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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Constantin F. Caruntu Cosmin Copot Corneliu Lazar Robin De Keyser Faculty of Automatic Control and Computer Engineering Gheorghe Asachi Technical University of Iasi Iasi Romania Department of Electrical energy Systems and Automation Ghent University Ghent Belgium

The increased traffic flow on both existing highways and city roads leads to the appearance of stop-and-go waves which have many disadvantages, e.g., increased risk of accidents, higher fuel consumption, increased pollution, increased wear of vehicle mechanical parts, driving stress and passenger discomfort. The platooning of vehicles could be the solution to this problem by enforcing them to follow each other and to maintain a safe distance between them. The advantages brought by this solution depend on ensuring the string stability of the vehicle platoon. In this paper the leader-follower system is employed to illustrate the stop-and-go waves mitigation. Several controllers (P, PI) were designed based on the conditions that have to be met in order to ensure the string stability of the vehicle platoon and comparisons were made with a controller based on the driver behavior.

关键词： Vehicles Mobile robots Accidents Stability criteria Transfer functions

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A Framework for the Nonlinear control of Dual-Stage Systems

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IFAC Proceedings Volumes 2014年第3期47卷 1140-1145页

作者： Aurélio T. Salton Jeferson V. Flores João M. Gomes da Silva Jinchuan Zheng Zhiyong Chen Group of Automation and Control Systems (GACS) Pontifícia Universidade Católica do Rio Grande do Sul Brazil Department of Electrical Engineering Universidade Federal do Rio Grande do Sul Brazil Faculty of Engineering and Industrial Sciences Swinburne University of Technology Australia School of Electrical Engineering and Computer Science The University of Newcastle Australia

This paper presents a framework for the nonlinear control of dual-stage actuators (DSA). Motivated by various nonlinear controllers that make use of sector bounded and £ ∞ nonlinearities for the control of saturated linear systems, a methodology for integrating such nonlinear functions in order to improve the performance of DSA is presented. The stability of the closed-loop system is assessed by casting the nonlinearities in a mixed sector-bounded plus quasi-Linear Parameter Varying (LPV) framework, leading to a set of linear matrix inequalities (LMIs) to be satisfied by the controller parameters. Taking advantage of the developed framework, a new £ ∞ function is proposed to avoid the saturation of the secondary actuator. Simulation results illustrate the validity of the proposed framework and its potential for the performance improvement of DSA.

关键词： DSA Nonlinear Feedback Time-Optimal Performance Macro-Micro Manipulator

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Extreme learning machine for the displacement prediction of landslide under rainfall and reservoir level

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Stochastic Environmental Research and Risk Assessment 2014年第8期28卷 1957-1972页

作者： Cheng Lian Zhigang Zeng Wei Yao Huiming Tang School of Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education Wuhan China School of Computer Science South-Central University for Nationalities Wuhan China Faculty of Engineering China University of Geosciences Wuhan China

Landslide prediction is always the emphasis of landslide research. Using global positioning system GPS technologies to monitor the superficial displacements of landslide is a very useful and direct method in landslide evolution analysis. In this paper, an EEMD–ELM model [ensemble empirical mode decomposition (EEMD) based extreme learning machine (ELM) ensemble learning paradigm] is proposed to analysis the monitoring data for landslide displacement prediction. The rainfall data and reservoir level fluctuation data are also integrated into the study. The rainfall series, reservoir level fluctuation series and landslide accumulative displacement series are all decomposed into the residual series and a limited number of intrinsic mode functions with different frequencies from high to low using EEMD technique. A novel neural network technique, ELM, is employed to study the interactions of these sub-series at different frequency affecting landslide occurrence. Each sub-series extracted from accumulative displacement of landslide is forecasted respectively by establishing appropriate ELM model. The final prediction result is obtained by summing up the calculated predictive displacement value of each sub. The EEMD–ELM model shows the best accuracy comparing with basic artificial neural network models through forecasting the displacement of Baishuihe landslide in the Three Gorges reservoir area of China.

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Performance of combined artificial neural networks for forecasting landslide displacement

Performance of combined artificial neural networks for forec...

