<p>Due to their rapid technological advancement, mobile sensors such as unmanned aerial vehicles (UAVs) are seeing growing application in the area of multi-target tracking and identity management (MTIM). For efficient and sustainable performance of a MTIM system with mobile sensors, proper algorithms are needed to both effectively estimate the states/identities of targets from sensing data and optimally guide the mobile sensors based on the target estimates. One major challenge in MTIM is that a target may be temporarily lost due to line-of-sight breaks or corrupted sensing data in cluttered environments. It is desired that these targets are kept tracking and identification, especially when they reappear after the temporary loss of detection. Another challenging task in MTIM is to correctly track and identify targets during track coalescence, where multiple targets get close to each other and could be hardly distinguishable. In addition, while the number of targets in the sensors’ surveillance region is usually unknown and time-varying in practice, many existing MTIM algorithms assume their number of targets to be known and constant, thus those algorithms could not be directly applied to real scenarios.</p> <p>In this research, a set of solutions is developed to address three particular issues in MTIM that involves the above challenges: 1) using a single mobile sensor with a limited sensing range to track multiple targets, where the targets may occasionally lose detection; 2) using a network of mobile sensors to actively seek and identify targets to improve the accuracy of multi-target identity management; and 3) tracking and managing the identities of an unknown and time-varying number of targets in clutter.</p>

This dissertation describes the evolution of the different local magnetic field generating systems for independent actuation of multiple microrobots. A description of the developed hardware, system characterization tests, and experimental results are presented. The system is designed for automated control of multiple microrobots. Finally, sample micromanipulation tasks are demonstrated using the new microrobot design, showcasing its improved manipulation capabilities.<br>First, a mm-scale local magnetic field generating system designed for single layer coils is used to control 3.175 mm size N52 magnets as robots independently in the workspace. The controller used a set of local equilibrium points that were generated from a sequence of coil currents around the robots from one state to the next. The robots moved along paths computed through optimal control synthesis approach to solve complex micromanipulation tasks captured by global LTL formulas. However, the use of local equilibrium points as the states limited the motion of the robot in the workspace to simple tasks. Also, the interaction between the robots limited the robots to stay within far distances with each other. Hence a larger workspace based coil is designed to actuate up to four mm-scale robots in the workspace.<br><br>To improve the resolution of motion of these robots in the workspace, the mm-scale coils are modeled extensively. The forces generated by various coil combinations of the array are modeled and solutions for different actuation force directions are discovered for different locations in the coil. A path planning problem is formulated as a Markov decision process that solves a policy to reach a goal from any location in the workspace. The MDP formulation is also expanded to work when other robots are present in the workspace. The formulation considers the interaction force between the robots and changes the policy to reach the goal location which reduces in the uncertainty of motion of the robot in the presence of interactions from other robots in the workspace.<br><br>The mm-scale coils are difficult to scale down for microrobotic applications and hence a new microscale local magnetic field system was designed. A new microscale local magnetic field system which consisted of two 8 × 8 array of coils aligned in two axes in two layers of a PCB was designed which could actuate robots as small as 1 mm in the plane. The microcoils in the second layer are also able to generate sufficient magnetic field gradients in the workspace, while the traces below it are spaced adequately to eliminate their influence in the workspace. A new microrobot design also enabled the orientation control of the microrobot for performing micromanipulation tasks. However, only two robots could be independently actuated in this workspace due to interaction between the robots.<br><br>In pursuit of actuation smaller and multiple robots in a small workspace, a serpentine coil based local magnetic field generating system was designed to control of the motion of magnets as small as 250 µm. The net size of the robot is 750 µm to enable orientation control and prevent tipping during motion. This system is capable of simultaneous independent closed loop control of up to 4 microrobots. The motion of the robot using the coils resembled that of a stepper motor which enabled the use of sine-cosine functions to specify currents in the coils for smooth motion of the<br>microrobot in the workspace. The experiments demonstrated the capability of the microrobot and platform to simultaneously actuate up to four robots independently and successfully perform manipulation tasks. The ability to control the orientation of the magnet is finally demonstrated that has improved ability to perform manipulation tasks.<br><br>

