Robotics, Automation and Control IV Published by In-Teh In-Teh is Croatian branch of I-Tech Education and Publishing KG, Vienna, Austria. Abstracting and non-profit use of the material is permitted with credit to the source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. Publisher assumes no responsibility liability for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained inside. After this work has been published by the In-Teh, authors have the right to republish it, in whole or part, in any publication of which they are an author or editor, and the make other personal use of the work. © 2008 In-teh www.in-teh.org Additional copies can be obtained from: publication@ars-journal.com First published October 2008 Printed in Croatia A catalogue record for this book is available from the University Library Rijeka under no. 120101001 Robotics, Automation and Control, Edited by Pavla Pecherková, Miroslav Flídr and Jindřich Duník p. cm. ISBN 978-953-7619-18-3 1. Robotics, Automation and Control, Pavla Pecherková, Miroslav Flídr and Jindřich Duník Preface This book was conceived as a gathering place of new ideas from academia, industry, research and practice in the fields of robotics, automation and control. The aim of the book was to point out interactions among the various fields of interests in spite of diversity and narrow specializations which prevail in the current research. We believe that the resulting collection of papers fulfills the aim of the book. The book presents twenty four chapters in total. The scope of the topics presented in the individual chapters ranges from classical control and estimation problems to the latest artificial intelligence techniques. Moreover, whenever possible and appropriate, the proposed solutions and theories are applied to real-world problems. The common denominator of all included chapters appears to be a synergy of various specializations. This synergy yields deeper understanding of the treated problems. Each new approach applied to a particular problem, may enrich and inspire improvements of already established approaches to the problem. We would like to express our gratitude to the whole team who made this book possible. We hope that this book will provide new ideas and stimulation for your research. October 2008 Editors Pavla Pecherková Miroslav Flídr Jindřich Duník Contents Preface V 1. Multi-Domain Modelling and Control in Mechatronics: the Case of Common Rail Injection Systems 001 Paolo Lino and Bruno Maione 2. Time-Frequency Representation of Signals Using Kalman Filter 023 Jindřich Liška and Eduard Janeček 3. Discrete-Event Dynamic Systems Modelling Distributed Multi-Agent Control of Intermodal Container Terminals 039 Guido Maione 4. Inclusion of Expert Rules into Normalized Management Models for Description of MIB Structure 059 Antonio Martin and Carlos Leon 5. Robust and Active Trajectory Tracking for an Autonomous Helicopter under Wind Gust 079 Adnan Martini, François Léonard and Gabriel Abba 6. An Artificial Neural Network Based Learning Method for Mobile Robot Localization 103 Matthew Conforth and Yan Meng 7. The Identification of Models of External Loads 113 Yuri Menshikov 8. Environment Modelling with an Autonomous Mobile Robot for Cultural Heritage Preservation and Remote Access 123 Grazia Cicirelli and Annalisa Milella VIII 9. On-line Cutting Tool Condition Monitoring in Machining Processes using Artificial Intelligence 143 Antonio J. Vallejo, Rubén Morales-Menéndez and J.R. Alique 10. Controlled Use of Subgoals in Reinforcement Learning 167 Junichi Murata 11. Fault Detection Algorithm Based on Filters Bank Derived from Wavelet Packets 183 Oussama Mustapha, Mohamad Khalil, Ghaleb Hoblos, Houcine Chafouk and Dimitri Lefebvre 12. Pareto Optimum Design of Robust Controllers for Systems with Parametric Uncertainties 205 Amir Hajiloo, Nader Nariman-zadeh and Ali Moeini 13. Genetic Reinforcement Learning Algorithms for On-line Fuzzy Inference System Tuning “Application to Mobile Robotic” 227 Abdelkrim Nemra and Hacene Rezine 14. Control of Redundant Submarine Robot Arms under Holonomic Constraints 257 E. Olguín-Díaz, V. Parra-Vega and D. Navarro-Alarcón 15. Predictive Control with Local Visual Data 289 Lluís Pacheco, Ningsu Luo and Xavier Cufí 16. New Trends in Evaluation of the Sensors Output 307 Michal Pavlik, Jiri Haze and Radimir Vrba 17. Modelling and Simultaneous Estimation of State and Parameters of Traffic System 319 Pavla Pecherková, Jindřich Duník and Miroslav Flídr 18. A Human Factors Approach to Supervisory Control Interface Improvement 337 Pere Ponsa, Ramon Vilanova, Marta Díaz and Anton Gomà 19. An Approach to Tune PID Fuzzy Logic Controllers Based on Reinforcement Learning Hacene Rezine, Louali Rabah, Jèrome Faucher and Pascal Maussion 353 IX 20. Autonomous Robot Navigation using Flatness-based Control and Multi-Sensor Fusion 395 Gerasimos G. Rigatos 21. An Improved Real-Time Particle Filter for Robot Localization 417 Dario Lodi Rizzini and Stefano Caselli 22. Dependability of Autonomous Mobile Systems 435 Jan Rüdiger, AchimWagner and Essam Badreddin 23. Model-free Subspace Based Dynamic Control of Mechanical Manipulators 457 Muhammad Saad Saleem and Ibrahim A. Sultan 24. The Verification of Temporal KBS: SPARSE - A Case Study in Power Systems 473 Jorge Santos, Zita Vale, Carlos Serôdio and Carlos Ramos [...]