A dissertation submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science in Engineering in the Control Research Group School of Electrical and Information Engineering, Johannesburg, 2017 / In this research, we observed a torque-related limitation in the swing-up control of underactuated mechanical systems which had been integrated with viscous damping in the unactuated joint. The objective of this research project was thus to develop a practical work-around solution to this limitation. The nth order underactuated robotic system is represented in this research as a collection of compounded pendulums with n-1 actuators placed at each joint with the exception of the first joint. This system is referred to as the PAn-1 robot (Passive first joint, followed by n-1 Active joints), with the Acrobot (PA1 robot) and the PAA robot (or PA2 robot) being among the most well-known examples. A number of friction models exist in literature, which include, and are not exclusive to, the Coulomb and the Stribeck effect models, but the viscous damping model was selected for this research since it is more extensively covered in existing literature. The effectiveness of swing-up control using Lyapunov’s direct method when applied on the undamped PAn-1 robot has been vigorously demonstrated in existing literature, but there is no literature that discusses the swing-up control of viscously damped systems. We show, however, that the application of satisfactory swing-up control using Lyapunov’s direct method is constrained to underactuated systems that are either undamped or actively damped (viscous damping integrated into the actuated joints only). The violation of this constraint results in the derivation of a torque expression that cannot be solved for (invertibility problem, for systems described by n > 2) or a torque expression which contains a conditional singularity (singularity problem, for systems with n = 2). This constraint is formally summarised as the matched damping condition, and highlights a clear limitation in the Lyapunov-related swing-up control of underactuated mechanical systems. This condition has significant implications on the practical realisation of the swing-up control of underactuated mechanical systems, which justifies the investigation into the possibility of a work-around. We thus show that the limitation highlighted by the matched damping condition can be overcome through the implementation of the partial feedback linearisation (PFL) technique. Two key contributions are generated from this research as a result, which iii include the gain selection criterion (for Traditional Collocated PFL), and the convergence algorithm (for noncollocated PFL). The gain selection criterion is an analytical solution that is composed of a set of inequalities that map out a geometric region of appropriate gains in the swing-up gain space. Selecting a gain combination within this region will ensure that the fully-pendent equilibrium point (FPEP) is unstable, which is a necessary condition for swing-up control when the system is initialised near the FPEP. The convergence algorithm is an experimental solution that, once executed, will provide information about the distal pendulum’s angular initial condition that is required to swing-up a robot with a particular angular initial condition for the proximal pendulum, along with the minimum gain that is required to execute the swing-up control in this particular configuration. Significant future contributions on this topic may result from the inclusion of more complex friction models. Additionally, the degree of actuation of the system may be reduced through the implementation of energy storing components, such as torsional springs, at the joint. In summary, we present two contributions in the form of the gain selection criterion and the convergence algorithm which accommodate the circumnavigation of the limitation formalised as the matched damping condition. This condition pertains to the Lyapunov-related swing-up control of underactuated mechanical systems that have been integrated with viscous damping in the unactuated joint. / CK2018

<p>This thesis presents a method for the design of a predictive controller with variable step sizes.Predictive methods such as receding horizon control (or model predictive control) use aa fixed sampling frequency when updating the inputs. In the proposed method, the switchingtimes are incorporated into an optimization problem, thus resulting in anadaptive step-size control process. The controller with variable timesteps is shown to require less tuning and to reduce the number of expensive model evaluations.An alternate solution approach had to be developed to accommodate the new problem formulation.The controller's stability is proven in a context that does not require terminal cost or constraints.The thesis presents examples that compare the performance of the variable switching time controllerwith the receding horizon method with a fixed step size. This research opens many roads for futureextension of the theoretical work and practical applications of the controller.</p> / Doctor of Science (PhD)

<p>Have a nice day :)</p> / <p>This thesis presents a unified framework for coordination and control of human-in-the-loop asymmetric semi-autonomous robotic systems. It introduces a highly general teleoperation system configuration involving any number of operators, haptic interfaces, and robots with possibly different degrees of mobility. The proposed framework allows for mixed teleoperation/autonomous control of user-defined subtasks by establishing position/force tracking as well as kinematic constraints among relevant <em>teleoperation control frames</em>. Three layers of velocity-based autonomous subtasks at different priority levels with respect to human teleoperation are integrated into the control system design. The control strategy is hierarchical comprising of a high-level teleoperation coordinating controller and low-level joint velocity controllers. A Lyapunov-based adaptive joint-space velocity controller is presented as one candidate for the low-level control. The approach utilizes idempotent, generalized pseudoinverse and weighting matrices, as well as a soft-switching rank changing algorithm in order to achieve new performance objectives that are defined for such asymmetric semi-autonomous teleoperation systems. A detailed analysis of system performance and stability is presented. The proposed framework constitutes the most general formulation and solution for the teleoperation control problem to date. It yields many interesting and useful system configurations never studied before, in addition to those already considered in the literature. In particular, seven system configurations arising from the proposed teleoperation architecture are analyzed and studied in detail. Experimental results are provided to demonstrate the desired system response in these configurations. Moreover, human factors experiments are carried out to assess operator(s) performance in maneuverability and grasping under various teleoperation system configurations. The results show statistically significant performance improvement in teleoperation of a nonholonomic mobile robot and telegrasping using a twin-armed manipulator.</p> / Doctor of Philosophy (PhD)

