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>

<div>Class 8 trucks on average drive the most miles and consume the most fuel of any major vehicle category annually. Indiana specifically is the fifth busiest state for commercial freight traffic and moves $750 billion dollars of freight annually, and this number is expected to grow by 60% by 2040. Reducing fuel consumption for class 8 trucks would have a significant benefit on business and the proportional decrease in CO₂ would be exceptionally beneficial for the environment.</div><div><br></div><div>Platooning is one of the most important strategies for increasing class 8 truck fuel savings. Platooning alone can help trucks save upwards of 7% platoon average fuel savings on flat ground. However, it can be difficult for a platooning controller to maintain a desired truck separation during uncoordinated shifting events. Using a high-fidelity simulation model, it is shown that simultaneous shifting–having the follow truck shift whenever the lead truck shifts (unless shifting would cause its engine to overspeed or underspeed)–decreases maximum truck separation by 24% on a moderately challenging grade route and 40% on a heavy grade route.</div><div><br></div><div>Model Predictive Control (MPC) of the follow truck is considered as a means to reduce the distance the follow truck falls behind during uncoordinated shifting events. The result in simulation is a reduction in maximum truck separation of 1% on a moderately challenging grade route and 19% on a heavy grade route. However, simultaneous shifting largely alleviates the need for MPC for the sake of tracking for the follow truck.</div><div><br></div><div>A different MPC formulation is considered to dynamically change the desired set point for truck separation for routes through a strategy called Route Optimized Gap Growth (ROGG). The result in simulation is 1% greater fuel savings on a moderately challenging grade route and 7% greater fuel savings on a route with heavy grade for the follow truck.</div>

<div>Aerospace and shipboard power systems, as well as merging terrestrial microgrids, typically include a large ercentage of regulated power-electronic loads. It is well nown that such systems are prone to so-called negative- mpedance instabilities that may lead to deleterious scillations and/or the complete collapse of bus voltage. umerous small-displacement criteria have been developed o ensure dynamic stability for small load perturbations, and echniques for estimating the regions of asymptotic stability bout specic equilibrium points have previously been established. However, these criteria and analysis techniques o not guarantee system stability following large nd/or rapid changes in net load power. More recent research as focused on establishing criteria that ensure arge-displacement stability for arbitrary time varying loads rovided that the net load power is bounded. These yapunov-based techniques and recent advancements in eachability analysis described in this thesis are applied to xample dc and ac microgrids to not only introduce a large- isplacement stability margin, but to demonstrate that the elected systems can be designed to be large-displacement table with practicable constraints and parameters.</div>

<p>Dynamic systems and robotic manipulators designed for time-optimal point-to-point motion are adversely affected by residual vibrations introduced due to the joint flexibility inherent in the system. Over the years, multiple techniques have been employed to improve the efficiency of such systems. While some techniques focus on increasing the system damping to efficiently dissipate the residual energy at the end of the move, several techniques achieve rapid repositioning by developing cleverly shaped input profiles that aim to reduce energy around the natural frequency to avoid exciting the resonant modes altogether. In this work, a numerical framework for constructing shaped inputs using a Versine basis function with peak acceleration constraint has been developed and improvements for the existing numerical framework for the Ramped Sinusoid basis function have been made to extend the range of values of the weighting function and improve the computational time. Performance metrics to evaluate the effectiveness of the numerical framework in minimizing residual vibrations have been developed. The effects of peak input acceleration and weighting function on the residual vibration in the system have been studied. The effectiveness of the method has been tested under multiple conditions in simulations and the results were validated by performing experiments on a two-link flexible joint robotic arm. The simulation and experimental results conclusively show that the inputs developed using the constrained numerical approach result in better residual vibration performance as compared to that of an unshaped input. </p>