Out-of-the-Loop Performance Problems

Another example is the case of adaptive cruise control, a car technology that in normal circumstances controls the speed of the vehicle depending on prevailing traffic situations. Studies have shown that drivers are much slower to respond to situations that require intervention compared to drivers who remained in manual control. Not just that, but the response of those in manual control was more efficacious in terms of avoiding a crash (Stanton and Young, 1998). 

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Out-of-the-loop performance problem—The out-of-the-loop performance problem typically occurs within automated systems when human operators are removed from the control loop and placed in the job of system monitor (Endsley, 1995). Because human operators are generally ill suited to the role of monitoring complex systems, out-of-the-loop performance has been found to lead to problems such as vigilance deaements, complacency, loss of operator situation awareness, poor feedback under automated conditions, and manual skill decay (Endsley and Kaber, 1999). The various consequences associated with operators performing out of the loop have been found to cause inadequate system monitoring and problems in humans assuming control when necessary. 

Endsley, M.R. and Kiris, E.O. (1995). The out-of-the-loop performance problem and level of control in automation. Human Eactors, 37(2), 381-394. 

Kaber, D.B. and Endsley, M.R. (1997). Out-of-the-loop performance problems and the use of intermediate levels of automation for improved control system functioning and safety. 

One final comment should be made about model-based control before we leave the subject. These model-based controllers depend quite strongly on the validity of the model. If we have a poor model or if the plant parameters change, the performance of a model-based controller is usually seriously affected. Model-based controllers are less robust than the more conventional PI controllers. This lack of robustness can be a problem in the single-input-single-output (SISO) loops that we have been examining. It is an even more serious problem in multi-variable systems, as we will find out in Chaps. 16 and 17. 

The control computer/DCS system consists of controllers, A/D and D/A converters, and the signal conditioifing hardware and software, i.e., filtering and validation. Each of these components requires separate evaluation. Table 15.5 lists possible problems with the controller/DCS system. One way to initially check controller tuning is to place the control loop in manual (open the control loop) and observe whether the controlled variable lines out to a steady-state or near steady-state value. Comparing the open-loop and closed-loop performance indicates whether the controller is upsetting the process. If not, disturbances to the control loop in question are the primary source of the upsets. 

In this revised process, human performance considerations are accounted for emly in the designs and played out for evaluation in the simulation mode. Iteration in this mode is flexible and timely. The flow then proceeds with a refined design into the standard design and prototype development phases. MIDAS provides a prototyping test bench, based on human performance models. Designers can work with computational representations of the crew station and human operators rather than relying solely on hardware simulators and man-in-the-loop studies, to discover problems tmd ask what-if questions regarding the projected mission, equipment, and environment. 

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