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International Joint Conference on Neural Networks (IJCNN)

作者： Cheng Lian Zhigang Zeng Wei Yao Huiming Tang School of Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan Hubei China School of Computer Science South-Central University for Nationalities Wuhan China Faculty of Engineering China University of Geo-sciences Wuhan China

An efficient and accurate method for landslide displacement prediction is very important to reduce the casualties and property losses caused by this type of natural hazard. In recent years, many kinds of artificial neural networks (ANNs) have been widely applied to landslide displacement prediction. But we can't know which type of ANN is the best until we have calculated the prediction error. An improper choice of ANN may result in bad prediction results. In this paper, we use a neural networks combination prediction method based on the discounted MSFE (mean squared forecast error) to reduce the risk of selecting the types of ANNs. Four popular ANNs, radial basis function neural network (RBFNN), support vector regression (SVR), least squares support vector machine (LSSVM) and extreme learning machine (ELM), are selected as candidate neural networks. The performance of our model is verified through two case studies in Baishuihe landslide and Bazimen landslide. Experimental results reveal that the combining neural networks can improve the generalization abilities of ANNs.

关键词： Terrain factors Artificial neural networks Monitoring Predictive models Kernel Training

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Observer based output feedback tuning for underwater remotely operated vehicle based on linear quadratic performance

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AIP Conference Proceedings 2015年第1期1660卷

作者： Mohd Shahrieel Mohd Aras Shahrum Shah Abdullah Muhammad Nizam Kamarudin Ahmad Fadzli Nizam Abdul Rahman Fadilah Abd Azis Hazriq Izzuan Jaafar Department of Mechatronics Faculty of Electrical Engineering Universiti Teknikal Malaysia Melaka Hang Tuah Jaya 76100 Durian Tunggal Melaka Malaysia Department of Electric and Electronics Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia International Campus Jalan Semarak 54100 Kuala Lumpur Malaysia Department of Control and Automation Faculty of Electrical Engineering Universiti Teknikal Malaysia Melaka Hang Tuah Jaya 76100 Durian Tunggal Melaka Malaysia Faculty of Information Technology and Computer Engineering Universiti Teknikal Malaysia Melaka Hang Tuah Jaya 76100 Durian Tunggal Melaka Malaysia

This paper describes the effectiveness of observer-based output feedback for Unmanned Underwater Vehicle (UUV) with Linear Quadratic Regulation (LQR) performance. Tuning of observer parameters is crucial for tracking purpose. Prior to tuning facility, the ranges of observer and LQR parameters are obtained via system output cum error. The validation of this technique using unmanned underwater vehicles called Remotely Operated Vehicle (ROV) modelling helps to improve steady state performance of system response. The ROV modeling is focused for depth control using ROV 1 developed by the Underwater Technology Research Group (UTeRG). The results are showing that this technique improves steady state performances in term of overshoot and settling time of the system response.

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Tuning the position of a fuzzy cognitive map attractor using backpropagation through time

Tuning the position of a fuzzy cognitive map attractor using...

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7th International Conference on Integrated Modeling and Analysis in Applied control and automation, IMAACA 2013, Held at the International Multidisciplinary Modeling and Simulation Multiconference, I3M 2013

作者： Gregor, Michal Groumpos, Peter P. Department of Control and Information Systems Faculty of Electrical Engineering University of Žilina Slovakia Laboratory for Automation and Robotics Department of Electrical and Computer Engineering University of Patras Greece

ISBN: (纸本)9781629934884

The paper proposes a new learning method for fuzzy cognitive maps, which makes it possible to encode an attractor into the map. The method is based on the principle of backpropagation through time known from the theory of artificial neural networks. Simulation results are presented to show how well the method performs. It is shown that the results are superior to those achieved using Hebbian learning approaches such as nonlinear Hebbian learning. Some lines for possible future research and development are given.

关键词： Fuzzy Cognitive Maps

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Towards an inclusive Parkinson's screening system

Towards an inclusive Parkinson's screening system

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Oana Geman Saeid Sanei Iuliana Chiuchisan Adrian Graur Aleš Procházka Oldřich Vyšata Department of Health and Human Development “Stefan eel Mare” University of Suceava Romania Department of Computer Science Electronics and Automation “Stefan cel Mare” University of Suceava Romania Faculty of Engineering and Physical Sciences University of Surrey United Kingdom Department of Computing and Control Engineering Institute of Chemical Technology Czech Republic Department of Neurology Charles University Czech Republic

In this study, brain, and gait dynamic information were combined and used for diagnosis and monitoring of Parkinson's disease (the most important Neurodegenerative Disorder). Analysis of the information corresponding to a prescribed movement involving tremor, and the related changes in brain connectivity is novel and original. Analytically, developing a space-time nonlinear adaptive system which fuses brain and gait information algorithmically is proposed here for the first time. The overall dynamic system will be constrained by the clinical impressions of the patient symptoms embedded in a knowledge-based system. The entire complex constrained problem were solved to enable a powerful model for recognition and monitoring of Parkinson's disease and establishing appropriate rules for its clinical following up.

