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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.

This dissertation demonstrates a new method for inferring a representation of the motor command, generated by the central nervous system for interactive point-to-point movements. This new tool, the input inference neural network or IINN, allows estimation of the complexity of the motor command. The IINN was applied to experimental data gathered from 7 volunteer subjects who performed point-to-point tasks while interacting with a specially constructed haptic robot. The motor plan inference demonstrates that, for the point-to-point movement tasks executed during experiments, the motor command can be projected onto a low-dimensional manifold. This dimension is estimated to be 4 or 5 and far less than the degrees of freedom available in the arm. It is hypothesized that subjects simplify the problem of adapting to changing environments by projecting the motor control problem onto a motor manifold of low dimension. Reducing the dimension of the movement optimization problem through the development of a motor manifold can explain rapid adaptation to new motor tasks.

This dissertation demonstrates a new method for inferring a representation of the motor command, generated by the central nervous system for interactive point-to-point movements. This new tool, the input inference neural network or IINN, allows estimation of the complexity of the motor command. The IINN was applied to experimental data gathered from 7 volunteer subjects who performed point-to-point tasks while interacting with a specially constructed haptic robot. The motor plan inference demonstrates that, for the point-to-point movement tasks executed during experiments, the motor command can be projected onto a low-dimensional manifold. This dimension is estimated to be 4 or 5 and far less than the degrees of freedom available in the arm. It is hypothesized that subjects simplify the problem of adapting to changing environments by projecting the motor control problem onto a motor manifold of low dimension. Reducing the dimension of the movement optimization problem through the development of a motor manifold can explain rapid adaptation to new motor tasks.

Upper Limb work-related musculoskeletal disorders (WMSDs) present a significant health risk to industrial workers. To address this, rigid-body exoskeletons have been widely used in industrial settings to mitigate these risks while exosuits offer advantages such as reduced weight, lower inertia, and no need for precise joint alignment, However, they remain in the early stages of development, especially for reducing muscular effort in repetitive and forceful tasks like heavy lifting and overhead work. This study introduces a multiple degrees-of-freedom cable-driven upper limb bilateral exosuit for human power augmentation. Two control schemes were developed and compared: an IMU based controller, and a myoelectric controller to compensate for joint torque exerted by the wearer. The results of preliminary experiments showed a substantial reduction in muscular effort with the exosuit's assistance, with the myoelectric control scheme exhibiting reduced operational delay. In parallel, the neuromusculoskeletal modeling and simulator (NMMS) has been widely applied in various fields. Most of the research works implements the PD-based internal model of human’s central nervous system to simulate the generated muscle activation. However, the PD-based internal models in recent works are tuned by the empirical data which requires empirical data from human subject experiments. In this dissertation, an off-policy DRL algorithm, Deep Deterministic Policy Gradient was implemented to tune the PD-based internal model of human’s central nervous system. Compared to the conventional approaches, the DRL-based auto-tuner can learn the optimal policy through trial-and-error which doesn’t require human subject experiment and empirical data. The experiment this work showed promising results of this DRL-based auto-tuner for internal-model of human’s central nervous system.

