Two-Dimensional Adaptive Model of a Human Controller Using Pattern Recognition Techniques

A two dimensional adaptive model of a human operator in a visual-manual compensatory tracking task is proposed. The model is adaptive to changes in gain and/or form of the plant dynamics on each axis, accommodating plant transfer functions of the form (k/s) or (k/s2). Pattern recognition is used to sense changes in the control system requiring a change in the model operating mode. The form of the model is derived from consideration of the physiological processes evident in a human controller. It contains separate sections dealing with perception of the control system variables, action taken by the central nervous system (CNS) as a result of the sensory inputs, and the conversion of CNS commands into motions of the controller. The model reflects the influence of the dominant type 2 control strategy on the type 1 strategy when tracking is performed with heterogeneous plant axis dynamics. Tests of the gain adaptive algorithm simulating time invariant human operator performance are described, and the test results presented for types 1 and 2 control situations. Results of pattern recognition tests conducted with model-generated data are also presented.