... valve and the rail pressure as the input u and output y respectively, a family of ARX models can be obtained, according the above mentioned design steps (Lino et al., 2008): (1 − a z ) y (t ) = (b z 1 1 0 1 ) − b1 z −2 u ( t ) (13 ) 10 Robotics, Automation and Control where z -1 is the shift operator and a1, b0, b1 are constant parameters The j-step optimal predictor of a system described by eq (13 )... measurement and control Functions ηa and ηb also depend on parameters which are uncertain (i.e camshaft angular position and cylinders pressure) and not available for control purpose The aim of the control action u is to take pp and pr close to the constant set-points Pp and Pr Hence, by defining ep = pp – Pp and er = pr – Pr, equation ( 21) become: e p = η a ( e p , er , ωrpm , θ ) er = f1 ( e p , er... working condition and had a sort of Multi-Domain Modelling and Control in Mechatronics: the Case of Common Rail Injection Systems 17 ramp profile as well The final design step consisted in the application of the GPC control law to the real system In Fig 7, the engine speed accelerates from 11 00rpm to 18 00rpm and then decelerates to 11 00rpm, within a 20s time interval (Fig 7b) The control action applied... fluid and the proper air/fuel mixture demanded by its speed and load 12 Robotics, Automation and Control 4 .1 Virtual prototype of the common rail diesel injection system To build the AMESim model for the common rail diesel injection system, similar assumptions than the previous case are made concerning pressure distributions within lumped volumes like common rail, high pressure pump and injectors control. .. rail and injectors The long pipe connecting delivery valve to common rail is modelled by using the Simple wave equation hydraulic pipe, which is based on the continuity equation (19 ) and momentum equation, giving for uncompressible fluids: 14 Robotics, Automation and Control ∂q A ∂p ∂q f q q =0 − +v + ∂t ρ ∂x ∂x 2 dA (20) being ρ the fuel density, d the pipe internal diameter, v the mean flow speed and. .. is (Rossiter, 2003): ˆ y (t + j|t ) = G j Δu(t + j − 1) + Fj y (t ) (14 ) where Gj and Fj are polynomials in q -1, and Δ is the discrete derivative operator Let r be the vector of elements y(t+j), j =1, , N, depending on known values at time t Then eq (14 ) can T ~ ~ ˆ be expressed in the matrix form y = Gu + r , being u = [Δu(t ),… , Δu(t + N − 1) ] , and G a lower triangular N×N matrix (Rossiter, 2003)... condition, and compares experimental and simulation results With a constant 40 bar input pressure, the system behaviour for a 16 Robotics, Automation and Control constant tj = 3ms injectors opening time interval, while varying engine speed and solenoid valve driving signal has been evaluated The engine speed is composed of ramp profiles (6c), while the duty cycle changes abruptly within the interval [2%, 12 %].. .1 Multi-Domain Modelling and Control in Mechatronics: the Case of Common Rail Injection Systems Paolo Lino and Bruno Maione Dipartimento di Elettrotecnica ed Elettronica, Politecnico di Bari Via Re David 200, 7 012 5 Bari, Italy 1 Introduction The optimal design of a mechatronic system calls for the proper dimensioning of mechanical, electronic and embedded control subsystems (Dieterle,... pump pressure; (d), (e), (f) rail pressure Multi-Domain Modelling and Control in Mechatronics: the Case of Common Rail Injection Systems 19 Fig 8 compares Matlab and AMESim simulations referred to two complete camshaft revolutions This figure represents pump and rail pressures, for 800, 13 00 and 18 00 rpm camshaft speeds respectively, and different values of the electro-hydraulic valve duty-cycle According... Δu(t ) = k1 w(t ) + (k2 + k3q 1 )y (t ) + k4 Δu(t − 1) (17 ) where [k1, k2, k3, k4] depends on N 4 The common rail injection system of diesel engines The main elements of the common rail diesel injection system in Fig 4 are a low pressure circuit, including the fuel tank and a low pressure pump, a high pressure pump with a delivery valve, a common rail and the electro-injectors (Stumpp & Ricco, 19 96) Few . under no. 12 010 10 01 Robotics, Automation and Control, Edited by Pavla Pecherková, Miroslav Flídr and Jindřich Duník p. cm. ISBN 978-953-7 619 -18 -3 1. Robotics, Automation and Control, Pavla. 2008): ( ) ( ) ( ) ( ) −−− −=− 11 2 10 1 1 az y t bz bz u t (13 ) Robotics, Automation and Control 10 where z -1 is the shift operator and a 1 , b 0 , b 1 are constant parameters. The. Luo and Xavier Cufí 16 . New Trends in Evaluation of the Sensors Output 307 Michal Pavlik, Jiri Haze and Radimir Vrba 17 . Modelling and Simultaneous Estimation of State and