<div>The goal of this research is to develop a method of control for a team of unmanned aerial and ground robots that is resilient, robust, and scalable given both complete and incomplete information of the environment. The method presented in this paper integrates approximate and optimal methods of path planning integrated with a market-based task allocation strategy. Further work presents a solution to unmanned ground vehicle path planning within the developed mission planning system framework under incomplete information. Deep reinforcement learning is proposed to solve movement through unknown terrain environment. The final demonstration for Advantage-Actor Critic deep reinforcement learning elicits successful implementation of the proposed model.</div>

<div> In recent decades as environmental concerns and the cost and availability of fossil fuels have become more pressing issues, the need to extract more work from each drop of fuel has increased accordingly. Electrification has been identified as a way to address these issues in vehicles powered by internal combustion engines, as it allows existing engines to be operated more efficiently, reducing overall fuel consumption. Two applications of electrification are discussed in the work presented: a series-electric hybrid powertrain from an on-road class 8 truck, and an electrically supercharged diesel engine for use in the series hybrid power system of a wheel loader.</div><div> </div><div> The first application is an experimental powertrain developed by a small start-up company for use in highway trucks. The work presented in this thesis shows test results from routes along (1) Interstate 75 between Florence, KY, and Lexington, KY, and (2) Interstates 74 and 70 east of Indianapolis, during which tests the startup collected power flow data from the vehicle's motor, generator, and battery, and three-dimensional position data from a GPS system. Based on these data, it was determined that the engine-driven generator provided an average of 15% more propulsive energy than required due to electrical losses in the drivetrain. Some of these losses occured in the power electronics, which are shown to be 82% - 92% efficient depending on power flow direction, but the battery showed significant signs of wear, accounting for the remainder of these electrical losses. Overall, most of the system's fuel savings came from its regenerative braking capability, which recaptured between 3% and 12% of the total drive energy output. Routes with significant grade changes maximize this energy recapture percentage, but it is shown minimizing drag and rolling resistance with a more modern truck and trailer could further increase this energy capture to between 8% and 18%.</div><div> </div><div> In the second application, an electrified air handling system is added to a 4.5L engine, allowing it to replace the 6.8L engine in John Deere's 644K hybrid wheel loader. Most of the fuel savings arise from downsizing the engine, so in this case an electrically driven supercharger (eBooster) allows the engine to meet the peak torque requirements of the larger, original engine. In this thesis, a control-oriented nonlinear state space model of the modified 4.5L engine is presented and linearized for use in designing a robust, multi-input multi-output (MIMO) controller which commands the engine's fueling rate, eBooster, eBooster bypass valve, exhaust gas recirculation (EGR) valve, and exhaust throttle. This integrated control strategy will ultimately allow superior tracking of engine speed, EGR fraction, and air-fuel ratio (AFR) targets, but these performance gains over independent single-input single-output control loops for each component demand linear models that accurately represent the engine's gas exchange dynamics. To address this, a physics-based model is presented and linearized to simulate pressures, temperatures, and shaft speeds based on sub-models for exhaust temperature, cylinder charge flow, valve flow, compressor flow, turbine flow, compressor power, and turbine power. The nonlinear model matches the truth reference engine model over the 1200 rpm - 2000 rpm and 100 Nm - 500 Nm speed and torque envelope of interest within 10% in steady state and 20% in transient conditions. Two linear models represent the full engine's dynamics over this speed and torque range, and these models match the truth reference model within 20% in the middle of the operating envelope. However, specifically at (1) low load for any speed and (2) high load at high speed, the linear models diverge from the nonlinear and truth reference models due to nonlinear engine dynamics lost in linearization. Nevertheless, these discrepancies at the edges of the engine's operating envelope are acceptable for control design, and if greater accuracy is needed, additional linear models can be generated to capture the engine's dynamics in this region.</div>

