Unmanned aerial vehicles are a salient solution for rapid deployment in disaster relief, search and rescue, and warfare operations. In these scenarios, the agility, maneuverability and speed of the UAV are vital components towards saving human lives, successfully completing a mission, or stopping dangerous threats. Hence, a high speed, highly agile, and small footprint unmanned aerial vehicle capable of carrying minimal payloads would be the best suited design for completing the desired task. This thesis presents the design, analysis, and realization of a dual-nacelle tiltrotor unmanned aerial vehicle. The design of the dual-nacelle tiltrotor aerial vehicle utilizes two propellers for thrust with the ability to rotate the propellers about the sagittal plane to provide thrust vectoring. The dual-nacelle thrust vectoring of the aerial vehicle provides a slimmer profile, a smaller hover footprint, and allows for rapid aggressive maneuvers while maintaining a desired speed to quickly navigate through cluttered environments. The dynamic model of the dual-nacelle tiltrotor design was derived using the Newton-Euler method and a nonlinear PD controller was developed for spatial trajectory tracking. The dynamic model and nonlinear PD controller were implemented in Matlab Simulink using SimMechanics. The simulation verified the ability of the controlled tiltrotor to track a helical trajectory. To study the scalability of the design, two prototypes were developed: a micro scale tiltrotor prototype, 50mm wide and weighing 30g, and a large scale tiltrotor prototype, 0.5m wide and weighing 2.8kg. The micro scale tiltrotor has a 1.6:1 thrust to weight ratio with an estimated flight time of 6 mins in hover. The large scale tiltrotor has a 2.3:1 thrust to weight ratio with an estimated flight time of 4 mins in hover. A detailed realization of the tiltrotor prototypes is provided with discussions on mechanical design, fabrication, hardware selection, and software implementation. Both tiltrotor prototypes successfully demonstrated hovering, altitude, and yaw maneuvering while tethered and remotely controlled. The developed prototypes provide a framework for further research and development of control strategies for the aggressive maneuvering of underactuated tiltrotor aerial vehicles.

This thesis presents the design and control of a cable-actuated mobile snake robot. The goal of this research is to reduce the size of snake robots and improve their locomotive efficiency by simultaneously actuating groups of links to fit optimized curvature profiles. The basic functional unit of the snake is a four-link, single degree of freedom module that bends using an antagonistic cable-routing scheme. Elastic elements in series with the cables and the coupled nature of the mechanism allow each module to detect and automatically respond to obstacles. The mechanical and electrical designs of the bending module are presented, with emphasis on the cable-routing scheme, key optimizations, and the use of series elastic actuation. An approximate expression for the propulsive force generated by a snake as a function of its articulation (i.e. the number of links it contains divided by its body length) is derived and a closed-form approximation for the optimal phase offset between joints to maximize the speed of a snake is obtained by simplifying a previous result. A simplified model of serpentine locomotion that considers the forces acting on a single link as it traverses a sinusoid is presented and compared to a detailed multibody dynamic model. Control strategies for snake robots with coupled joints are developed, along with a feedback linearization of the joint dynamics. Experimental studies of force control, locomotion, and adaptation to obstacles using a fully integrated prototype are presented and compared with simulated results. / MS

"Recent works on the control of wheeled mobile robots have shifted from the use of the kinematic model to the use of the dynamic model. Since theoretical results typically treat the inputs to the dynamic model as torques, few experimental results have been provided, as torque is typically not the input to most commercially available robots. Few papers have implemented controllers based on the dynamic model, and those that have did not address the issue of identifying the parameters of the dynamic model. This work focuses on a method for identifying the parameters of the dynamic model of a wheeled mobile robot. The method is shown to be both effective and easy to implement, and requires no prior knowledge of what the parameters may be. Experimental results on two mobile robots of different scale demonstrate its effectiveness. The estimates of the parameters created by the proposed method are then used in an adaptive controller to verify their accuracy. For future work, this method should be completed autonomously in a two-part manner, onboard the mobile robot. First, the robot should perform the method proposed here to generate an initial parameter estimate, and then use adaptive control to update the estimates."

