Advanced Tutorial Workshops, Sunday, June 30, 1995, 8:00 to 15:30
A-1: Design and Analysis of Robust Control Systems
A-2: Control Issues in Intelligent Vehicle Highway Systems
A-3: Supervision, Fault Detection and Diagnosis of Technical Systems
A-4: Techniques for Time-Critical Software Design
A-5: Control of Semiconductor Manufacturing Processes
A-6: Automated Multivariable System Identification: Basic Principles and Control Applications
A practical approach to robust control problems is to study the influence of uncertain plant parameters on system stability. This is in contrast to mathematically formulated problem descriptions where deviations from a nominal model are bounded by certain norms. This workshop deals with non-conservative design and analysis methods for plants with physical parameter uncertainties.
The problem for robust controller design is to find a controller which robustly stabilizes the plant for the entire operating domain of the uncertain parameters.
For a nonconservative design procedure it is in general not possible to determine a controller taking into account the operating domain. An application oriented solution to this problem is to find a controller which simultaneously stabilizes a finite number of representatives of the given operating domain of the uncertain parameters. To guarantee stability for the entire operating domain, the closed loop system with the selected controller has to undergo a precise analysis.
This workshop focuses mainly on the parameter space approach, but other design and analysis methods like value set approach and real stability radius will also be presented.
For technical problems, Hurwitz-stability by itself is mostly insufficient. So-called Gamma-regions which are subsets of the left complex half plane can be used to define stability margins and to incorporate specifications in the time domain. All methods presented in this workshop can be applied to Gamma-stability.
CACSD-tools will be used throughout the course for design and analysis of robust control systems.
Reference: J. Ackermann, A. Bartlett, D. Kaesbauer, W. Sienel, and R. Steinhauser, Robust Control: Systems with Uncertain Physical Parameters, Springer, London 1993.
This one-day workshop will emphasize advanced vehicle control concepts such as AICC (autonomous intelligent cruise control), coordinated control such as automated longitudinal control of vehicles in "platoons", automated lateral control, and total vehicle control. In addition to vehicle control algorithm development, an explanation of Automated Highway System architecture and operating concepts will be discussed. Topics shall include control oriented vehicle dynamic models, control algorithm development, vehicle simulation and "system level" simulation, as well as field test results from the California PATH research program.
Improvement of reliability and safety of technical systems requires advanced methods of supervision including fault detection and fault diagnosis. This workshop reviews the basic principles of model based fault detection and fault diagnosis methods (based on analytical and heuristic knowledge) and appropriate actions. The introductory lecture describes classical and advanced methods of monitoring, automatic protection, and supervision; the generation of analytical and heuristic symptoms; and an overview of fault detection and fault diagnosis methods. This is then followed by reviewing the most important model based fault detection methods (via parameter estimation, state observers, and parity approaches) and by describing change detection methods. Based on analytical (model based) and heuristic (operator observed) symptoms, a fault diagnosis can be performed by using classification methods or fault-symptom trees and methods of approximate reasoning. Then redundancy and reconfiguration schemes are described to cope with faults and to avoid process malfunctions and failures. Other topics include human factors.
Application examples are used throughout the workshop, showing practical results obtained e.g. for machine tools, robots, engines, automobiles, actuators, and sensors.
This workshop will introduce, critically appraise, and contrast techniques which are currently available for specifying and designing real-time software for time-critical applications, where it is essential to ensure that all the timing requirements are satisfied. The workshop will cover frequently used methods, such as Petri nets, other state-transition-based methods and temporal logic, and will introduce a new range of techniques, based on the Q-methodology, which are now being developed within a major trans-European technology transfer program. The workshop reviews the problem of specifying real-time systems(in which temporal correctness is as essential as logical correctness), the nature of time in real-time computing systems, existing approaches to specification and design(with special attention given to analysis and verification), the Q-methodology, and a detailed case study, comparing and contrasting various time-critical software design approaches.
This one day workshop addresses the emerging area of control technology applied to semiconductor manufacturing. Modern integrated circuits play a major role in almost all high technology products. Due to the importance of the electronics industry to international economy, the application of real-time control technology to improve performance of semiconductor manufacturing --- as measured by improved yield, decreased minimal feature size, lower power, and higher device density --- is a rapidly developing research area. This workshop draws extensively on the results and experiences from current research on processes such as plasma etching and deposition, and control of rapid thermal processing. The workshop is accessible to second year graduate students, engineers in industry, and other interested researchers.
The workshop will follow the format of a similar Workshop offered at the 1995 American Control Conference by the same organizers. The morning session will be devoted to background and overview material in semiconductor processes, sensors, actuators, and appropriate control technologies. The afternoon will focus on specific case studies presented by the organizers and other selected researchers from the field. The general outline for the course is,
Technology transfer
Over the past several years, computational methods and software have been developed to reliably identify system dynamics from input/output data with optimal statistical accuracy. These automatic methods apply to a very general class of linear systems including multi-input/multi-output, state and measurement noise disturbances, unknown feedback, unknown state order, and possibly unstable or highly resonant dynamics. Existing methods for high accuracy identification such as Box/Jenkins and prediction error methods are problematic in that they are both computationally unreliable and involve a tedious toolbox approach requiring graduate level training.
The automatic methods presented in this workshop are fundamentally different and involve direct determination of the system states, i.e. system rank, using stable singular value decomposition (SVD) computations. Optimal rank selection based on canonical variate analysis (CVA) is related to partial least squares (PLS) and principal component analysis (PCA) methods. Statistical order selection methods are described which give optimal determination of state order. The state space dynamics are determined by simple multivariate regression. The concepts are presented in a direct first principles way that is appropriate for advanced undergraduate and graduate curriculum so that automated system identification can be made much more accessible to those in most need of using it. This advance in system identification has major implications for analysis, system monitoring, and design and implementation of control systems for many applications including aerospace systems and industrial process control. Several such examples are presented including an industrial recovery boiler, stirred tank reactor, autothermal reactor, distillation column, and on-line adaptive control of aircraft wing flutter. Automated system identification methods are compared with alternative approaches in terms of model types considered, required user knowledge, computational requirements and reliability, and results of model fitting using simulated data sets.
The intended audience includes those wishing to do model identification on applications data, those wishing an introduction to the concepts of automated system identification, those considering teaching an undergraduate or graduate course on the subject, or those with more advanced background. A draft of an introductory textbook on automated system identification including Matlab compatible software will be included in the course.