ICGST International Conference on Automatic Control and System Engineering, ACSE 06 (ACSE 06)
Venue: Berli, Germany
|Event Date/Time: Jul 05, 2006||End Date/Time: Jul 07, 2006|
|Registration Date: May 15, 2006|
|Early Registration Date: Apr 15, 2006|
|Abstract Submission Date: Mar 15, 2006|
|Paper Submission Date: Mar 15, 2006|
The international conference on Automatic Control and System Engineering ACSE-06 publishes research papers on theoretical analysis, experimental studies and innovations concerning automatic control and system engineering. This conference is, therefore, not only a forum where researchers, scientists, engineers and vendors can exchange their knowledge and experience, but also serves as an educational channel for students and any others who would like to learn more about the new trends in this field of study. The ACSE-06 publishes work that addresses the application of methods to real world scenes and seeks to strengthen a deeper understanding in the discipline by encouraging the qualitative and quantitative performance evaluation of the emerging subjects of automatic control and system engineering.
The coverage includes:
Embedded Control Systems.
Adaptive Control Techniques
Object-Oriented Petri Nets
Real-Time and Fault-Tolerant Systems
Alarming and Fault Diagnosis Systems
Fuzzy Control Systems
Stability, Controllability and Observations
Robot and Manipulator Control
Pursuing and Tracking
Linear and Non-Linear Systems
Power System Control
Perceptual Control Systems
Autonomous Traffic and Transport Systems
Modeling and Simulator Building
Modeling, Estimation and Prediction
Automatic Control of Chemical Processes
Automotive Control Systems and Autonomous Vehicles
Thermal System Control
Sensors, Actuators and Transducers
Industrial Control Electronics
Biologically inspired Control Techniques
Data Acquisition and Measurement engineering
Studies on Signal and System
Large Scale Control Systems
Intelligent Control Systems
Aerospace Control Systems
Defense and Military Systems Control
Digital and Analogue Control
Motion and Navigation Control
Temporal and Spectral System Analysis
Studies on nuclear systems control
Control Analysis of Social and Human Systems
Biomedical control systems
Some of ACSE-06 Special Sessions
Hardware for ANNs and Fuzzy Controllers
Artificial Neural Networks (ANNs) as well as Fuzzy Systems (FS) are omnipresent in almost every intelligent system design. Just to name few, engineering, control, economics and forecasting are some of the scientific fields that enjoy the use of ANN and FS. Unfortunately, the majority of the Neuro and/or Fuzzy applications are complex and so require a large computational effort to yield useful and practical results. Therefore, dedicated hardware for Neuro-Fuzzy computation is becoming a key issue for designers. With the spread of reconfigurable hardware such as FPGAs and FPAAs, digital as well as analog hardware implementations of such computation become cost-effective. The focus of this special session will be on all aspects of Neuro and/or Fuzzy embedded hardware and controllers. Of special interest are contributions that describe new and efficient hardware architectures and high speed implementations of Neuro and/or Fuzzy controllers.
Chaotic Dynamics and Nonlinearity Analysis in Time Series Data
This session provides a forum in which researchers and practitioners present work on working on nonlinear dynamics analysis for predictability of time series data. The session will be of interest to people who are doing active research on nonstandard computation models that involves chaotic dynamics, or nonlinear dynamic analysis and its applications in various fields such as Economics and Medicine, or mining of time series data for predictability.
Safety & Security in System of Systems
Complex Systems and Systems of Systems manifest many emergent properties amongst which safety and security are increasingly valued, demanded and regulated. This session would provide a tutorial on advanced approaches to the assurance of safe and secure performance in large/complex systems and system of systems. The participants will be introduced to advanced systemic frameworks necessary for such endeavors whilst a debate will be held to engage the participants in the process and advances in this evolving but largely underdeveloped discipline.
