NEURAL NETWORKS FOR AUTONOMOUS MOTOR CONTROL

WE PROPOSE TO DEVELOP A ROBOTIC CONTROL SYSTEM WITH NEURAL NETWORK TECHNOLOGY WHICH CAN ADAPT TO VARIABLE PAYLOAD TRANSPORT CONDITIONS. TRADITIONAL TECHNOLOGIES HAVE DIFFICULTY PROVIDING ADAPTIVE AND AUTONOMOUS MOTOR CONTROL OR THE ABILITY TO GENERALIZE TO UNFORESEEN CONDITIONS. ADAPTIVE CONTROL IS NECESSARY BECAUSE THERE IS VARIATION OF COMPONENT MASSES AND INERTIA, DIRECTION OF GRAVITY AND LITTLE ABILITY TO PREPLAN FOR VARIATIONS IN PAYLOADS. IN PHASE I, WE WILL ASSESS THE FEASIBILITY OF SOLVING THESE UNCONTROLLABLE PROBLEMS WITH NEURAL NETWORK TECHNOLOGY. IT APPEARS THAT OUR NEURAL NETWORK CONCEPTS AND PROGRAMS, WHICH HAVE BEEN DEVELOPED OR ARE CURRENTLY IN PROGRESS, ARE APPLICABLE TO ADAPTIVE CONTROL. WE WILL DESIGN AND TEACH THESE ADAPTIVE SYSTEMS TO INTEGRATE DIVERSE INFORMATION, GENERALIZE PRETRAINED RESPONSES, AND SMOOTHLY COORDINATE ANY MULTI-JOINT MOTORIC RESPONSE. IT IS EXPECTED THAT OUR EFFORTS WILL LEAD TO THE DESIGN AND PROTOTYPING OF IMPROVED ROBOTIC SYSTEMS. USING NEUROPHYSIOLOGICAL CONCEPTS TO INFLUENCE THE DESIGN OF NEURAL NETWORK CONTROL SYSTEM STRUCTURES, WILL RESULT IN SMOOTHER, OPTIMALLY OPERATING ROBOT.