For precise robot control, endpoint compliance strategies utilize feedback from a force sensor located near the tool/workpiece interface. Such endpoint force control systems have been observed in the laboratory to be limited to unsatisfactory closed-loop performance. This paper discusses the particular dynamic properties of robot systems which can lead to instability and limit performance. A series of lumped-parameter models is developed in an effort to predict the closed-loop dynamics of a force-controlled single axis arm. The models include some effects of robot structural dynamics, sensor compliance, and workpiece dynamics. The qualitative analysis shows that the robot dynamics contribute to force-controlled instability. Recommendations are made for models to be used in control system design.

Dynamic models have been developed in an attempt to match the response of a robot arm. The experimental data show rigid-body and five resonant modes. The frequency response and pole-zero arrays for various models of structural flexibility are compared with the data to evaluate the characteristics of the models, and to provide insight into the nature of the flexibility in the robot. Certain models are better able to depict transmission flexibility while others describe types of structural flexibility.

Activities of daily living (ADL) such as writing, eating, and object manipulation are challenging for patients suffering from pathological tremors. Pathological tremors are involuntary, rhythmic, and oscillatory movements that manifest in limbs, the head, and other body parts. Among the existing treatments, mechanical loading through wearable rehabilitation devices is popular for being non-invasive and innocuous to the human body. In particular, a few exoskeletons are developed to actively mitigate pathological tremors in the forearm. While these forearm exoskeletons can effectively suppress tremors, they still require significant improvements in ergonomics to be implemented for ADL applications. The ergonomics of the exoskeleton can be improved via design and motion control pertaining to human biomechanics, which leads to better efficiency, comfort, and safety for the user. The wrist is a complicated biomechanical joint with two coupled degrees of freedom (DOF) pivotal to human manipulation capabilities. Existing exoskeletons either do not provide tremor suppression in all wrist DOFs, or can be restrictive to the natural wrist movement. This motivates us to explore a better exoskeleton solution for wrist tremor suppression. We propose TAWE - a wearable exoskeleton that provides alleviation of pathological tremors in all wrist DOFs. The design adopts a 6-DOF rigid linkage mechanism to ensure unconstrained natural wrist movements, and wearability features without extreme tight-binding or precise positioning for convenient ADL applications. When TAWE is equipped by the user, a closed-kinematic chain is formed between the exoskeleton and the forearm. We analyze the coupled multibody dynamics of the human-exoskeleton system, which reveals a few robotic control problems - (i) The first problem is the identification of the unknown wrist kinematics within the closed kinematic chain. We realize the real-time wrist kinematic identification (WKI) based on a novel ellipsoidal joint model that describes the coupled wrist kinematics, and a sparsity-promoting Extended Kalman Filter for the efficient real-time regression; (ii) The second problem is the exoskeleton motion control for tremor suppression. We design a robust adaptive controller (IO-RAC) based on model reference adaptive control and inverse optimal robust control theories, which can identify the unknown model inertia and load, and provide stable tracking control under disturbance; (iii) The third problem is the estimation of voluntary movement from tremorous motion data for the motion planning of exoskeleton. We develop a lightweight and data-driven voluntary movement estimator (SVR-VME) based on least square support vector regression, which can estimate voluntary movements with real-time signal adaptability and significantly reduced time delay. Simulations and experiments are carried out to test the individual performance of robotic control algorithms proposed in this study, and their combined real-time performance when integrated into the full exoskeleton control system. We also manufacture the prototype of TAWE, which helps us validate the proposed solutions in tremor alleviation exoskeletons. Overall, the design of TAWE meets the expectations in its compliance with natural wrist movement and simple wearability. The exoskeleton control system can execute stably in real-time, identify unknown system kinematics and dynamics, estimate voluntary movements, and suppress tremors in the wrist. The results also indicate a few limitations in the current approaches, which require further investigations and improvements. Finally, the proposed exoskeleton control solutions are developed based on generic formulations, which can be applied to not only TAWE, but also other rehabilitation exoskeletons. / Doctor of Philosophy / Activities of daily living (ADL) such as writing, eating, and object manipulation are challenging for patients suffering from pathological tremors, which affect millions of people worldwide. Tremors are involuntary, rhythmic, and oscillatory movements. In recent years, rehabilitation exoskeletons are developed as non-invasive solutions to pathological tremor alleviation. The wrist is pivotal to human manipulation capabilities. Existing exoskeletons either do not provide tremor suppression in all wrist movements, or can be restrictive to natural wrist movements. To explore a better solution with improved performance and ergonomics, we propose TAWE - a wearable exoskeleton that provides tremor alleviation in full wrist motions. TAWE adopts a high-degree-of-freedom mechanism to ensure unconstrained natural wrist movements, and wearability features for convenient ADL applications. The coupled dynamics between the forearm and TAWE leads to a few robotic control problems. We propose novel real-time robotic control solutions in the identification of unknown wrist kinematics, robust adaptive exoskeleton control for tremor suppression, and voluntary movement estimation for motion planning. Later, simulations and experiments validate the TAWE prototype and its exoskeleton control framework for tremor alleviation, and reveal limitations in the current approaches that require further investigations and improvements. Finally, the proposed exoskeleton control solutions are developed based on generic formulations, which can be applied to not only TAWE, but also other rehabilitation exoskeletons.