Control systems based on ANNs & fuzzy Logic
This session focuses on fuzzy or/and neural network control design for various systems. Topics covered in the session include: adaptive PID-Like fuzzy-neural controller applied to the nonlinear model reference control system, adaptive bound reduced-form genetic algorithm (ABRGA) to train B-spline membership function (BMF) fuzzy-neural sliding mode controller for guaranteeing robust stability and tracking performance for robot manipulators with uncertainties and external disturbances; an adaptive fuzzy sliding mode control for uncertain time-delay systems with sector nonlinearities; a complementary variable structure speed control scheme using fuzzy logic for a switched reluctance motor to improve the tracking performance of the system, irrespective of the highly nonlinear characteristic of the electromagnetic torque; and a vision-based automatic guided vehicle hybrid control system that incorporates fuzzy adaptive mechanism with dynamic motion model for improving the efficiency and precision of vehicle guidance trajectory. Also, the session covers: a hybrid neural network control which combines neural network (NN) and cerebellar model articulation controller (CMAC) methodology; a modified Genetic Algorithm (MGA) to construct a fuzzy neural network (FNN) spontaneously; a practical application report on the neural-fuzzy controller design to accomplish a four-link robot standing up vertically and stably from a flat horizontal surface; a recurrent-neural-network based predictive control for a class of nonlinear discrete time systems; and an identification method for discrete-time nonlinear systems using a Hopfield neural network (HNN) to obtain optimized coefficients over a set of Gaussian basis functions.
Human-Robot Interaction is a rapidly developing field which presents novel challenges for researchers in robotics, autonomous systems, and computational intelligence. Due to the embodied nature of interaction involving humans and robots, it is not possible to merely transfer the methods of human-computer interaction design to robotics. Robots, that might act as servants or companions to humans in a home environment, need to be carefully designed to be beneficial and pleasant to interact with beyond the phase of initial novelty effects. The design of appropriate social interaction behaviors and methodologies for robots that interact with humans requires interdisciplinary work with psychology, as well as new methodologies for evaluation the evaluation of human-robot interaction. Social spaces, gestures, legibility of behavior, appearance and intentionality, represent key challenges. Robots in the home will not be accepted if they are annoying, irritating, or too social inept to be useful. Human-robot interaction design should not be technology-driven, but needs to respect human wholeness, human living spaces and activities, individual preferences. Adaptation of behavior of the robots to humans and learning from them in social interaction are also key challenges.
In the past decade robotics research has made many advances in control methodologies, sensory processing, and planning strategies. Robots are now increasingly expected to function in uncertain, dynamic real world environments, and to closely interact with untrained humans. Handling such environment poses many challenging problems. Control methodologies of multi-robot systems have also advanced considerably. Such systems can often deal with tasks that are difficult if not impossible for a single robot. This relatively new field offers many interesting research issues.
Biologically inspired instrumentation, sensors and measuring techniques
This special session will focus on all the aspects of the biologically inspired technologies for instrumentation and measurement applications. Original papers are solicited in, but are not limited to the following technical areas: models of the biological sensing and perception mechanisms, random-pulse/random-data instrumentation and artificial NN architectures, biologically inspired sensors (visual, haptic, audio, smell, etc.), distributed sensor agent networks, adaptability, configurability, emergence, self organization, self optimization etc.
Measurement problems in medical applications are continuously increasing and several examples exist that process measurement data from several instruments in order to derive specific knowledge about the patient status from the vital parameters. This special session will focus on all the aspects related to sensors and measurements in the medical field. Main session topics, but not limited to, include: Sensors for medical systems and medical specific instrumentation; Embedded systems and signal processing; Sensor fusion and calibration; Standards and medical applications; Digital imaging and communication in medicine issues.