<div>In this thesis work, a novel approach for the design of a fractional order proportional integral</div><div>derivative (FOPID) controller is proposed. This design introduces a new time-varying FOPID controller</div><div>to mitigate a voltage spike at the controller output whenever a sudden change to the setpoint occurs. The</div><div>voltage spike exists at the output of the proportional integral derivative (PID) and FOPID controllers when a</div><div>derivative control element is involved. Such a voltage spike may cause a serious damage to the plant if it is</div><div>left uncontrolled. The proposed new FOPID controller applies a time function to force the derivative gain to</div><div>take effect gradually, leading to a time-varying derivative FOPID (TVD-FOPID) controller, which maintains</div><div>a fast system response and signi?cantly reduces the voltage spike at the controller output. The time-varying</div><div>FOPID controller is optimally designed using the particle swarm optimization (PSO) or genetic algorithm</div><div>(GA) to ?nd the optimum constants and time-varying parameters. The improved control performance is</div><div>validated through controlling the closed-loop DC motor speed via comparisons between the TVD-FOPID</div><div>controller, traditional FOPID controller, and time-varying FOPID (TV-FOPID) controller which is created</div><div>for comparison with all three PID gain constants replaced by the optimized time functions. The simulation</div><div>results demonstrate that the proposed TVD-FOPID controller not only can achieve 80% reduction of voltage</div><div>spike at the controller output but also is also able to keep approximately the same characteristics of the system</div><div>response in comparison with the regular FOPID controller. The TVD-FOPID controller using a saturation</div><div>block between the controller output and the plant still performs best according to system overshoot, rise time,</div><div>and settling time.</div>

<div>In this thesis, we explore different methods to enhance the safety and robustness for autonomous systems. We achieve this goal using concepts and tools from reachability analysis and control barrier functions. We first take on a multi-player reach-avoid game that involves two teams of players with competing objectives, namely the attackers and the defenders. We analyze the problem and solve the game from the attackers' perspectives via a moving horizon approach. The resulting solution provides a safety guarantee that allows attackers to reach their goals while avoiding all defenders. </div><div><br></div><div>Next, we approach the problem of target re-association after long-term occlusion using concepts from reachability as well as Bayesian inference. Here, we set out to find the probability identity matrix that associates the identities of targets before and after an occlusion. The solution of this problem can be used in conjunction with existing state-of-the-art trackers to enhance their robustness.</div><div><br></div><div>Finally, we turn our attention to a different method for providing safety guarantees, namely control barrier functions. Since the existence of a control barrier function implies the safety of a control system, we propose a framework to learn such function from a given user-specified safety requirement. The learned CBF can be applied on top of an existing nominal controller to provide safety guarantees for systems.</div>