关键词： Brain modeling Adaptation models Parkinson's disease Chaos Signal processing algorithms Nonlinear dynamical systems

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Rate control and flight stabilization for a quadrotor system

Rate control and flight stabilization for a quadrotor system

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Benjamin Jahn Alexander Barth Kai Wulff Tobias Simon Jan Römisch Control Engineering Group Computer Science and Automation Faculty Ilmenau University of Technology Ilmenau Germany Integrated Communication Systems Group Computer Science and Automation Faculty Ilmenau University of Technology Ilmenau Germany

In this paper we propose a stability augmentation system for a quadrotor, enabling direct rate control for a human pilot. Our system includes feedforward for rate control, feedback for disturbance attenuation, and an online optimization technique for handling input restrictions. We show that the proposed algorithms are able to stabilize the system under highly challenging flight conditions, such as wind gusts and fast manual descend. Our input restriction technique ensures that the revolution speeds for the desired torques are feasible. The complete control system is analyzed using MATLAB, and full experimental validation is provided based on a quadrotor prototype.

关键词： Mathematical model Torque Actuators Angular velocity Equations Feedforward neural networks Stability analysis

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Hardware-based parallel firefly algorithm for embedded applications

Hardware-based parallel firefly algorithm for embedded appli...

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2013 NASA/ESA Conference on Adaptive Hardware and Systems, AHS 2013

作者： Muñoz, Daniel M. Llanos, Carlos H. Dos Santos Coelho, Leandro Ayala-Rincón, Mauricio Electronics Engineering Graduate Program Faculty of Gama University of Brasilia Brasilia DF Brazil Department of Mechanical Engineering Automation and Control Group/GRACO University of Brasilia Brasilia DF Brazil Industrial and Systems Engineering Graduate Program LAS/PPGEPS Pontifical Catholic University of Parana Curitiba Parana Brazil Departments of Computer Science and Mathematics University of Brasilia Brasilia DF Brazil

ISBN: (纸本)9781467363839

The firefly algorithm (FA) is a new population-based metaheuristic bioinspired on the behavior of the flashing characteristics of fireflies. As a population-based algorithm, the FA suffers from large execution times specifically for embedded optimization problems with computational limitations. For reducing execution times we propose a hardware parallel architecture of the FA algorithm that facilitates the implementation in Field Programmable Gate Arrays (FPGAs). In addition, this work proposes the application of the opposition-based learning (OBL) approach to the FA algorithm. The respective hardware implementation (HPOFA) was mapped into a Virtex5 FPGA device and numerical experiments using four well-known benchmark problems demonstrate that the opposition-based approach allows the FA algorithm to improve its functionality, preserving the swarm diversity and avoiding the premature convergence problem. Synthesis results point out that the HPOFA architecture is effectively mapped in hardware and is suitable for embedded applications. © 2013 IEEE.

关键词： Parallel architectures

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Application of optimization technique for PID controller tuning in position tracking of pneumatic actuator system

Application of optimization technique for PID controller tun...

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International Colloquium on Signal Processing & Its Applications, CSPA

作者： N.H Sunar M.F. Rahmat Z. H. Ismail A. M. Faudzi Sy Najib Sy Salim Sharatul Izah Samsudin Department of Mechatronics and Control Engineering Faculty of Electrical Engineering Universiti Teknologi Malaysia Johor Malaysia Department of Control Instrument and Automation Faculty of Electrical Engineering Universiti Teknikal Malaysia Melaka Melaka Malaysia Department of Industrial Electronics Faculty of Electronics and Computer Engineering Universiti Teknikal Malaysia Melaka Melaka Malaysia

In this paper, two optimization techniques of Particle Swarm Optimization (PSO) and Firefly Algorithm (FA) is used to obtain the optimal PID control parameters. To represent the model of the system, system identification with ARX model structure is developed. The results are determined by analysis the step response characteristic of the system. It was observed that the performances of PID controller with PSO optimized parameters perform well in position tracking of the pneumatic actuator system.

关键词： Tuning Zinc Optimization Signal processing algorithms PD control Pneumatic actuators

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