The Optimized Design and Use of Automated Control Systems - State of the Literature and Proposed Research

摘要

Today we are faced with what some call the "automation paradox" and others call "the ironies of automation". Lisanne Bainbridge (1983)1 cautioned that the more automated a system becomes, the more important it is to appropriately integrate human contributions into the system. There is no question that automated control systems provide immeasurable benefit (improved efficiency, reliability, accuracy, safety, etc.); however, this comes at a cost; loss of skill, knowledge, decision-making capability and reaction-time in our human operators. Without daily engagement in the cognitive performance-based activities required by a control system, humans become less useful to the system. There seem to be two prevailing schools of thought on the best approach to human-system interface design. One advocates automating the system as much as possible to keep the human operator out of harm's way and to remove the error-prone human from critical operations. The other camp claims that the human operator suffers significant losses in physical capability, memory and attention capacity and their learned responses diminish in quality if they have not been actively and cognitively engaged in the operation. Then when called upon to take over control in an automated system, they are less capable of effectively operating the system manually. It is not well enough established which is correct or if there is a generalizable correct path. Currently, as automated control systems are designed, it is often the case that operators are left in the operational fringes. So, researchers at the University of Pittsburgh have been exploring design methodologies that will allow system designers to understand and to then implement the best of the control system and human performance attributes, with the intent of concurrently minimizing the likelihood of human error-induced incidents. Through a series of trials using licensed reactor operators in a reconfigurable control room simulator, researchers are planning to identify and measure key performance variables while varying task configuration, level of automation, and override authority. If impact on system output is shown to be predictable, the generated model can help designers simulate various design strategies and their resulting impact on system performance, thus providing more-informed training protocols and content, more-informed simulator practice decisions and improved operational and operating procedure consistency.