<div>Stoichiometric air-fuel ratio (AFR) and air/EGR flow control are essential control problems in today’s advanced spark-ignited (SI) engines to enable effective application of the three-way-catalyst (TWC) and generation of required torque. External exhaust gas recirculation (EGR) can be used in SI engines to help mitigate knock, reduce enrichment and improve efficiency[1 ]. However, the introduction of the EGR system increases the complexity of stoichiometric engine-out lambda and torque management, particularly for high BMEP commercial vehicle applications. This thesis develops advanced frameworks for sensing and control architecture designs to enable robust air handling system management, stoichiometric cylinder air-fuel ratio (AFR) control and three-way-catalyst emission control.</div><div><br></div><div><div>The first work in this thesis derives a physically-based, control-oriented model for turbocharged SI engines utilizing cooled EGR and flexible VVA systems. The model includes the impacts of modulation to any combination of 11 actuators, including the throttle valve, bypass valve, fuel injection rate, waste-gate, high-pressure (HP) EGR, low-pressure (LP) EGR, number of firing cylinders, intake and exhaust valve opening and closing timings. A new cylinder-out gas composition estimation method, based on the inputs’ information of cylinder charge flow, injected fuel amount, residual gas mass and intake gas compositions, is proposed in this model. This method can be implemented in the control-oriented model as a critical input for estimating the exhaust manifold gas compositions. A new flow-based turbine-out pressure modeling strategy is also proposed in this thesis as a necessary input to estimate the LP EGR flow rate. Incorporated with these two sub-models, the control-oriented model is capable to capture the dynamics of pressure, temperature and gas compositions in manifolds and the cylinder. Thirteen physical parameters, including intake, boost and exhaust manifolds’ pressures, temperatures, unburnt and burnt mass fractions as well as the turbocharger speed, are defined as state variables. The outputs such as flow rates and AFR are modeled as functions of selected states and inputs. The control-oriented model is validated with a high fidelity SI engine GT-Power model for different operating conditions. The novelty in this physical modeling work includes the development and incorporation of the cylinder-out gas composition estimation method and the turbine-out pressure model in the control-oriented model.</div></div><div><br></div><div><div>The second part of the work outlines a novel sensor selection and observer design algorithm for linear time-invariant systems with both process and measurement noise based on <i>H</i>2 optimization to optimize the tradeoff between the observer error and the number of required sensors. The optimization problem is relaxed to a sequence of convex optimization problems that minimize the cost function consisting of the <i>H</i>2 norm of the observer error and the weighted <i>l</i>1 norm of the observer gain. An LMI formulation allows for efficient solution via semi-definite programing. The approach is applied here, for the first time, to a turbo-charged spark-ignited (SI) engine using exhaust gas recirculation to determine the optimal sensor sets for real-time intake manifold burnt gas mass fraction estimation. Simulation with the candidate estimator embedded in a high fidelity engine GT-Power model demonstrates that the optimal sensor sets selected using this algorithm have the best <i>H</i>2 estimation performance. Sensor redundancy is also analyzed based on the algorithm results. This algorithm is applicable for any type of modern internal combustion engines to reduce system design time and experimental efforts typically required for selecting optimal sensor sets.</div></div><div><br></div><div><div>The third study develops a model-based sensor selection and controller design framework for robust control of air-fuel-ratio (AFR), air flow and EGR flow for turbocharged stoichiometric engines using low pressure EGR, waste-gate turbo-charging, intake throttling and variable valve timing. Model uncertainties, disturbances, transport delays, sensor and actuator characteristics are considered in this framework. Based on the required control performance and candidate sensor sets, the framework synthesizes an H1 feedback controller and evaluates the viability of the candidate sensor set through analysis of the structured</div><div>singular value μ of the closed-loop system in the frequency domain. The framework can also be used to understand if relaxing the controller performance requirements enables the use of a simpler (less costly) sensor set. The sensor selection and controller co-design approach is applied here, for the first time, to turbo-charged engines using exhaust gas circulation. High fidelity GT-Power simulations are used to validate the approach. The novelty of the work in this part can be summarized as follows: (1) A novel control strategy is proposed for the stoichiometric SI engines using low pressure EGR to simultaneously satisfy both the AFR and air/EGR-path control performance requirements; (2) A parametrical method to simultaneously select the sensors and design the controller is first proposed for the internal combustion engines.</div></div><div><br></div><div><div>In the fourth part of the work, a novel two-loop estimation and control strategy is proposed to reduce the emission of the three-way-catalyst (TWC). In the outer loop, an FOS estimator consisting of a TWC model and an extended Kalman-filter is used to estimate the current TWC fractional oxygen state (FOS) and a robust controller is used to control the TWC FOS by manipulating the desired engine λ. The outer loop estimator and controller are combined with an existing inner loop controller. The inner loop controller controls the engine λ based on the desired λ value and the control inaccuracies are considered and compensated by the outer loop robust controller. This control strategy achieves good emission reduction performance and has advantages over the constant λ control strategy and the conventional two-loop switch-type control strategy.</div></div>