<p>In this thesis, the problem of estimating the clutter spatial intensity function for the multitarget tracking algorithms has been considered. In many scenarios, after the signal detection process, measurement points provided by the sensor (e.g., sonar, infrared sensor, radar) are not distributed uniformly in the surveillance region as assumed by most tracking algorithms. On the other hand, in order to obtain accurate results, the multitarget tracking algorithm requires information about clutter’s spatial intensity. Thus, non-homogeneous clutter spatial intensity has to be estimated from the measurement set and the tracking filter’s output. Also, in order to take advantage of existing tracking algorithms, it is desirable for the clutter estimation method to be integrated into the tracker itself. In this thesis, the clutter is modeled by a non-homogeneous Poisson point (NHPP) process with a spatial intensity function g(z). To calculate the value of the clutter spatial intensity, all we need to do is estimating g(z). First, two new methods for joint spatial clutter intensity estimation and multitarget tracking using the Probability Hypothesis Density (PHD) Filter are presented. Then, based on NHPP process, multitarget multi-Bernoulli processes and set calculus, the approximated Bayesian method is extended to joint the non–homogeneous clutter background estimation and multitarget tracking with standard multitarget tracking algorithms, like the Multiple Hypothesis Tracking (MHT) and the Joint Integrated Probabilistic Data Association (JIPDA) tracker. Finally, a kernel density method is proposed for the clutter spatial intensity estimation problem. Simulation results illustrate the performance of the above algorithms, both in terms of the false track number and the true track initialization speed. All proposed algorithms show the ability to improve the performance of the multitarget tracker in the presence of slowly time varying non–homogeneous clutter background.</p> / Doctor of Philosophy (PhD)

<p>Driver Assistance Systems like Adaptive Cruise Control (ACC) can help prevent accidents by reducing the workload on the driver. However, this can only be accomplished if the driver can rely on the system to perform safely even in the presence of faults.</p> <p>In this thesis we develop an Adaptive Cruise Control model that will be used to investigate Hybrid Systems Fault Diagnosis techniques. System Identification is performed upon an electric motor to obtain its transfer function. This electric motor belongs to a 1/10th scale RC car that is being used as part of a test bench for the Adaptive Cruise Control system. The identified model is then used to design a hybrid controller which will switch between a set of LQR controllers to create an example Adaptive Cruise Controller. The model of the controller is then used to generate fixed point code for implementation on the testbed and validation against the model controller. Finally a detailed hazard analysis of the resulting system is performed using Leveson's STPA.</p> / Master of Applied Science (MASc)

<p>Robotic platforms have several characteristics such as speed and precision that make them enticing for use in medical procedures. Companies such as Intuitive Medical and Titan Medical have taken advantage of these features to introduce surgical robots for minimally invasive procedures. Such robots aim to reduce procedure and patient recovery times. Current technology requires platforms to be master-slave manipulators controlled by a surgeon, effectively converting the robot into an expensive surgical tool. Research into the interaction between robotic platforms and deformable objects such as human tissue is necessary in the development of autonomous and semi-autonomous surgical systems. This thesis investigates a class of robust linear controllers based on a worst case performance measure known as the $H_{\infty}$ norm, for the purpose of performing so called Indirect Deformable Object Manipulation (IDOM). This task allows positional regulation of regions of interest in a deformable object without directly interacting with them, enabling tasks such as stabilization of tumors during biopsies or automatic suturing. A complete approach to generating linear $H_{\infty}$ based controllers is presented, from derivation of a plant model to the actual synthesis of the controller. The introduction of model uncertainty requires $\mu$ synthesis techniques, which extend $H_{\infty}$ designs to produce highly robust controller solutions. In addition to $H_{\infty}$ and $\mu$ synthesis designs, the thesis presents an approach to design an optimal PID controller with gains that minimize the $H_{\infty}$ norm of a weighted plant. The three control approaches are simulated performing set point regulation in $\text{MATLAB}^{TM}$'s $simulink$. Simulations included disturbance inputs and noises to test stability and robustness of the approaches. $H_{\infty}$ controllers had the best robust performance of the controllers simulated, although all controllers simulated were stable. The $H_{\infty}$ and PID controllers were validated in an experimental setting, with experiments performed on two different deformable synthetic materials. It was found that $H_{\infty}$ techniques were highly robust and provided good tracking performance for a material that behaved in a relatively elastic manner, but failed to track well when applied to a highly nonlinear rubber compound. PID based control was outperformed by $H_{\infty}$ control in experiments performed on the elastic material, but proved to be superior when faced with the nonlinear material. These experimental findings are discussed and a linear $H_{\infty}$ control design approach is proposed.</p> / Master of Applied Science (MASc)