<p>In this masters thesis we consider a method for experimental identification of the inertial parameters of an industrial robot, using measured torques and joint angles. A dynamic model of the first three joints of the robot has been identified.</p><p>To achieve good identification results, it is important to carefully choose the trajectory for the experimental identification. A method to generate trajectories using two suggested design criteria has been used and evaluated using an ABB industrial robot, and one of them yields good identification results.</p> / <p>Denna rapport behandlar en metod för experimentell identifiering av en stelkroppsmodell för en industrirobot. Metoden använder sig av uppmätta moment och armvinklar för att identifiera parametrarna i en dynamikmodell för robotens tre huvudaxlar.</p><p>För att erhålla bra identifieringsresultat är det viktigt att välja en lämplig identifieringsbana och i detta arbete har en metod för generering av banor använts och utvärderats för två olika designkriterier. Experiment har utförts på en industrirobot från ABB och vi har erhållit goda identifieringsresultat med ett av designkriterierna.</p>

In this masters thesis we consider a method for experimental identification of the inertial parameters of an industrial robot, using measured torques and joint angles. A dynamic model of the first three joints of the robot has been identified. To achieve good identification results, it is important to carefully choose the trajectory for the experimental identification. A method to generate trajectories using two suggested design criteria has been used and evaluated using an ABB industrial robot, and one of them yields good identification results. / Denna rapport behandlar en metod för experimentell identifiering av en stelkroppsmodell för en industrirobot. Metoden använder sig av uppmätta moment och armvinklar för att identifiera parametrarna i en dynamikmodell för robotens tre huvudaxlar. För att erhålla bra identifieringsresultat är det viktigt att välja en lämplig identifieringsbana och i detta arbete har en metod för generering av banor använts och utvärderats för två olika designkriterier. Experiment har utförts på en industrirobot från ABB och vi har erhållit goda identifieringsresultat med ett av designkriterierna.

