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Modelling for predictive control
Published in J.A. Rossiter, Model-Based Predictive Control, 2004
Identification is the process whereby one discerns a system model from available information [76]. Model identification could be considered as an end in itself; however, often the model is wanted as a base for control law design and hence the potential utility of the model for this purpose must be considered during the identification. The basic premise [126] taken in this chapter is that there should be a synergy between the model identification and the role of the model in control law design. Such a view point differs from typical practise within MPC whereby one identifies a model via some algorithm and then uses it without considering possible interactions in these two steps. In the case of predictive control, the potential for synergy is large and should be exploited.
ISO-9000 Requirements
Published in Michael B. Weinstein, Total Quality Safety Management and Auting, 2018
Identification is the ability to distinguish one product, part, procedure, or process from another. Identification of parts allows workers to ensure that the proper parts are used in an assembly. Identification of procedures ensures that the proper procedures have been used for an activity or that the proper steps have been taken in an analysis. Identification of test data and instrument calibrations ensures that important test data is being analyzed properly. In all cases, the type of identification employed should suit the specific requirements, importance and risk levels.
Zonotope parameter identification for piecewise affine system
Published in Systems Science & Control Engineering, 2020
Hong Jianwang, Ricardo A. Ramirez-Mendoza
The piecewise affine system considered in this paper is one of the hybrid dynamical systems, as the piecewise affine system represents the switching dynamics among a collection of linear differential or difference equations with state space being partitioned by a finite number of linear hyperplanes. Hybrid dynamical systems are a class of complex systems, which involve interacting discrete events and continuous variable dynamics. They are important in applications in embedded systems, cyber-physical systems, robotics, manufacturing systems, biomolecular networks, and have recently been at the centre of intense research activity in the control theory and artificial intelligence communities. But when to control a system, one needs to know at least something about how the system behaves and reacts to different actions taken on it. Hence, we need a model of the system. A system can be informally defined as an entity which interacts with the rest of the world through more or less well-defined input and output data. A model is then an approximate description of the system, and an ideal model may be simple, accurate and general. This approximate description of the system can be constructed by a system identification strategy, as the goal of system identification is to build a mathematical model for a dynamic system based on some initial information about the system and the measurement data collected from the system. According to (Lennart, 1999), the process of system identification consists in designing and conducting the identification experiment in order to collect the measurement data, selecting the structure of the model and specifying the parameters to be identified and eventually fitting the model parameters to the obtained data. Finally, the quality of the obtained model is evaluated through the model validation process. Generally, system identification is an iterative process and if the quality of the obtained model is not satisfactory, some or all of the listed phases can be repeated in order to obtain one satisfied model for that considered system.