CAREER: Artificial Learning Control Systems for Performance Critical Applications

点击翻译按钮获取中文摘要

The goal of human-like (or better) intelligent control, and systems that combine the best elements of human operators and machines to achieve a more capable whole, is held by many researchers. Controllers with intermediate levels of intelligence and adaptability (between current systems and human-like control) are significant for at least two reasons: (1) They are a stepping-stone to the more capable systems envisioned; and (2) They have important practical uses in their own right, as part of semi-intelligent systems or utilized as an element of more capable human/machine systems.This project will depart from previous important and useful artificial neural network (NN) adaptive control methods, based on a dynamic inverse or feedback linearization controller and a neural network trained online. This class of adaptive control has recently been extended by the principal investigator to include plants that are not feedback linearizable (even approximately). These methods have distinct advantages for performance critical applications (i.e., those where safety, economic pressures, or other factors imply that the probability of system failure must be minimized), because of rapid response to new uncertainties and important theoretical stability results. A new approach will be taken, which retains the advantages described above, but utilizes untapped potential of NN adaptive controllers to enable the system to learn the plant over all state and control space for which information is available, including information given a priori, recorded data, and current state. It will explore this idea of simultaneous online training and training on recorded past data within other adaptive control frameworks. The development of these approaches is hoped to lead to many important new applications of performance critical controllers, applications that generally required a human "touch" to achieve acceptable performance (and also require stability and robustness guarantees), examples include: aspects of factory automation (those where uncertainty is not negligible), ground vehicle control, construction equipment, integrated avionics, spacecraft docking, and flight control. This predicted impact is based on providing an important element of what humans can do as controllers: become highly proficient at controlling a plant over a large segment of state and control space, yet also able to respond to a sudden change in plant dynamics.