How should I carry out the identification of my process in order to obtain a good model?
How can I assess the quality of a model with a view to using it in control design?
How can I ensure that a controller will stabilise a real process sufficiently well before implementation?
What is the most efficient method of order reduction to facilitate the implementation of high-order controllers?
Different tools, namely system identification, model/controller validation and order reduction are studied in a framework with a common basis: closed-loop identification with a controller that is close to optimal will deliver models with bias and variance errors ideally tuned for control design. As a result, rules are derived, applying to all the methods, that provide the practitioner with a clear way forward despite the apparently unconnected nature of the modelling tools. Detailed worked examples, representative of various industrial applications, are given: control of a mechanically flexible structure; a chemical process; and a nuclear power plant.
Process Modelling for Control uses mathematics of an intermediate level convenient to researchers with an interest in real applications and to practising control engineers interested in control theory. It will enable working control engineers to improve their methods and will provide academics and graduate students with an all-round view of recent results in modelling for control.
Doctor Codrons is ideally suited to authorship in the Advances in Industrial Control Series having both academic and industrial experience. He has worked for the large French electricity supply company Électricité de France and then a four-and-a-half-year academic appointment at the Université Catholique de Louvain studying control-oriented system modelling techniques. He now works for the research arm of the Belgian national supplier as a project leader in process control.
Beginning with a review of the fundamental principles of internal-model-based feedback control design, Robust Autonomous Guidance moves on to expound recent enhancements to such designs and then to their implementation in systems operating under conditions of great uncertainty.
The three case studies presented: attitude control of a low-Earth-orbit satellite and the landing of fixed- and rotary-winged aircraft on a ship involve control systems coping with a high degree of nonlinear behaviour. The key issues addressed in each case study are the design of an adaptive internal model for the specific tracking task and of stabilizing control capable of steering the tracking error to zero while keeping all internal states bounded for any arbitrarily large but bounded envelope of initial data and uncertain parameters. Nested saturated controls form the basis of novel tools for asymptotic analysis and design.
Robust Autonomous Guidance will be of great interest to academic and industrial researchers working with nonlinear control systems and to engineers involved in the design of aerospatial guidance systems. It will also be a useful reference for graduate students working with non-linear systems.
Control of Traffic Systems in Buildings presents the state of the art in the analysis and control of transportation systems in buildings focusing primarily on elevator groups. The theory and design of passenger traffic and cargo transport systems are covered, together with actual operational examples and topics of special current interest such as:
• noisy, on-line and algorithmic optimization;
• simulation-based modeling of passengers and goods;
• control of cooperative agent-oriented systems;
• proposal for a benchmark to compare new control methods;
• deployment and testing of transportation systems.
Special attention is given to the techniques and uses of simulation and a working simulator is included that allows readers to explore the subject for themselves.
The safe running of such automated traffic systems, though vital, gets rather taken for granted but workers in elevator control have pioneered the development of many modern control systems for employment in all sorts of traffic and scheduled systems being among the first to realize the potential of techniques like fuzzy logic, neural networks and genetic algorithms. For this reason, this exposition of recent work in in-building transport control will be of considerable interest to researchers and engineers in many areas of control, particularly those working in optimal or supervisory control, urban transportation systems and intelligent transport systems as well as to those directly interested in the elevator control systems under discussion.