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How can we provide guarantees of behaviours for autonomous systems such as driverless cars? This tutorial text, for professionals, researchers and graduate students, explains how autonomous systems, from intelligent robots to driverless cars, can be programmed in ways that make them amenable to formal verification. The authors review specific definitions, applications and the unique future potential of autonomous systems, along with their impact on safer decisions and ethical behaviour. Topics discussed include the use of rational cognitive agent programming from the Beliefs-Desires-Intentions paradigm to control autonomous systems and the role model-checking in verifying the properties of this decision-making component. Several case studies concerning both the verification of autonomous systems and extensions to the framework beyond the model-checking of agent decision-makers are included, along with complete tutorials for the use of the freely-available verifiable cognitive agent toolkit Gwendolen, written in Java.
Presenting students with a comprehensive and efficient approach to the modelling, simulation, and analysis of dynamic systems, this textbook addresses mechanical, electrical, thermal and fluid systems, feedback control systems, and their combinations. It features a robust introduction to fundamental mathematical prerequisites, suitable for students from a range of backgrounds; clearly established three-key procedures – fundamental principles, basic elements, and ways of analysis – for students to build on in confidence as they explore new topics; over 300 end-of-chapter problems, with solutions available for instructors, to solidify a hands-on understanding; and clear and uncomplicated examples using MATLAB®/Simulink® and Mathematica®, to introduce students to computational approaches. With a capstone chapter focused on the application of these techniques to real-world engineering problems, this is an ideal resource for a single-semester course in dynamic systems for students in mechanical, aerospace and civil engineering.
In this introductory chapter, the general concepts and classification of dynamic systems in engineering are introduced; commonly used methods and computer software for modeling, simulation, and analysis of dynamic systems are previewed; and the scope of this book is outlined.
Mechanical systems are seen in machines, devices, equipment, and structures in a wide variety of engineering applications. Modeling of a mechanical system involves forces and motion about relevant objects, which can be either solids or fluids. Depending on specific concerns in an application, there are several types of mathematical models for mechanical systems at different levels of complexity, including lumped-parameter models, rigid-body models, and deformable- or flexible-body models. For instance, in studying the dynamic behaviors of an airplane, a lumped-parameter model may be good enough to describe its flight trajectory and a rigid-body model may be sufficient to study its three-dimensional motion in space. However, a flexible-body model is necessary to understand the vibration and stress in the airplane structure in performance evaluation and failure analysis.
This chapter assembles six problems of combined dynamic systems from engineering applications, namely, vibration analysis of a moving car, speed control of a coupled engine–propeller system (electromechanical system), modeling and analysis of a bimetallic strip thermometer (thermomechanical system), modeling and analysis of a resistive-heating element (electro-thermo-mechanical system), feedback control of a water purification system component (liquid-level system), and the working principles of sensors, electroacoustic, and piezoelectric devices. Each problem is presented in one section.
This chapter provides the reader with a brief refresher course on the mathematical apparatus crucial for modeling of dynamic systems. Sections 2.1 and 2.2 present basic concepts and terminology of vector and matrix algebra. Definitions and basic operations on complex numbers are introduced in Section 2.3. Section 2.4 is devoted to one of the important methods for solving differential equations – the Laplace transform. Sections 2.5 and 2.6 discuss the types of differential equations widely encountered in modeling of common dynamic systems and develop methods for solving these equations. Section 2.7 introduces the mathematical foundation for deriving transfer functions and creating block diagrams of various linear time-invariant dynamic systems. Section 2.8 presents a brief overview of solving differential equations numerically, with MATLAB and Wolfram Mathematica.
Practically every modern engineered dynamic system has electrical components such as motors, sensors, controllers, or, at the very least, power sources. Therefore, an understanding of the physical processes occurring in the typical electrical circuits, and the ability to model behavior of an electrical subsystem are essential for anyone interested in dynamic systems.