<div> <div> <div> <p>Commercial vehicles in the United States account for a significant fraction of greenhouse gas emissions and NOx emissions. The objectives of this work are reduction in commercial vehicle NOx emissions through enhanced aftertreatment thermal management via diesel engine variable valve actuation and the reduction of commercial vehicle fuel consumption/GHG emissions by enabling more effective class 8 truck platooning. </p> <p><br></p><p>First, a novel diesel engine aftertreatment thermal management strategy is proposed which utilizes a 2-stroke breathing variable value actuation strategy to increase the mass flow rate of exhaust gas. Experiments showed that when allowed to operate with modestly higher engine-out emissions, temperatures comparable to baseline could be achieved with a 1.75x exhaust mass flow rate, which could be beneficial for heating the SCR catalyst in a cold-start scenario. </p> <p><br></p><p>Second, a methodology is presented for characterizing aerodynamic drag coefficients of platooning trucks using experimental track-test data, which allowed for the development of high-fidelity platoon simulations and thereby enabled rapid development of advanced platoon controllers. Single truck and platoon drag coefficients were calculated for late model year Peterbilt 579’s based on experimental data collected during J1321 fuel economy tests for a two-truck platoon at 65 mph with a 55’ truck gap. Results show drag coefficients of 0.53, 0.50, and 0.45 for a single truck, a platoon front truck, and a platoon rear truck, respectively. </p> <p><br></p><p>Finally, a PID-based platoon controller is presented for maximizing fuel savings and gap control on hilly terrain using a dynamically-variable platoon gap. The controller was vetted in simulation and demonstrated on a vehicle in closed-course functionality testing. Simulations show that the controller is capable of 6-9% rear truck fuel savings on a heavily-graded route compared to a production-intent platoon controller, while increasing control over the truck gap to discourage other vehicles from cutting in. </p></div></div></div>

<p>Stringent regulatory requirements and modern diesel engine technologies have engaged automotive manufacturers and researchers in accurately predicting and controlling diesel engine-out emissions. As a result, engine control systems have become more complex and opaquer, increasing the development time and costs. To address this challenge, Model-based control methods are an effective way to deal with the criticality of the system study and controls. And physics-based combustion engine modeling is a key to achieve it. This thesis focuses on development and validation of a physics-based model for both engine and emissions using model-based design tools from MATLAB & Simulink. Engine model equipped with exhaust gas circulation and variable geometry turbine is adopted from the previously done work which was then integrated with the combustion and emission model that predicts the heat release rates and NO_xemission from engine. Combustion model is designed based on the mass fraction burnt from CA10 to CA90 and then NO_xpredicted using the extended Zeldovich mechanism. The engine models are tuned for both steady state and dynamics test points to account for engine operating range from the performance data. Various engine and combustion parameters are estimated using parameter estimation toolbox from MATLAB and Simulink by applying least squared solver to minimize the error between measured and estimated variables. This model is validated against the virtual engine model developed in GT-power for Cummins 6.7L turbo diesel engine. To account the harmonization of the testing cycles to save engine development time globally, a world harmonized stationary cycle (WHSC) is used for the validation. Sub-systems are validated individually as well as in loop with a complete model for WHSC. Engine model validation showed promising accuracy of more than 88.4 percent in average for the desired parameters required for the NO_xprediction. NO_x estimation is accurate for the cycle except warm up and cool down phase. However, NO_xprediction during these phases is limited due to actual NO_xmeasured data for tuning the model for real time NO_xestimation. Results are summarized at the end to compare the trend of NO_xestimation from the developed combustion and emission model to show the accuracy of in-cylinder parameters and required for the NO_x estimation. </p>