Multi-sensor and model based sensory systems
In order to improve the quality, availability and reliability of measurements several approaches can be pursued, which make use of multi sensor systems or model based sensor systems. Multi sensor systems exploit redundancy and diversity of sensor signals by using data fusion techniques. Model based sensor systems use a set of operating points of the sensor element for a better calculation of measurement values, correction of effects, self-test, self-validation, etc. This special session deals with all aspects related to the design, development, evaluation, and testing of multi sensor and model based sensor systems, such as: Multi sensor systems, Multi signal processing, data fusion, pattern recognition, Video measurement technology, Model-based sensor systems, Signal processing for smart sensor systems, Modeling of sensor signals, Model-based self-diagnosis and self-validation techniques.
Wireless sensor networks
The purpose of this special session is to present and discuss the latest analytic, systems, and deployment challenges in wireless sensor networks. Such networks, featuring myriads of tiny devices equipped with sensing, local actuation, communication, and processing, offer significant new problems in the design of real-time communication protocols, middleware services, and programming abstractions for massively distributed wireless computing. They bring about a need for new models of computation and real-time performance analysis, as well as new theory on which such models are based. The session hopes to bring together experts, practitioners and researchers, from academia and industry, to present challenges and solutions in this growing field.
Complex adaptive systems
This special session is concerned with fostering the formation of an active multi-disciplinary community on Complex Adaptive Systems. We especially intend to increase the awareness of researchers in many fields sharing the common view on combining agent-based modeling and the evolutionary computation model in order to develop insight and foster predictive methodologies. Complex adaptive systems involve the study of many agents and their rich interactions. A basic methodology is to specify how the agents interact, and then observe properties that occur at the collective level in order to discover predictive principles and key descriptive variables for understanding and/or shaping and harnessing the resulting dynamics. Generally the high-dimensional, non-linear nature of the resulting dynamical systems makes them difficult or impossible to analyze using traditional methods. Agents follow local rules under various constraints. The resulting dynamics are not necessarily derivable from any principles of analytic calculation. Under the action of evolution, such agents adapt to their environments and other agents' behaviors. The adaptation processes can be massively parallel, depending on the number of agents, and we especially need to explore the relationship between at the individual level and at the collective level. The idea of combining evolutionary computation and agent-based modeling is particularly rich and fresh and applicable to answer these issues. The emergent phenomena arising from interactions even among a small number of agents and their environment are not well-understood, e.g. in the evolution signaling, communication, and interaction dynamics. We will invite high quality contributions on a wide variety of topics relevant to the wide research areas of Complex Adaptive Systems. We will especially cover in-depth of important areas such as: Collective Behavior, Complex Networks of Adaptive Agents, Multiscale Robustness and Plasticity, Applications in Robotics & Sensor Evolution, Information-Theoretic Methods and Dynamical Systems Analyses for Complex Adaptive Systems, Signaling, Communication and Social Networks, Unconventional Computing Media Substrates for Complex Adaptive Systems, Applications and Models for Systems Biology, Multicellular Complex Adaptive Systems, Role of Constraints in Dynamics of Complex Adaptive Systems, Sensor-Actuator Evolution, Agent-based models: Theory and Simulations, Co-evolutionary Learning, Collective Learning, Particle Swarms, Replicator Dynamics, Applications to Nanotechnology and Medicine, Evolutionary Games, Evolutionary Reinforcement Learning Interacting Particle Systems, Learning of heterogeneous agents, Learning in Games, Markets as Complex Adaptive Systems, Scalable, Evolvable, Emergent Developmental Systems.
Swarm Algorithms in Control Systems
In this special session, we seek papers on applications of Swarm algorithms to challenging problems in design and tuning of control systems. Important issues in advanced control systems are autonomy, adaptability, robustness and fault-tolerance. We are looking for original contributions on new developments in Swarm algorithms to solve these problems. Approaches using Co-evolution, multi-objectives, and parallel implementations are encouraged. Topics include, but are not limited to, Robust, Adaptive, and Optimal Control, Design and Tuning Methodologies for Control Systems, Control Applications, Emerging Control Technologies, Modeling, Estimation, Identification and Optimization, Process Control, Power Systems, Real-time Control, Fault Detection, Robotics and Mechatronics, eg., Swarm Robotics, Guidance and Flight Control, Vehicles and Transportation Systems, Hybrid and Complex Systems.
