Hybrid annex

Hybrid Control Systems are increasingly investigated as models for control systems where the interaction between continuous and discrete processes is tightly integrated. As yet, there is no general agreement as to what constitutes a canonical hybrid system. Rather various subclasses of systems combining continuous evolution and discrete event characteristics are analyzed for each application. In this dissertation we first unify the presentation of hybrid systems with a rich model which encompasses most examples of interest. We classify the available results and show how they fit together within the different subcategories. In the main part of the dissertation we prove that for piecewise linear control systems---introduced over twenty years ago---the isomorphism problem is decidable in polynomial time in the number of inequalities defining the sets and the dimension of the sets. We also prove a stability result for switching systems arising from the problem of vehicle formations.

Uncertainty is an inherent characteristic of actual control systems since their parameters are not fixed and may change based on external factors. The behaviour and performance of control systems can be strongly impacted by these unknown characteristics. Variability in the environment, ageing of core components, and manufacturing tolerances are some of the factors that contribute to this uncertainty and pose an unknown risk to control systems. As a result, real control systems' intrinsic parameter indeterminacy may have an impact on how well they operate. Real-life scenarios are often ambiguous and complex, posing challenges for decision-makers to articulate their opinions. Neutrosophic sets are employed in such contexts to handle indeterminacy effectively. Neutrosophic theory suggests a variable interval based on truth, indeterminacy, and falsity, allowing for the expression of both determinate and indeterminate information. Nevertheless, neutrosophic sets have not yet been used in the modelling, analysis, and design of uncertain control systems with determinate parameters. To close this gap, a new neutrosophic design technique is established in this study by introducing single-valued neutrosophic systems. The suggested control design method is illustrated with a numerical example employing neutrosophic parameters.

The increasing complexity associated with many modern engineering applications, including autonomous robot guidance and navigation, process control in sensor-rich environments, and control of biological systems, has far-reaching implications for control system design. As an example, reactive, embedded software systems, interacting among themselves and remote users over communication networks, introduce a whole new set of system-level challenges, and classic control design objectives such as stability, performance, and robustness are being complemented with a number of new questions. These include the cost of hardware implementation, measured for example not only by computational requirements such as speed and memory, but also by communication requirements such as available communication bandwidth. Moreover, the complexity associated with specifying the control procedures and with verifying the behavior of the closed-loop system increasingly plays a fundamental role, especially in safety-critical control systems arising in energy and transportation networks, and in medical applications.

This chapter describes the role of symbolic computation in several phases of the control system design cycle, namely modelling, analysis and synthesis. It provides some mathematical background of nonlinear system theory and presents a guided tour through those phases using typical examples, both contrived and real life. To name them, the symbolic computation of (i) the zero dynamics, used for different purposes, (ii) the input-output exact linearising state feedback, and (iii) the state-space exact linearising state feedback, all for nonlinear dynamic systems, are discussed. The examples come from a diverse range of applications and provide a picturesque landscape of this area of research. The possibilities and limitations of the so-called NON^CON package, based on the symbolic computation program Maple, are outlined with these examples. This package was developed to aid with analytical computations in the area of nonlinear control systems.

Modelling and Analysis of Aeroelastic Tailoring Blade Wind Turbine Systems

The application of fuzzy models in thermal power control systems is considered. The structure of a fuzzy system for a thermal object (boiler) is described. Results of the design and a schematic realization of boiler aggregate control systems are reported. Use of the suggested fuzzy models for control of the fuel-air relationship and boiler water level are described as examples.

