HUMAN STRATEGIES IN THE CONTROL OF TIME CRITICAL UNSTABLE SYSTEMS

The purpose of this study is to investigate the human manual control strategy when balancing an inverted pendulum under time critical constraints. The strategy was assessed through the quantification and evaluation of human response while performing tasks that require fast reaction from the human operator. The results show that as the task becomes more difficult due to increased time delay or shortened pendulum length, the human operator adopts a more discrete-type strategy. Additionally, dissimilarities between control of a short pendulum and a delayed pendulum are identified and discussed. Finally, the discrete-control mechanism is interpreted by relating the observed human responses to human-performance models. These results can be applied to systems requiring human interaction, such as teleoperation, which could be designed to maximize human response

Human Manual Control as an Information Processing Channel

Human-machine interaction (HMI) can be modeled as information flows through bidirectional communication channels, where the human receives sensory information from the machine and sends command information back to the machine. The interaction between human and machine can thus be characterized by the dynamics of information exchange measured in bits per second (b/s). The information-transmission rate (ITR) from human to machine is expected to depend on the complexity of the machine dynamics as well as human capabilities and limitations. We propose to investigate this dependency quantitatively in order to provide a measure of human performance. The HMI task considered in our investigation is a one-dimensional manual control task of an unstable system. A set of experiments are conducted where the human subjects maneuver a joystick in order to stabilize an inverted pendulum simulation. Two scenarios are analyzed: when time delay affects the feedback control system, and when the degree of instability of the task is increased. Using information- and control-theoretic approaches we identify the lower bound for the ITR of the controller, which is dependent on the dynamics of the control task. From time series analysis, we suggest a method for ITR estimation from human experiments. We believe that the difference between these two quantities allows for assessment of human performance and for the prediction of its limitations. Additionally, the association between the recorded ITR and the bandwidth of the controller will be discussed in order to create a more complete picture of human manual control abilities and limitations