.ManualTracking.UsersGuide.Introduction

Information

There are many situations where a human operator is required to make the output of a system follow a desired trajectory. Examples of manual tracking tasks include aiming a tank turret, driving an automobile, and piloting an aircraft.

Note that to simulate one of these systems, a model of the human's control behavior must be specified. Such models can be found in the field of manual control, which uses the tools and techniques of control theory to study the control behavior of humans.

Previous studies have made extensive use of single-axis manual tracking tasks to investigate the control behavior of humans performing continuous control. In a typical experimental tracking task, a human operator views a display on a computer screen and uses an input device, such as a joystick or force stick, to generate control input. An example display is shown above. There are two objects on the screen: one is a target that represents the reference (desired) state, and the other is a cursor that represents the actual state of the controlled system. The human's goal is to make the cursor follow the target as closely as possible.

Many situations require humans to perform multi-axis, multi-loop control tasks, so it might seem that studying one-dimensional control would be an unreasonable oversimplification. However, it has been found that multi-axis tracking performance is highly related to one-axis tracking, and that information about the human controller derived from single-axis tracking tasks can be applied to multi-loop tasks.

This library contains dynamic models of human control behavior from the manual control literature. In addition, Python-based tools allow users to perform manual tracking tasks designed in Modelica, and to tune parameter values in the manual controller models to either maximize tracking performance, or to match recorded control input from user experiments.