AN ADVANCED HYBRID NEURAL NETWORK FOR ROBOT DYNAMICS MODELING

DYNAMICS MODELING IS IMPORTANT FOR THE DESIGN, ANALYSIS, SIMULATION, AND CONTROL OF ROBOTIC AND OTHER COMPUTER CONTROLLED MECHANICAL SYSTEMS. THE FORMULATION OF A MODEL AND THE REQUIREMENTS IMPOSED ON THE COMPUTATIONAL ALGORITHMSDIFFER FROM ONE APPLICATION TO THE OTHER. TODAY, ACCURATE MODELING CAN ONLY BE ACHIEVED BY HAVING A THOROUGH UNDERSTANDING OF THE PHYSICS OF THE SYSTEM IN ADDITION TO PERFORMING A COMPLETE DYNAMICS CHARACTERIZATION OF THE VARIABLES USED IN THE MODEL. REAL TIME EXECUTION OF THE MODEL IS THEN ACHIEVED BY SIMPLIFYING THE EQUATIONS OR BY EMPLOYING COSTLY, SPECIAL PURPOSE, HYBRID- OR SUPER-COMPUTERS. NEURAL NETWORKS LEND THEMSELVES WELL TO THIS APPLICATION DUE TO THEIR ABILITY TO ACCURATELY REPRESENT COMPLEX FUNCTIONS BY MEANS OF A REPETITIVE TRAINING PROCESS, WITHOUT REQUIRING EXTENSIVE KNOWLEDGE OF THE SYSTEM, IN ADDITION TO THEIR ABILITY TO OPERATE IN REAL TIME DUE TO THEIR SIMPLICITY IN COMPUTATION AND STRONG DEPENDENCE ON TABULATION. THE OBJECTIVE OF THE PROPOSED RESEARCH IS TO INVESTIGATE THEAPPLICATION OF ADVANCED NEURAL NETWORK METHODS TO DYNAMICS MODELING OF COMPLEX ROBOTIC SYSTEMS FOR THE PURPOSE OF ACHIEVING REAL TIME EXECUTION WHILE MAINTAINING THE ACCURACYOF THE MODEL. BOTH THE CMAC AND MULTI-LAYERED TYPE NETWORKSWILL BE EMPLOYED. AN ADVANCED HYBRID IMPLEMENTATION OF THE NEURAL NETWORKS IS PROPOSED TO ALLOW REVERSED USE OF THE MODEL. THE SUCCESSFUL COMPLETION OF PHASE I AND II WILL RESULT IN A HYBRID METHODOLOGY FOR MODELING DYNAMIC SYSTEMS FOR THE PURPOSE OF ACHIEVING REAL-TIME EXECUTION AS WELL AS MINIMIZING MODEL SET-UP TIME AND SYSTEM CHARACTERIZATION.