<div>The recent progress of machine learning, driven by pervasive data and increasing computational power, has shown its potential to achieve higher robot autonomy. Yet, with too much focus on generic models and data-driven paradigms while ignoring inherent structures of control systems and tasks, existing machine learning methods typically suffer from data and computation inefficiency, hindering their public deployment onto general real-world robots. In this thesis work, we claim that the efficiency of autonomous robot learning can be boosted by two strategies. One is to incorporate the structures of optimal control theory into control-objective learning, and this leads to a series of control-induced learning methods that enjoy the complementary benefits of machine learning for higher algorithm autonomy and control theory for higher algorithm efficiency. The other is to integrate necessary human guidance into task and control objective learning, leading to a series of paradigms for robot learning with minimal human guidance on the loop.</div><div><br></div><div>The first part of this thesis focuses on the control-induced learning, where we have made two contributions. One is a set of new methods for inverse optimal control, which address three existing challenges in control objective learning: learning from minimal data, learning time-varying objective functions, and learning under distributed settings. The second is a Pontryagin Differentiable Programming methodology, which bridges the concepts of optimal control theory, deep learning, and backpropagation, and provides a unified end-to-end learning framework to solve a broad range of learning and control tasks, including inverse reinforcement learning, neural ODEs, system identification, model-based reinforcement learning, and motion planning, with data- and computation- efficient performance.</div><div><br></div><div>The second part of this thesis focuses on the paradigms for robot learning with necessary human guidance on the loop. We have made two contributions. The first is an approach of learning from sparse demonstrations, which allows a robot to learn its control objective function only from human-specified sparse waypoints given in the observation (task) space; and the second is an approach of learning from</div><div>human’s directional corrections, which enables a robot to incrementally learn its control objective, with guaranteed learning convergence, from human’s directional correction feedback while it is acting.</div><div><br></div>

The oil and gas industry faces a lack of compact drilling devices capable of performing horizontal drilling maneuvers in depleted or abandoned wells in order to enhance oil recovery. The purpose of this project was to design and develop a smart sensing system that can be later implemented in compact drilling devices used to perform horizontal drilling to enhance oil recovery in wells. A smart sensor is the combination of a sensing element (sensor) and a microprocessor. Hence, a smart sensing system is an arrangement that consists of different sensors, where one or more have smart capabilities. The sensing system was built and tested in a laboratory setting. For this, a test bench was used as a case study to simulate the operation from a micro-drilling device. The smart sensing system integrated the sensors essential for the direct operational measurements required for the robot. The focus was on selecting reliable and sturdy components that can handle the operation Down the Hole (DTH) on the final lateral micro-drilling robot. The sensing system's recorded data was sent to a microcontroller, where it was processed and then presented visually to the operator through a User Interface (UI) developed in a cloud-based framework. The information was filtered, processed, and sent to a controller that executed commands and sent signals to the test bench’s actuators. The smart sensing system included novel modules and sensors suitable for the operation in a harsh environment such as the one faced in the drilling process. Furthermore, it was designed as an independent, flexible module that can be implemented in test benches with different settings and early robotic prototypes. The outcome of this project was a sensing system able to provide robotic drilling devices with flexibility while providing accurate and reliable measurements during their operation.

<p dir="ltr">This project aimed to develop an instrumentation and control system for a micro-drilling robot based on Industrial Internet of Things (IIoT) technologies. The automation system integrated IIoT technological tools to create a robust automation system capable of being used in drilling operations. The system incorporated industrial-grade sensors, which carried out direct measurements of the critical variables of the process. The indirect variables relevant to the control of the robot were calculated from the measured parameters. The system also considered the telemetry architecture necessary to reliably transmit data from the down-the-hole (DTH) robot to a receiver on the surface. Telemetry was based on wireless communication through long-range radio frequency (LoRa). The system developed had models based on Artificial Intelligence (AI) and Machine Learning (ML) for determining the mode of operation, detecting changes in the process, and changes in drilling variables in critical hydraulic components for the drilling process. Algorithms based on AI and ML models also allowed the user to make better decisions based on the variables' correlation to optimize the drilling process (e.g., dynamic change of flow, pressure, and RPMs based on automatic rock identification). A user interface (UI) was developed, and digital tools to perform data analysis were implemented. Safety assessment in all robot systems (e.g., electrical, hardware, software) was contemplated as a critical design component. The result of this research project provides innovative micro-drilling robots with the necessary technological tools to optimize the drilling process. The system made drilling more efficient, reliable, and safe, providing diagnostic and prognostic tools that allowed planning maintenance based on the actual health of the devices. The system that was developed was tested in a test bench under controlled conditions within a laboratory to characterize the system and collect data that allowed ML models' development, training, validation, and testing. The prototype of a micro-drilling robot installed on the test bench served as a case study to assess the implemented models' reliability and the proposed telemetry.</p>