Model-based control strategies for robot manipulators can present numerous performance advantages when an accurate model of the system dynamics is available. In practice, obtaining such a model is a challenging task which involves modeling such physical processes as friction, which may not be well understood and difficult to model. Furthermore, uncertainties in the physical parameters of a system may be introduced from significant discrepancies between the manufacturer data and the actual system. Traditionally, adaptive and robust control strategies have been developed to deal with parametric uncertainty in the dynamic model, but often require knowledge of the structure of the dynamics. Recent approaches to model-based manipulator control involve data-driven learning of the inverse dynamics relationship, eliminating the need for any a-priori knowledge of the system model. Locally Weighted Projection Regression (LWPR) has been proposed for learning the inverse dynamics function of a manipulator. Due to its use of simple local, linear models, LWPR is suitable for online and incremental learning. Although global regression techniques such as Gaussian Process Regression (GPR) have been shown to outperform LWPR in terms of accuracy, due to its heavy computational requirements, GPR has been applied mainly to offline learning of inverse dynamics. More recent efforts in making GPR computationally tractable for real-time control have resulted in several approximations which operate on a select subset, or sparse representation of the entire training data set. Despite the significant advancements that have been made in the area of learning control, there has not been much work in recent years to evaluate these newer regression techniques against traditional model-based control strategies such as adaptive control. Hence, the first portion of this thesis provides a comparison between a fixed model-based control strategy, an adaptive controller and the LWPR-based learning controller. Simulations are carried out in order to evaluate the position and orientation tracking performance of each controller under varied end effector loading, velocities and inaccuracies in the known dynamic parameters. Both the adaptive controller and LWPR controller are shown to have comparable performance in the presence of parametric uncertainty. However, it is shown that the learning controller is unable to generalize well outside of the regions in which it has been trained. Hence, achieving good performance requires significant amounts of training in the anticipated region of operation. In addition to poor generalization performance, most learning controllers commence learning entirely from `scratch,' making no use of any a-priori knowledge which may be available from the well-known rigid body dynamics (RBD) formulation. The second portion of this thesis develops two techniques for online, incremental learning algorithms which incorporate prior knowledge to improve generalization performance. First, prior knowledge is incorporated into the LWPR framework by initializing the local linear models with a first order approximation of the prior information. Second, prior knowledge is incorporated into the mean function of Sparse Online Gaussian Processes (SOGP) and Sparse Pseudo-input Gaussian Processes (SPGP), and a modified version of the algorithm is proposed to allow for online, incremental updates. It is shown that the proposed approaches allow the system to operate well even without any initial training data, and further performance improvement can be achieved with additional online training. Furthermore, it is also shown that even partial knowledge of the system dynamics, for example, only the gravity loading vector, can be used effectively to initialize the learning.

The subject of this thesis is the design and practical application of a model-based controller with variable structure control (VSC). Robot manipulators are highly non-linear systems, however they form a specific class in the non-linear group. Exact mathematical descriptions of the robot dynamics can be achieved and further, robot manipulators have specific useful properties that can be used for the design of advanced controllers. The inclusion of the inverse dynamic description of the robot manipulator as a feedforward term of the controller (model-based controller) is used to transform two non-linear systems i.e. the controller and the robot, into one linear system. The limitation of this technique arises from the accuracy of the inverse dynamic model. The linearisation only takes place if the model is known exactly. To deal with the uncertainties that arise in the model, a control methodology based on variable structure control is proposed. The design of the controller is based on a Lyapunov approach and engineering considerations of the robot. A candidate Lyapunov function of a pseudo-energy form is selected to start the controller design. The general form of the controller is selected to satisfy the negative definiteness of the Lyapunov function. The initial uncertainties between the actual robot dynamics and the model used in the controller are dealt with using a classical VSC regulator. The deficiencies of this approach are evident however because of the chattering phenomenum. The model uncertainties are examined from an engineering point of view and adjustable bounds are then devised for the VSC regulator, and simulations confirm a reduction in the chattering. Implementation on the SPRINTA robot reveals further limitations in the proposed methodology and the bound adjustment is enhanced to take into account the position of the robot and the tracking errors. Two controllers based on the same principle are then obtained and their performances are compared to a PID controller, for three types of trajectory. Tests reveal the superiority of the devised control methodology over the classic PID controller. The devised controller demonstrates that the inclusion of the robot dynamics and properties in the controller design with adequate engineering considerations lead to improved robot responses.