<div>With the upsurge of greenhouse gas emissions and rapid depletion of fossil fuels, the pressure on the transportation industry to develop new vehicles with improved fuel economy without sacrificing performance is on the rise. Hybrid Electric Vehicles (HEVs), which employ an internal combustion engine as well as an electric motor as power sources, are becoming increasingly popular alternatives to traditional engine only vehicles. However, the presence of multiple power sources makes HEVs more complex. A significant task in developing an HEV is designing a power management strategy, defined as a control system tasked with the responsibility of efficiently splitting the power/torque demand from the separate energy sources. Five different types of power management strategies, which were developed previously, are reviewed in this work, including dynamic programming, equivalent consumption minimization strategy, proportional state-of-charge algorithm, regression modeling and long short term memory modeling. The effects of these power management strategies on the vehicle performance are studied using a simplified model of the vehicle. This work also proposes an original power management strategy development using a genetic algorithm. This power management strategy is compared to dynamic programming and several similarities and differences are observed in the results of dynamic programming and genetic algorithm. For a particular drive cycle, the implementation of the genetic algorithm strategy on the vehicle model leads to a vehicle speed profile that almost matches the original speed profile of that drive cycle.</div>

Spacecraft formation flying refers to the coordinated operation of a group of spacecraft with a common objective. While the concept has been in existence for a long time, practical fruition of the ideas was not possible earlier due to technological limitations. The topic has received widespread attention in the last decade, with the development of autonomous control, improved computational facilities and better communication technology. It allows a number of small, lightweight, economical spacecraft to work together to execute the function of a larger, heavier, more complex and expensive spacecraft. The primary advantage of such systems is that they are flexible, modular, and cost-effective.<div><br></div><div>The flexibility of formation flying and other derived concepts comes from the fact that the units are not physically attached, allowing them to change position or orientation when the need arises. To fully realize this possibility, it is important to develop methods for spatial reorganization. This thesis is an attempt to contribute to this development. </div><div><br></div><div>In this thesis, the reconfiguration problem has been formulated as a single system with multiple inputs and multiple outputs, while preserving the individuality of the agents to a certain degree. The agents are able to communicate with their neighbors by sharing information. In this framework, a distributed closed-loop stabilizing controller has been developed, that would drive the spacecraft formation to a target shape. An expression for the controller gain as a function of the graph Laplacian eigenvalues has also been derived. The practical applications of this work have been demonstrated through simulations</div>

<p> In this thesis, we investigate and characterize policies for task allocation to teams of agents in settings with heterogeneity and risk. We first consider a scenario consisting of a set of heterogeneous mobile agents located at a base (or depot), and a set of tasks dispersed over a geographic area. The agents are partitioned into different types. The tasks are partitioned into specialized tasks that can only be done by agents of a certain type, and generic tasks that can be done by any agent. The distances between every pair of tasks are specified and satisfy the triangle inequality. Given this scenario, we address the problem of allocating these tasks among the available agents (subject to type compatibility constraints) while minimizing the maximum travel cost for any agent. We first look at the Heterogeneous Agent Cycle Problem (HACP) where agents start at a common base (or depot) and need to tour the set of tasks allocated to them before returning to the base. This problem is NP-hard, and we provide a 5-approximation algorithm. We then consider the Heterogeneous Agent Path Problem (HAPP) where agents can start from arbitrary locations and are not constrained to return to their start location. We consider two approaches to solve HAPP and provide a 15-approximation algorithm for HAPP.</p> <p> We then look at the effect of risk on path planning by considering a scenario where a mobile agent is required to collect measurements from a geographically dispersed set of sensors and return them to a base. The agent faces a risk of destruction while traversing the environment to reach the sensors and gets the reward for gathering a sensor measurement only if it successfully returns to base. We call this the Single Agent Risk Aware Task Execution (SARATE) problem. We characterize several properties of the optimal policy for the agent. We provide the optimal policy when the risk of destruction is sufficiently high and evaluate several heuristic policies via simulation. We then extend the analysis to multiple heterogeneous agents. We show that the scoring scheme is submodular when the risk is sufficiently high, and the greedy algorithm gives solutions that provide a utility that is guaranteed to be within 50% of the optimal utility. </p>

The work in this dissertation concerns the construction of a robust iterative learning control (ILC) algorithm for a class of systems characterized by measurement delays, parametric uncertainty, and linear parameter varying (LPV) dynamics. One example of such a system is the twin roll strip casting process, which provides a practical motivation for this research. I propose three ILC algorithms in this dissertation that advance the state of the art. The first algorithm compensates for measurement delays that are longer than a single iteration of a periodic process. I divide the delay into an iterative and residual component and show how each component effects the asymptotic stability properties of the ILC algorithm. The second algorithm is a coupled delay estimation and ILC algorithm that compensates for time-varying measurement delays. I use an adaptive delay estimation algorithm to force the delay estimate to converge to the true delay and provide stability conditions for the coupled delay estimation and ILC algorithm. The final algorithm is a norm optimal ILC algorithm that compensates for LPV dynamics as well as parametric uncertainty and time delay estimation error. I provide a tuning method for the cost function weight matrices based on a sufficient condition for robust convergence and an upper bound on the norm of the error signal. The functionality of all three algorithms is demonstrated through simulated case studies based on an identified system model of the the twin roll strip casting process. The simulation testing is also augmented with experimental testing of select algorithms through collaboration with an industrial sponsor.

