Intelligent Control of Robotic Systems

This text provides a strong coverage of both the theoretical and application aspects of neural networks, fuzzy logic, genetic algorithms and hybrid intelligent techniques in robotics. Specific emphasis in the research work is given on the development of new efficient learning rules for robotic connectionist training and synthesis of neural learning algorithms for low-level control in the domain of robotic compliance tasks. The book contains several different examples of applications based on neural and hybrid intelligent techniques. The book: provides the theoretical background an a survey of most up-to-date developments in this rapidly growing application area of intelligent control of robotic systems; focuses on research in connectionist and hybrid intelligent techniques, directly applicable to control or making use of modern control theory in robotics; takes a new approach to the synthesis of learning and classification of control laws for robotic compliance tasks, together with the appropriate application examples.This text should be useful to a wide audience of engineers, ranging from undergraduate and graduate students, new and advanced academic researchers, to the practitioners (mechanical and electrical engineers, computer and systems scientists).