THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Fault detection, isolation, and reconfiguration (FDIR) is an important and challenging problem in many engineering applications and continues to be an active area of research in the control community. This paper presents a survey of the various model-based FDIR methods developed in the last decade. In the paper, the FDIR problem is divided into the fault detection and isolation (FDI) step, and the controller reconfiguration step. For FDI, we discuss various model-based techniques to generate residuals that are robust to noise, unknown disturbance, and model uncertainties, as well as various statistical techniques of testing the residuals for abrupt changes (or faults). We then discuss various techniques of implementing reconfigurable control strategy in response to faults.

A Survey of Fault Detection, Isolation, and Reconfiguration Methods

Past, present and future of nonlinear system identification in structural dynamics

Technology developments in structural health monitoring of large-scale bridges

https://people.duke.edu/~hpgavin/SystemID/References/Markovsky+VanHuffel-SP-2007.pdf

Overview of total least-squares methods

Detection of nonlinear dynamic behaviour of mechanical structures IMAC-86)

https://lirias.kuleuven.be/bitstream/123456789/577246/1/114_Rottiers.pdf

Mechatronic powertrain virtual sensor: system-level model and sensor selection

Strain-Based Experimental Modal Analysis: Use of Mode Curvature and Strain-to-Displacement Relations

This paper presents an investigation of Gaussian Processes (GPs) in combination with model predictive control (MPC) for autonomous driving control on slippery snowy road conditions. A double lane change scenario with two different road friction coefficients is considered for learning the GP model. The model is then incorporated into the MPC algorithm development. The performance of the GP-MPC controller is evaluated and compared with conventional MPC controller. The validation is conducted based on a co-simulation platform that simulates high fidelity vehicle/tire dynamics and snowy traffic environment in different setting conditions, respectively. The results demonstrate that the GP-MPC controller can achieve better trajectory tracking performance and with less control input than the conventional MPC controller however with higher computation time.

Learning Control for Autonomous Driving on Slippery Snowy Road Conditions

Comfort, road holding and safety of passenger cars are mainly influenced by an appropriate design of suspension systems. Improvements of the dynamic behaviour can be achieved by implementing semi-active or active suspension systems. In these cases, the correct design of a well-performing suspension control strategy is fundamental for obtaining satisfying results. In-Operation Modal Analysis allows the experimental structural identification in real operating conditions: moving from output-only data, leading to modal models linearised around the more interesting working points and, in the case of controlled systems, providing the needed information for the optimal design and verification of the controller performance. All these characters are needed for the experimental assessment of vehicle suspension systems. In the paper, two suspension architectures are considered equipping the same car type. The former is a semi-active commercial system, the latter a novel prototypic active system. For the assessment of suspension performance, two different kind of tests have been considered, proving ground tests on different road profiles and laboratory four poster rig tests. By OMA-processing the signals acquired in the different testing conditions and by comparing the results, it is shown how this tool can be effectively utilised to verify the operation and the performance of those systems, by only carrying out a simple, cost-effective road test.

Herman Van der Auweraer

Papers

Detection of nonlinear dynamic behaviour of mechanical structures IMAC-86)

Mechatronic powertrain virtual sensor: system-level model and sensor selection

Strain-Based Experimental Modal Analysis: Use of Mode Curvature and Strain-to-Displacement Relations

Learning Control for Autonomous Driving on Slippery Snowy Road Conditions

Active Suspension Systems for Passenger Cars: Operational Modal Analysis as a Tool for the Performance Assessment