Modeling and analysis of networked control systems using stochastic hybrid systems

Context. The question of increasing the productivity of technological processes of extraction, processing and preparation of raw materials, improving product quality, reducing energy consumption, as well as creating safe working conditions during technological processes and preventing accidents is always quite relevant and requires the implementation of modern control and management systems. For the effective operation of such systems, it is important to pre-process and filter the data received from the sensors for monitoring the grinding processes and the explosive status of objects. One of the possible ways to increase the informativeness of data is the use of singular spectral analysis.Objective. Increasing the efficiency of technological process control systems and the reliability of explosive control systems of coal mines and oil and fuel complex facilities by processing and pre-filtering data received from sensors for monitoring grinding processes and the state of facilities.Method. To analyze the output signals of sensors used in control and management systems, the method of singular spectral analysis is used, which allows revealing hidden structures and regularities in time series by pre-filtering and data processing of acoustic, thermocatalytic, and semiconductor sensors.Results. A new approach to the management of technological processes of grinding raw materials in jet mills and control of the explosiveness of coal mines and objects of the oil and fuel complex is proposed, based on methods that allow to speed up the processing speed of sensor output data and improve the quality of information. It is shown that one of the promising methods that can be used for the pre-processing of time series of output data of sensors in control and control systems is the method of singular spectral analysis, the use of which allows filtering data, revealing hidden structures and regularities, and forecasting changes based on the analysis of previous information , identify anomalies and unusual situations, make more informed decisions and improve the processes of managing technological processes.Conclusions. The conducted experiments have confirmed the proposed software operability and allow recommending it for use in advancing both theoretical and practical aspects of process control systems through an enhanced singular spectral analysis (SSA) method for time series processing. This improved approach has been successfully demonstrated in real-world applications, including grinding processes in jet mills and explosion monitoring in coal mines and oil and fuel facilities. The implementation demonstrates a significant increase in data processing speed and information quality, which makes it particularly valuable for use in safety-critical industrial facilities.

Integrative modeling and simulation analysis of 11kW squirrel-cage asynchronous motor and vector control system have been done in the paper. Integrative model deals with the coupling effect between the motor and its control system, and analyzes system dynamic response. At the same time, the motor transient electromagnetic field performance has been acquired in the integrative simulation with slip frequency vector control strategy. Furthermore, the simulation results of integrative model are compared with that of non-integrative model, including control system circuit model and motor physical model without precise control strategy. The integrative model considers not only the motor external control strategy but also motor internal electromagnetic field performance and response with the control strategy. In addition, integrative modeling and simulation realizes coupling simulation analysis of squirrel-cage asynchronous motor and control system in system perspective and component perspective as well, which could effectively help us do the precise analysis of system performance.

Stability and steady state analysis of control and safety systems of Nuclear Power Plants

A new method for modeling and analysis of networked control systems with time delay using hidden Markov models is presented. The optimal estimator of system state for networked control systems without full state information is given when the networked induced time delay is governed by an underlying Markov chain. The design of stochastic optimal controllers of networked control systems with output feedback is also presented. The result of simulation verified the efficiency and correction of the proposed methods.

Computer aided modeling, analysis and design of control systems - A perspective (Reprint; ibid, vol. 3, n. 2 83) [25 Years Ago

Modeling and Comparative Analysis of Aircraft Environmental Control Systems

From the Publisher: Singular perturbations and time-scale techniques were introduced to control engineering in the late 1960s and have since become common tools for the modeling, analysis, and design of control systems. In this SIAM Classics edition of the 1986 book, the original text is reprinted in its entirety (along with a new preface), providing once again the theoretical foundation for representative control applications. This book continues to be essential in many ways. It lays down the foundation of singular perturbation theory for linear and nonlinear systems, it presents the methodology in a pedagogical way that is not available anywhere else, and it illustrates the theory with many solved examples, including various physical examples and applications. So while new developments may go beyond the topics covered in this book, they are still based on the methodology described here, which continues to be their common starting point. Audience Control engineers and graduate students who seek an introduction to singular perturbation methods in control will find this text useful. The book also provides research workers with sketches of problems in the areas of robust, adaptive, stochastic, and nonlinear control. No previous knowledge of singular perturbation techniques is assumed. About the Authors Petar Kokotovic is Director of the Center for Control Engineering and Computation at the University of California, Santa Barbara. Hassan K. Khalil is Professor of Electrical and Computer Engineering at Michigan State University. John O'Reilly is Professor of Electronics and Electrical Engineering at the University of Glasgow, Scotland.

FUTURE TRENDS IN PROCESS AUTOMATION