L'émergence Troubles Musculo-Squelettiques (TMS) en industrie constitue un véritable fléau ayant de lourdes conséquences socio-économique en France. Afin de réduire la pénibilité au travail et les risques TMS, les industriels s'engagent dans une politique de réaménagement des postes de travail par la mise en œuvre de moyens robotisés d'assistance aux opérateurs. Dans cette politique de prévention, le groupe PSA Peugeot Citroën aspire à utiliser des cobots et des exosquelettes comme dispositifs d'assistance pour améliorer les conditions de travail des opérateurs. Mais pour mettre en œuvre ces types de robot en usine, il est nécessaire de quantifier leurs apports ergonomiques. C'est dans ce contexte que s'inscrit cette thèse, dont l'objectif est de proposer une méthode d'évaluation de robots collaboratifs visant à être mis en œuvre dans les usines PSA Peugeot Citroën. Dans le cadre de ces travaux, nous avons utilisé l'exosquelette mono-bras droit ABLE, conçu par le CEA-LIST. A partir d'une analyse biomécanique d'une tâche de manipulation humaine, nous avons pu évaluer l'apport de l'exosquelette en termes de réduction de charge physique de l'utilisateur. Aussi avons-nous proposé dans ces travaux d'analyser les mécanismes neuromusculaires résultants du mouvement effectué en interaction avec l'exosquelette. Sur la base de la théorie du contrôle moteur humain et en utilisant une méthode d'optimisation inverse, les fonctions objectifs telles que jerk, le couple articulaire, ou l'énergie, caractérisant la tâche de manipulation humaine en termes d'efforts, de cinématique et de temps d'exécution, ont été identifiées. Cette meilleure compréhension du mouvement du membre supérieur humain a permis ensuite de revenir sur la conception de l'exosquelette afin de proposer une stratégie de commande optimisée à l'exécution de tâches de travail en environnement industriel. / The emergence of Musculo-Squelettal Disorders (MSD) in the industry is a real blight, having major socioeconomic consequences in France. In order to reduce work painfulness and MSD risks, some industries are committing to modifying workstations by assisting operators with robotic devices. Following this MSD prevention policy, PSA Peugeot Citroen aims to use cobots or exoskeletons as assistive devices to improve workers conditions. However, implementing this type of robot in factories requires quantifying their ergonomic benefit. In this context, the objective of this thesis is to develop a method to assess collaborative robot that are intended to be used in PSA Peugeot Citroen factories. In this framework, the right mono-arm ABLE exoskeleton, designed by the CEA-LIST has been used. With a biomechanical analysis of an industrial manipulation task, we have been able to assess the benefit of the exoskeleton in terms of physical load reduction. We also proposed in this work to assess neuromuscular mechanisms underlying the industrial task performed in interaction with the exoskeleton. On the basis of the human motor control theory and using an inverse optimisation method, objectives functions such as jerk, joint torque or energy that characterize the human manipulation task in terms of efforts, kinematics and execution time, have been identified. This improved understanding of human upper limb movements then allowed reviewing the exoskeleton design in order to propose an optimal command strategy adapted to the execution of industrial tasks.

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A novel controller termed as Experience Mapping based Prediction Controller (EMPC) is developed in this work. EMPC is developed utilizing the broad control concepts of human motor control (HMC). The concepts of HMC are utilized to develop the core concepts of EMPC for the control of ideal Type-1 LTI systems. The control accuracy of the developed concepts is studied and the mathematical stability criterion for the controller is developed. The applicability of EMPC for the control of real world problems is tested on a Permanent Magnet DC motor based position control system. 1. Novel learning methods are presented to form experience mapped knowledge-base (EMK) which is used for the creation of the forward and inverse models. 2. Control and Adaptation Techniques which overcome the presence of non idealities are developed using the inverse model. 3. Two separate techniques which utilize the forward model for improving the adaptation capabilities of EMPC are developed. 4. Two novel techniques are developed for the improvement of the tracking performance in terms of the accuracy and smoothness of tracking. These techniques are tested under various system conditions including large dynamic parameter changes on a simulation model and a practical setup. The performance of EMPC is compared against that of PID, MRAC and LQG controllers for all the proposed techniques and EMPC is found to perform significantly better under the various system conditions in terms of transient and steady state characteristics. Finally, the effectiveness of EMPC in stabilizing unstable systems using the concepts developed is tested on a practical Inverted Pendulum system. The problem of the simultaneous development of experiences and control of the system is addressed with the stabilizing problem. The proposed controller, EMPC provides an alternative approach for the existing control of systems without the requirement of an accurate system mathematical model. Its capability to learn by directly interacting with the system and adapt using experiences makes it an attractive alternative to other control techniques present in literature. Keywords: EMPC, Position Control, PMDC motors

abstract: Daily collaborative tasks like pushing a table or a couch require haptic communication between the people doing the task. To design collaborative motion planning algorithms for such applications, it is important to understand human behavior. Collaborative tasks involve continuous adaptations and intent recognition between the people involved in the task. This thesis explores the coordination between the human-partners through a virtual setup involving continuous visual feedback. The interaction and coordination are modeled as a two-step process: 1) Collecting data for a collaborative couch-pushing task, where both the people doing the task have complete information about the goal but are unaware of each other's cost functions or intentions and 2) processing the emergent behavior from complete information and fitting a model for this behavior to validate a mathematical model of agent-behavior in multi-agent collaborative tasks. The baseline model is updated using different approaches to resemble the trajectories generated by these models to human trajectories. All these models are compared to each other. The action profiles of both the agents and the position and velocity of the manipulated object during a goal-oriented task is recorded and used as expert-demonstrations to fit models resembling human behaviors. Analysis through hypothesis teasing is also performed to identify the difference in behaviors when there are complete information and information asymmetry among agents regarding the goal position. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2020