Memory pattern identification for feedback tracking control in human-machine systems

Objective: The aim of this paper was to identify the characteristics of memory patterns with respect to a visual input, perceived by the human operator during a manual control task, which consisted in following a moving target on a display with a cursor.Background: Manual control tasks involve nondeclarative memory. The memory encodings of different motor skills have been referred to as procedural memories. The procedural memories have a pattern, which this paper sought to identify for the particular case of a onedimensional tracking task. Specifically, data recorded from human subjects controlling dynamical systems with different fractional order were investigated.Method: A Finite Impulse Response (FIR) controller was fitted to the data, and pattern analysis was performed to the fitted parameters. Then, the FIR model was further reduced to a lower order controller; from the simplified model, the stability analysis of the human-machine system in closedloop was conducted.Results: It is shown that the FIR model can be employed to identify and represent patterns in human procedural memories during manual control tasks. The obtained procedural memory pattern presents a time scale of about 650 ms before decay. Furthermore, the fitted controller is stable for systems with fractional order less or equal to 1.Conclusion: For systems of different fractional order, the proposed control scheme – based on a FIR model – can effectively characterize the linear properties of manual control in humans.Application: This research supports a biofidelic approach to human manual control modeling over feedback visual perceptions. Relevant applications of this research are: the development of shared-control systems, where a virtual human model assists the human during a control task, and human operator state monitoring.</div

Inverse Dynamic Game Methods for Identification of Cooperative System Behavior

Die dynamische Spieltheorie hat sich als ein effektiver Ansatz zur Modellierung und Analyse der Interaktion zwischen mehreren Akteuren oder Spielern in dynamischen Prozessen erwiesen. Um diese Theorie in realen Anwendungen umzusetzen, ist jedoch die Möglichkeit einer schnellen Identifikation der Ziele jedes Spielers entscheidend. Dieses Identifikationsproblem wird als inverses dynamisches Spiel bezeichnet. Hierfür präsentiert diese Dissertation Lösungen, die auf Beobachtungen der Spieleraktionen und der resultierenden Zustandstrajektorie basieren, welche die Entwicklung des Spiels über die Zeit beschreibt. Es werden zwei Arten von Methoden zur Lösung von inversen dynamischen Spielen entwickelt. Die erste besteht in der Anwendung von regelungstechnischen Methoden. Für die weitverbreitete Klasse der linear-quadratischen dynamischen Spiele werden zusätzlich explizite Mengen formuliert, die alle möglichen Lösungen des inversen Problems beschreiben. Der zweiten Methode liegen Verfahren des Inverse Reinforcement Learnings aus der Informatik zugrunde. Für beide Arten von Methoden werden mathematische Bedingungen formuliert, unter denen eine erfolgreiche Schätzung der Ziele aller Spieler garantiert ist. Ein simulativer Vergleich mit einem Verfahren aus dem Stand der Technik zeigt die höhere Effizienz der vorgestellten neuen Ansätze. Darüber hinaus werden die Methoden für die Identifikation von kooperativem menschlichen Verhalten in einem Lenkmanöver angewendet. Die entwickelten Ansätze für inverse dynamische Spiele ermöglichen die effiziente Identifikation von Spielerzielen und können in zahlreichen Anwendungsfeldern wie beispielsweise der Mensch-Maschine-Interaktion und der Verhaltensbeschreibung biologischer Systeme eingesetzt werden

Haptic Steering Interfaces for Semi-Autonomous Vehicles

Autonomous vehicles are predicted to significantly improve transportation quality by reducing traffic congestion, fuel expenditure and road accidents. However, until autonomous vehicles are reliable in all scenarios, human drivers will be asked to supervise automation behavior and intervene in automated driving when deemed necessary. Retaining the human driver in a strictly supervisory role, however, may make the driver complacent and reduce driver's situation awareness and driving skills which ironically, can further compromise the driver’s ability to intervene in safety-critical scenarios. Such issues can be alleviated by designing a human-automation interface that keeps the driver in-the-loop through constant interaction with automation and continuous feedback of automation's actions. This dissertation evaluates the utility of haptic feedback at the steering interface for enhancing driver awareness and enabling continuous human-automation interaction and performance improvement in semi-autonomous vehicles. In the first part of this dissertation, I investigate a driving scheme called Haptic Shared Control (HSC) in which the human driver and automation system share the steering control by simultaneously acting at the steering interface with finite mechanical impedances. I hypothesize that HSC can mitigate the human factors issues associated with semi-autonomous driving by allowing the human driver to continuously interact with automation and receive feedback about automation action. To test this hypothesis, I present two driving simulator experiments that are focused on the evaluation of HSC with respect to existing driving schemes during induced human and automation faults. In the first experiment, I compare obstacle avoidance performance of HSC with two existing control sharing schemes that support instantaneous transfers of control authority between human and automation. The results indicate that HSC outperforms both schemes in terms of obstacle avoidance, maneuvering efficiency, and driver engagement. In the second experiment, I consider emergency scenarios where I compare two HSC designs that provide high and low control authority to automation and an existing paradigm that decouples the driver input from the tires during collision avoidance. Results show that decoupling the driver invokes out-of-the-loop issues and misleads drivers to believe that they are in control. I also discover a `fault protection tradeoff': as the control authority provided to one agent increases, the protection against that agent's faults provided by the other agent reduces. In the second part of this dissertation, I focus on the problem of estimating haptic feedback from the road, or the road feedback. Road feedback is critical to making the driver aware of the state of the vehicle and road conditions, and its estimates are used in a variety of driver assist systems. However, conventional estimators only estimate road feedback on flat roads. To overcome this issue, I develop three estimators that enable road feedback estimation on uneven roads. I test and compare the performance of the three estimators by performing driving experiments on uneven roads such as road slopes and cleats. In the final part of this dissertation, I shift focus from physical human-automation interaction to human-human interaction. I present the evidence from the literature demonstrating that haptic feedback improves the performance of two humans physically collaborating on a shared task. I develop a control-theoretic model for haptic communication that can describe the mechanism by which haptic interaction facilitates performance improvement. The model creates a promising means to transfer the obtained insights to design robots or automation systems that can collaborate more efficiently with humans.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169975/1/akshaybh_1.pd