Model Quality and Validation
Issues of model quality have received increased attention recently with the growth of “smart procurement” processes, particularly in the aerospace and defense related areas of industry. Modern trends in engineering design towards a more integrated approach in which hardware, software and the control aspects of complex systems are developed in parallel are also leading to greater interest in issues of model quality. Mechatronic systems provide many good examples where such an integrated approach to design is becoming well established and where issues concerning the adequacy or otherwise of mathematical and computer-based models are being highlighted. This session is intended to focus attention on some of the areas of current interest with a broadly-based review paper and other papers covering a range of topics including the identification of nonlinear systems, models in fault detection, links between computer-based modeling and established software engineering methods and simulation model development by reuse.
NN-based Optimization and Control for Complex Stochastic Processes
Complex stochastic processes mainly include the stochastic processes with nonlinearity and non-Gaussian variables, for which the optimization and control for the error mean or variance will be insufficient. In this case, stochastic distribution functions and their entropies (in various definitions) can be used to characterize the stochastic property of the processes. Consequently, the entropy optimization also depends on the solutions of the output PDFs. However, it is well-known that the output probability density functions (PDFs) obey a nonlinear partial differential equation even for the so-called Ito equation. In practice, an analytical expression for the PDF of a random variable, which is necessary for the computation of the entropy, is not available in most cases. In many practical processes (such as the batch processes), it is available to use neural networks (NNs) to model the output PDFs together with other tools such as Parzen windowing. With the NN expansions, one can set up the model-based analysis and synthesis for both the (non-Gaussian) signal processing and feedback optimization problems. In this case, it is possible to transform the infinite-dimensional optimization to a finite-dimensional problem. This is a new direction in the control and signal processing fields, and has been shown great significance in many batch processes in engineering. Sub-topics include (but not limited to): Stochastic Processes; Optimization and Control; Non-Gaussian Systems and Filter Design.
Simulation and Modeling
A special session on how simulation and modeling can enhance the quantitative evaluation of system performance and system intelligence will take place. Topics areas to be considered for this session include: How simulation and modeling can be used to test and validate real system performance, How successful are we at modeling intelligent systems? Real experiences with using simulation to develop, test, and validate intelligent system performance, Simulation and modeling in support of machine learning, Simulation and modeling of sensors, planners, and other components of intelligent systems, Ground truth capture for constructing simulations, and models, Modeling decision making with uncertain and incomplete information, Developing realistic system performance requirements using modeling and simulation tools, Evaluating multi-system control paradigms using modeling and simulation.
Feedback control and real-time systems
The topics of the session include any issue related to control in real-time systems and the interaction between computing and control systems, in particular: computational models and languages for control applications; implementation-aware and resource-constrained control systems; integration of control and scheduling incl. feedback scheduling; co-design tools for control, computing and communication; modeling and simulation of performance control; temporally robust control systems; hybrid system approaches in integrated control and computing; applications of control to real-time computing
Evolutionary Computation for Systems and Control Applications
Evolutionary computing techniques are capable of solving global optimization and search problems with robust performance. The advances in evolutionary computation techniques are making them more popular in solving complex, nonlinear, nonconvex and dynamically interactive problems. Evolutionary computation has been successfully applied in the areas of power systems engineering, including system planning, security assessment, decision making, electricity market management and control; Evolutionary computation has also been used to solve sophisticated control systems and communications engineering problems, such as system identification, linearization, optimal and robust control. The objective of this special session is to bring together research and development of evolutionary computation in system and control areas. The topics of interests are (but not limited to) evolutionary identification and modeling, evolutionary control system design, industrial applications, robotics and sensors, learning and optimization, evolutionary computation application in power systems engineering and communications.