<p>This thesis investigates two significant problems in control and coordination of complex teleoperation systems as they relate to the operation of a mobile robotic manipulator. The first part of the thesis focuses on the design of a control framework to resolve kinematic redundancy in teleoperation of a mobile robotic manipulator. Apart from the redundancy, workspace considerations for the operator and robot and asymmetry of master and slave systems pose significant design challenges in such telerobotic systems . The second part of the thesis considers psychophysical aspects of teleoperation from the operator's perspective. This part presents a method for automatic {\em optimal} positioning of a single camera for a remotely navigated mobile robot in systems with a controllable camera platform. In each part, a constrained optimization problem is formulated and solved in real time. The solution of these optimization problems are integrated seamlessly into the teleoperation control framework in order to assist the operator in accomplishing the main task. The proposed control framework in the first part allows the operator to concentrate on the manipulation task while the mobile base and arm joint configurations are automatically {\em optimized} according to the needs of the task. Autonomous control subtasks are defined to guide the base and the arms towards this optimal configuration while the operator teleoperates the end-effector(s) of the mobile arm(s). The teleoperation and autonomous control tasks have adjustable relative priorities set by the system designer. The work in the second part enables the operator to focus mainly on navigation and manipulation while the camera viewpoint is automatically adjusted. The workspace and motion limits of the camera system and the location of the obstacles are taken into consideration in camera view planning. A head tracking system enables the operator to use his/her head movements as an extra control input to guide the camera placement, if and when necessary. Both proposed controllers have been implemented and evaluated in teleoperation experiments and user studies. The results of these experiments confirm the effectiveness of these controllers and demonstrate significant improvements compared to other existing controllers from the literature included in the studies.</p> / Master of Applied Science (MASc)

<p>In this thesis, a human-in-the-loop control system is presented to assist an operator in teleoperation of a mobile robot. In a conventional teleoperation paradigm, the human operator would directly navigate the robot without any assistance which may result in poor performance in complex and unknown task environments due to inadequacy of visual feedback. The proposed method in this thesis builds on an earlier general control framework that systematically combines teleoperation and autonomous control subtasks. In this approach, the operator controls the mobile robot (slave) using a force-feedback haptic interface (master). Teleoperation control commands coordinate master and slave robots while an autonomous control subtask helps the operator avoid collisions with obstacles in the robot task environment by providing corrective force feedback. The autonomous collision avoidance is based on a Model Predictive Control (MPC) philosophy. The autonomous subtask control commands are generated by formulating and solving a constrained optimization problem over a rolling horizon window of time into the future using system models to predict the operator force and robot motion. The goal of the optimization is to prevent collisions within the prediction horizon by applying corrective force feedback, while minimizing interference with the operator teleoperation actions. It is assumed that the obstacles are stationary and sonar sensors mounted on the mobile robot measure the obstacle distances relative to the robot. Two formulation of MPC-based collision avoidance are proposed. The first formulation directly incorporates raw observation points as constraints in the MPC optimization problem. The second formulation relies on a line segment representation of the task environment. This thesis employs the well-known Hough transform method to effectively transform the raw sensor data into line segments. The extracted line segments constitute a compact model for the environment that is used in the formulation of collision constraints. The effectiveness of the proposed model-predictive control obstacle avoidance schemes is demonstrated in teleoperation experiments where the master robot is a 3DOF haptic interface and the slave is a P3-DX mobile robot equipped with eight (8) sonar sensors at the front.</p> / Master of Applied Science (MASc)

This thesis aims to investigate the controllability and observability of the discrete fractional linear time-invariant state-space model. First, we will establish key concepts and properties which are the tools necessary for our task. In the third chapter, we will discuss the discrete state-space model and set up the criteria for these two properties. Then, in the fourth chapter, we will attempt to apply these criteria to the discrete fractional model. The general flow of our objectives is as follows: we start with the first-order linear difference equation, move on to the discrete system, then the fractional difference equation, and finally the discrete fractional system. Throughout this process, we will develop the solutions to the (fractional) difference equations, which are the basis of our criteria.

Social control theory focuses on why some people do not commit deviant behaviours, such as illicit drug use. It proposes that bonding to conventional society constrains deviant conduct. In the book Causes of Delinquency, Hirschi distinguished four elements of social bonds: attachment, commitment, involvement, and belief. This study draws upon data from the 2012 Canadian Alcohol and Drug Monitor Survey to examine the effect of social control theory, specifically the element of attachment, on controlling cannabis use. This study also uses the element of attachment to interpret gender and rural/non-rural area differences in cannabis use. Two hypotheses are offered: (1) females are less likely to use cannabis than males because females have greater attachment to others; (2) rural residents are less likely to use cannabis than non-rural residents because rural residents have greater attachment to others. The research methods in the study are cross-tabulation analysis and binary logistic regression. The statistical analysis results support both hypotheses: females have a significantly lower rate of cannabis use than males and rural residents have a significantly lower rate of cannabis use than non-rural residents. Having greater attachment to others may be associated with a decreased rate of cannabis use. Policy and further research recommendations are made.