A unique approach to teaching particle and rigid body dynamics using solved illustrative examples and exercises to encourage self-learning The study of particle and rigid body dynamics is a fundamental part of curricula for students pursuing graduate degrees in areas involving dynamics and control of systems. These include physics, robotics, nonlinear dynamics, aerospace, celestial mechanics and automotive engineering, among others. While the field of particle and rigid body dynamics has not evolved significantly over the past seven decades, neither have approaches to teaching this complex subject. This book fills the void in the academic literature by providing a uniquely stimulating, “flipped classroom” approach to teaching particle and rigid body dynamics which was developed, tested and refined by the author and his colleagues over the course of many years of instruction at both the graduate and undergraduate levels. Complete with numerous solved illustrative examples and exercises to encourage self-learning in a flipped-classroom environment, Dynamics of Particles and Rigid Bodies: A Self-Learning Approach: Provides detailed, easy-to-understand explanations of concepts and mathematical derivations Includes numerous flipped-classroom exercises carefully designed to help students comprehend the material covered without actually solving the problem for them Features an extensive chapter on electromechanical modelling of systems involving particle and rigid body motion Provides examples from the state-of-the-art research on sensing, actuation, and energy harvesting mechanisms Offers access to a companion website featuring additional exercises, worked problems, diagrams and a solutions manual Ideal as a textbook for classes in dynamics and controls courses, Dynamics of Particles and Rigid Bodies: A Self-Learning Approach is a godsend for students pursuing advanced engineering degrees who need to master this complex subject. It will also serve as a handy reference for professional engineers across an array of industrial domains. 

Dynamics of Particles and Rigid Bodies. A Self-Learning Approach