A SUBSYSTEM IDENTIFICATION APPROACH TO MODELING HUMAN CONTROL BEHAVIOR AND STUDYING HUMAN LEARNING

Humans learn to interact with many complex dynamic systems such as helicopters, bicycles, and automobiles. This dissertation develops a subsystem identification method to model the control strategies that human subjects use in experiments where they interact with dynamic systems. This work provides new results on the control strategies that humans learn. We present a novel subsystem identification algorithm, which can identify unknown linear time-invariant feedback and feedforward subsystems interconnected with a known linear time-invariant subsystem. These subsystem identification algorithms are analyzed in the cases of noiseless and noisy data. We present results from human-in-the-loop experiments, where human subjects in- teract with a dynamic system multiple times over several days. Each subject’s control behavior is assumed to have feedforward (or anticipatory) and feedback (or reactive) components, and is modeled using experimental data and the new subsystem identifi- cation algorithms. The best-fit models of the subjects’ behavior suggest that humans learn to control dynamic systems by approximating the inverse of the dynamic system in feedforward. This observation supports the internal model hypothesis in neuro- science. We also examine the impact of system zeros on a human’s ability to control a dynamic system, and on the control strategies that humans employ

THE EFFECTS OF SYSTEM CHARACTERISTICS, REFERENCE COMMAND, AND COMMAND-FOLLOWING OBJECTIVES ON HUMAN-IN-THE-LOOP CONTROL BEHAVIOR

Humans learn to interact with many complex physical systems. For example, humans learn to fly aircraft, operate drones, and drive automobiles. We present results from human-in-the-loop (HITL) experiments, where human subjects interact with dynamic systems while performing command-following tasks multiple times over a one-week period. We use a new subsystem identification (SSID) algorithm to estimate the control strategies (feedforward, feedforward delay, feedback, and feedback delay) that human subjects use during their trials. We use experimental and SSID results to examine the effects of system characteristics (e.g., system zeros, relative degree, system order, phase lag, time delay), reference command, and command-following objectives on humans command-following performance and on the control strategies that the humans learn. Results suggest that nonminimum-phase zeros, relative degree, phase lag, and time delay tend to make dynamic systems difficult for human to control. Subjects can generalize their control strategies from one task to another and use prediction of the reference command to improve their command-following performance. However, this dissertation also provides evidence that humans can learn to improve performance without prediction. This dissertation also presents a new SSID algorithm to model the control strategies that human subjects use in HITL experiments where they interact with dynamic systems. This SSID algorithm uses a two-candidate-pool multi-convex-optimization approach to identify feedback-and-feedforward subsystems with time delay that are interconnected in closed loop with a known subsystem. This SSID method is used to analyze the human control behavior in the HITL experiments discussed above

Interaction Dynamics in Oscillator and Human-in-the-loop Systems.

This dissertation addresses control system analysis and system identification in three areas: error propagation in synchronization of harmonic oscillators, modeling of human active movement, and identification of human control strategies in manual pursuit tracking. 1) While most studies of synchronization in oscillator systems have focused on the existence of synchronous solutions in steady state, many problems pertaining to the transient dynamics have not been fully resolved. We extend the well-established theory of fundamental limitations to study the transient error propagation (string stability) in a string of synchronized harmonic oscillators. We first translate design requirements in terms of time-domain response and hardware limitations into a set of constraints on closed-loop frequency response. We further capture the conflict between string stability on the one hand and time-domain design requirements and hardware limitations on the other through a new Bode integral. 2) Modeling human active movement is a challenging problem not only because muscle has very sophisticated and highly nonlinear dynamics but also because neural and other signals internal to the body are difficult to observe directly. We seek a simple yet general and competent model to describe active movement in object manipulation tasks. Inspired by the Norton equivalent circuit in electrical engineering, we build a model based on the motion and force/torque signals that may be observed at the points of contact between the human body and the environment. The model consists of a motion source to represent a human's motor plan and a spring-mass-damper coupler to capture the time-varying driving point impedance of the human hand. The model is validated using occasional experimental trials in which a participant experiences unexpected loads in a grasp and twist task. 3) Although a large amount of literature has provided methods to identify feedback control in manual tracking tasks, very little research has been undertaken to experimentally identify feedforward control. We capitalize on the theory of fundamental limitations to study the link between a human's ability to simultaneously reject disturbances and perform pursuit tracking. We further develop an identification method to separate human feedback and feedforward control strategies in sinusoidal tracking tasks.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/108853/1/ybo_1.pd

Modeling the Human Visuo-Motor System for Remote-Control Operation

University of Minnesota Ph.D. dissertation. 2018. Major: Computer Science. Advisors: Nikolaos Papanikolopoulos, Berenice Mettler. 1 computer file (PDF); 172 pages.Successful operation of a teleoperated miniature rotorcraft relies on capabilities including guidance, trajectory following, feedback control, and environmental perception. For many operating scenarios fragile automation systems are unable to provide adequate performance. In contrast, human-in-the-loop systems demonstrate an ability to adapt to changing and complex environments, stability in control response, high level goal selection and planning, and the ability to perceive and process large amounts of information. Modeling the perceptual processes of the human operator provides the foundation necessary for a systems based approach to the design of control and display systems used by remotely operated vehicles. In this work we consider flight tasks for remotely controlled miniature rotorcraft operating in indoor environments. Operation of agile robotic systems in three dimensional spaces requires a detailed understanding of the perceptual aspects of the problem as well as knowledge of the task and models of the operator response. When modeling the human-in-the-loop the dynamics of the vehicle, environment, and human perception-action are tightly coupled in space and time. The dynamic response of the overall system emerges from the interplay of perception and action. The main questions to be answered in this work are: i) what approach does the human operator implement when generating a control and guidance response? ii) how is information about the vehicle and environment extracted by the human? iii) can the gaze patterns of the pilot be decoded to provide information for estimation and control? In relation to existing research this work differs by focusing on fast acting dynamic systems in multiple dimensions and investigating how the gaze can be exploited to provide action-relevant information. To study human-in-the-loop systems the development and integration of the experimental infrastructure is described. Utilizing the infrastructure, a theoretical framework for computational modeling of the human pilot’s perception-action is proposed and verified experimentally. The benefits of the human visuo-motor model are demonstrated through application examples where the perceptual and control functions of a teleoperation system are augmented to reduce workload and provide a more natural human-machine interface

Measuring pilot control behavior in control tasks with haptic feedback

The research goal of this thesis was to increase the understanding of effects of haptic feedback on human’s performance and control behavior. Firstly, we investigated the effectiveness of haptic aids on improving human’s performance in different control scenarios. Beneficial effects of haptic aids were shown in terms of human's performances and control effort. Comparisons with input-mixing systems showed that, although input-mixing systems yielded better performance than haptic aids in nominal conditions, participants recovered better from failures of haptic systems than from failures of input-mixing aids. Secondly, we investigated how humans adapt their dynamic responses to realize benefits of the haptic feedback. To achieve this goal, we developed novel identification methods to estimate human's neuromuscular dynamics in a multi-loop control task. The novel methods assumed a time-invariant behavior of humans responses. The novel methods were validated in simulation and applied to experimental data. Finally, novel methods were developed to account for time-varying behavior of human's responses. Different sets of numerical simulations were used to validate the novel methods. Then, the methods were applied to data obtained in human in-the-loop experiments