For dynamic distributed systems modeled by partial differential equations, existing methods of sensor location in parameter estimation experiments are either limited to one-dimensional spatial domains or require large investments in software systems. With the expense of scanning and moving sensors, optimal placement presents a critical problem.

Sensor networks have recently come into prominence because they hold the potential to revolutionize a wide spectrum of both civilian and military applications. An ingenious characteristic of sensor networks is the distributed nature of data acquisition. Therefore they seem to be ideally prepared for the task of monitoring processes with spatio-temporal dynamics which constitute one of most general and important classes of systems in modelling of the real-world phenomena. It is clear that careful deployment and activation of sensor nodes are critical for collecting the most valuable information from the observed environment. Optimal Sensor Network Scheduling in Identification of Distributed Parameter Systems discusses the characteristic features of the sensor scheduling problem, analyzes classical and recent approaches, and proposes a wide range of original solutions, especially dedicated for networks with mobile and scanning nodes. Both researchers and practitioners will find the case studies, the proposed algorithms, and the numerical examples to be invaluable.

This judicious selection of articles combines mathematical and numerical methods to apply parameter estimation and optimum experimental design in a range of contexts. These include fields as diverse as biology, medicine, chemistry, environmental physics, image processing and computer vision. The material chosen was presented at a multidisciplinary workshop on parameter estimation held in 2009 in Heidelberg. The contributions show how indispensable efficient methods of applied mathematics and computer-based modeling can be to enhancing the quality of interdisciplinary research. The use of scientific computing to model, simulate, and optimize complex processes has become a standard methodology in many scientific fields, as well as in industry. Demonstrating that the use of state-of-the-art optimization techniques in a number of research areas has much potential for improvement, this book provides advanced numerical methods and the very latest results for the applications under consideration.

Introduction to and survey of parameter estimation; Probability; Introduction to statistics; Parameter estimation methods; Introduction to linear estimation; Matrix analysis for linear parameter estimation; Minimization of sum of squares functions for models nonlinear in parameters; Design of optimal experiments.

Proceedings of the European Control Conference 1995, Rome, Italy 5-8 September 1995

Control and Dynamic Systems: Advances in Theory and Applications, Volume 54: System Performance Improvement and Optimization Techniques and their Applications in Aerospace Systems covers the issue of aerospace system performance and optimization techniques in aerospace systems. This book is composed of 12 chapters and begins with an examination of the techniques for aircraft conceptual design for mission performance. The succeeding chapters describe the balances and optimized design for aircraft and spacecraft structures through finite element procedures and the application of the knowledge-based system techniques for pilot aiding. These topics are followed by discussions of the optimal sensor placement for on-orbit modal identification experiments; the optimization techniques for helicopter airframe vibrations design; the size reduction techniques for efficient aeroservoelastic model determination; sensitivity analysis of eigendata of aeroelastic systems; and a simplified solution for transient structural dynamic problems with local nonlinearities. Other chapters explore a reduction algorithm for systems with integrators and the techniques for overcoming the difficulty of nonuniqueness of mode shape in modal analysis when random input data are not or cannot be measured. The last chapters consider the combined concepts of Krylov vectors and parameter matching and their application to develop model-reduction algorithms for structural dynamics. These chapters also provide the techniques for the development of new tracking algorithms that would incorporate explicit models of the maneuvering/nonmaneuvering phases of target encounter. This book will prove useful to aerospace, control, systems, and design engineers.

Control of Distributed Parameter Systems 1982 covers the proceeding of the Third International Federation of Automatic Control (IFAC) Symposium on Control of Distributed Parameter Systems. The book reviews papers that tackle issues concerning the control of distributed parameter systems, such as modeling, identification, estimation, stabilization, optimization, and energy system. The topics that the book tackles include notes on optimal and estimation result of nonlinear systems; approximation of the parameter identification problem in distributed parameters systems; and optimal control of a punctually located heat source. This text also encompasses the stabilization of nonlinear parabolic equations and the decoupling approach to the control of large spaceborne antenna systems. Stability of Hilbert space contraction semigroups and the tracking problem in the fractional representation approach are also discussed. This book will be of great interest to researchers and professionals whose work concerns automated control systems.