approach as a fault diagnosis system in dynamic processes. The first stage of the system is responsible for fault detection and is implemented using a neuro-fuzzy (N-F) model. The second stage of the system is responsible for fault isolation and is built using an hierarchical structure of fuzzy neural networks. The FDI system is applied to fault diagnosis in the actuators of one sugar factory. Copyright © 2002 IFAC.

关键词： Fault detection

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LMI based stability analysis and controller design for a class of 2D continuous-discrete linear systems

LMI based stability analysis and controller design for a cla...

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American control Conference (ACC)

作者： K. Galkowski W. Paszke B. Sulikowski E. Rogers D.H. Owens Institute of Control and Computation Engineering University of Zielona Gora Zielona Gora Poland Dept. Electronics and Computer Science University of Southampton Southampton U.K. Dept Automatic Control and Systems Engineering University of Sheffield Sheffield U.K

Differential linear repetitive processes are a distinct class of 2D continuous-discrete linear systems of both applications and systems theoretic interest. In the latter area, they arise, for example, in the analysis of both iterative learning control schemes and iterative algorithms for computing the solutions of nonlinear dynamic optimal control algorithms based on the maximum principle. Repetitive processes cannot be analysed/controlled by direct application of existing systems theory and to date there are few results on the specification and design of control schemes for them. The paper uses an LMI setting to develop the first really significant results in this problem domain.

关键词： Stability analysis control systems Linear systems System testing Iterative algorithms Automatic control Algorithm design and analysis Optimal control control engineering computing Design engineering

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STOCHASTIC APPROACHES TO DYNAMIC NEURAL NETWORK TRAINING. ACTUATOR FAULT DIAGNOSIS STUDY

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IFAC Proceedings Volumes 2002年第1期35卷 53-58页

关键词： Actuators dynamic modelling fault detection learning algorithms neural network models

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