<p>Due to the rapid development of technologies for unmanned aircraft systems (UAS's), the supply and demand market for UAS's is expanding globally. With the great number of UAS's ready to fly in civilian airspace, an UAS aircraft traffic management system that can guarantee the safe, resilient and efficient operation of UAS's is absent. The vast majority of existing literature on UAS traffic lacks of the attention to the fundamental characteristics of UAS operation, which leads to models and methods that are difficult to implement or lacks scalability. Motivated by these challenges, this research aims at achieving three objectives: 1) the proper frameworks that scale well with high-frequency, high-density UAS operations, 2) the models that captures the fundamental characteristics of UAS operations, 3) the methods that can be implemented in practice with guarantees of efficiency, safety, and resilience. In particular, the objectives are studied at low-level UAS traffic congestion control, agent-level UAS configuration control and unknown agent prediction. The proposed frameworks and obtained results offer comprehensive and practical guidelines of real world UAS operations at different levels.</p>

<p> </p> <p>This work proposes a proof of concept along with a prototype of a multi-agent autonomous drone system that can be used to detect, and capture a intruding adversarial drone. The functional Counter Unmanned Aerial System (CUAS) prototype is used to convey the feasibility of a persistent multi-agent aerial protection and apprehension system by demonstrating important features of the mission through both simulation and field testing.<br> </p> <p> </p>

<p>Automated Vehicle Path Following Control (PFC) is an advanced control system that can regulate the vehicle into a collision-free region in the presence of other objects on the road. Common collision avoidance functions, such as forward collision warning and automatic emergency braking, have recently been developed and equipped on production vehicles. However, it is impossible to develop a perfectly precise vehicle model when the vehicle is driving. The most PFC did not consider uncertainties in the vehicle model, external disturbances, and parameter variations at the same time. To address the issues associated with this important feature and function in autonomous driving, a new vehicle PFC is proposed using a robust model predictive control (MPC) design technique based on matrix inequality and the theoretical approach of the hybrid $\&$ switched system. The proposed methodology requires a combination of continuous and discrete states, e.g. regulating the continuous states of the AV (e.g., velocity and yaw angle) and discrete switching of the control strategy that affects the dynamic behaviors of the AV under different driving speeds. Firstly, considering bounded model uncertainties, norm-bounded external disturbances, the system states and control matrices are modified. In addition, the vehicle time-varying longitudinal speed is considered, and a vehicle lateral dynamic model based on Linear Parameter Varying (LPV) is established by utilizing a polytope with finite vertices. Then the Min-Max robust MPC state feedback control law is obtained at every timestamp by solving a set of matrix inequalities which are derived from Lyapunov stability and the minimization of the worst-case in infinite-horizon quadratic objective function. Compared to adaptive MPC, nonlinear MPC, and cascade LPV control, the proposed robust LPV MPC shows improved tracing accuracy along vehicle lateral dynamics. Finally, the state feedback switched LPV control theory with separate Lyapunov functions under both arbitrary switching and average-dwell-time (ADT) switching conditions are studied and applied to cover the path following control in full speed range. Numerical examples, tracking effectiveness, and convergence analysis are provided to demonstrate and ensure the control effectiveness and strong robustness of the proposed algorithms.</p>

<p>While traditional rigid robots are widely used in almost all applications today, their rigidity restricts the use of these robots in environments where interaction with the surroundings or humans is inevitable. This is where soft robots come into play. Due to their compliant and adaptable nature, these robots can safely interact with humans and traverse through unpredictable, cluttered environments. This research focuses on the navigation of a special class of soft growing robots called Vine robots. Vine robots can easily maneuver through tight spaces and rough terrain and have an added advantage of speed over general soft robots. In this work, we develop a model which localizes the Vine robot in an unknown surrounding by giving us the position of the tip of the robot at every instant. The model exploits the passive steering of growing robots using obstacle aided navigation. The robot is sensorized to record the orientation of the its tip and the total length it has grown to. This data along with the force generated on collision with the environment is used to localize the robot in space. The localization model is implemented using the sensor data. The accuracy of this model is then verified by comparing the tip position of the robot we have calculated with its predicted position and the actual position as measured by an overhead camera. It is concluded that the robot can be localized in an environment with a maximum error of 7.65 cm (10\%) when the total length the robot has grown to is 170 cm. </p>