Multidisciplinary design of a micro-USV for re-entry operations

Unmanned Space Vehicles (USV) are seen as a test-bed for enabling technologies and as a carrier to deliver and return experiments to and from low-Earth orbit. USV's are a potentially interesting solution also for the exploration of other planets or as long-range recognisance vehicles. As test bed, USV's are seen as a stepping stone for the development of future generation re-usable launchers but also as way to test key technologies for re-entry operations. Examples of recent developments are the PRORA-USV, designed by the Italian Aerospace Research Center (CIRA) in collaboration with Gavazzi Space, or the Boeing X-37B Orbital Test Vehicle (OTV), that is foreseen as an alternative to the space shuttle to deliver experiments into Earth orbit. Among the technologies to be demonstrated with the X-37 are improved thermal protection systems, avionics, the autonomous guidance system, and an advanced airfram

Adaptive Model Predictive Control for Engine-Driven Ducted Fan Lift Systems using an Associated Linear Parameter Varying Model

Ducted fan lift systems (DFLSs) powered by two-stroke aviation piston engines present a challenging control problem due to their complex multivariable dynamics. Current controllers for these systems typically rely on proportional-integral algorithms combined with data tables, which rely on accurate models and are not adaptive to handle time-varying dynamics or system uncertainties. This paper proposes a novel adaptive model predictive control (AMPC) strategy with an associated linear parameter varying (LPV) model for controlling the engine-driven DFLS. This LPV model is derived from a global network model, which is trained off-line with data obtained from a general mean value engine model for two-stroke aviation engines. Different network models, including multi-layer perceptron, Elman, and radial basis function (RBF), are evaluated and compared in this study. The results demonstrate that the RBF model exhibits higher prediction accuracy and robustness in the DFLS application. Based on the trained RBF model, the proposed AMPC approach constructs an associated network that directly outputs the LPV model parameters as an adaptive, robust, and efficient prediction model. The efficiency of the proposed approach is demonstrated through numerical simulations of a vertical take-off thrust preparation process for the DFLS. The simulation results indicate that the proposed AMPC method can effectively control the DFLS thrust with a relative error below 3.5%

Deep Reinforcement Learning Attitude Control of Fixed-Wing UAVs Using Proximal Policy Optimization

Contemporary autopilot systems for unmanned aerial vehicles (UAVs) are far more limited in their flight envelope as compared to experienced human pilots, thereby restricting the conditions UAVs can operate in and the types of missions they can accomplish autonomously. This paper proposes a deep reinforcement learning (DRL) controller to handle the nonlinear attitude control problem, enabling extended flight envelopes for fixed-wing UAVs. A proof-of-concept controller using the proximal policy optimization (PPO) algorithm is developed, and is shown to be capable of stabilizing a fixed-wing UAV from a large set of initial conditions to reference roll, pitch and airspeed values. The training process is outlined and key factors for its progression rate are considered, with the most important factor found to be limiting the number of variables in the observation vector, and including values for several previous time steps for these variables. The trained reinforcement learning (RL) controller is compared to a proportional-integral-derivative (PID) controller, and is found to converge in more cases than the PID controller, with comparable performance. Furthermore, the RL controller is shown to generalize well to unseen disturbances in the form of wind and turbulence, even in severe disturbance conditions.Comment: 11 pages, 3 figures, 2019 International Conference on Unmanned Aircraft Systems (ICUAS

A methodology for the validated design space exploration of fuel cell powered unmanned aerial vehicles

Unmanned Aerial Vehicles (UAVs) are the most dynamic growth sector of the aerospace industry today. The need to provide persistent intelligence, surveillance, and reconnaissance for military operations is driving the planned acquisition of over 5,000 UAVs over the next five years. The most pressing need is for quiet, small UAVs with endurance beyond what is capable with advanced batteries or small internal combustion propulsion systems. Fuel cell systems demonstrate high efficiency, high specific energy, low noise, low temperature operation, modularity, and rapid refuelability making them a promising enabler of the small, quiet, and persistent UAVs that military planners are seeking. Despite the perceived benefits, the actual near-term performance of fuel cell powered UAVs is unknown. Until the auto industry began spending billions of dollars in research, fuel cell systems were too heavy for useful flight applications. However, the last decade has seen rapid development with fuel cell gravimetric and volumetric power density nearly doubling every 2-3 years. As a result, a few design studies and demonstrator aircraft have appeared, but overall the design methodology and vehicles are still in their infancy. The design of fuel cell aircraft poses many challenges. Fuel cells differ fundamentally from combustion based propulsion in how they generate power and interact with other aircraft subsystems. As a result, traditional multidisciplinary analysis (MDA) codes are inappropriate. Building new MDAs is difficult since fuel cells are rapidly changing in design, and various competitive architectures exist for balance of plant, hydrogen storage, and all electric aircraft subsystems. In addition, fuel cell design and performance data is closely protected which makes validation difficult and uncertainty significant. Finally, low specific power and high volumes compared to traditional combustion based propulsion result in more highly constrained design spaces that are problematic for design space exploration. To begin addressing the current gaps in fuel cell aircraft development, a methodology has been developed to explore and characterize the near-term performance of fuel cell powered UAVs. The first step of the methodology is the development of a valid MDA. This is accomplished by using propagated uncertainty estimates to guide the decomposition of a MDA into key contributing analyses (CAs) that can be individually refined and validated to increase the overall accuracy of the MDA. To assist in MDA development, a flexible framework for simultaneously solving the CAs is specified. This enables the MDA to be easily adapted to changes in technology and the changes in data that occur throughout a design process. Various CAs that model a polymer electrolyte membrane fuel cell (PEMFC) UAV are developed, validated, and shown to be in agreement with hardware-in-the-loop simulations of a fully developed fuel cell propulsion system. After creating a valid MDA, the final step of the methodology is the synthesis of the MDA with an uncertainty propagation analysis, an optimization routine, and a chance constrained problem formulation. This synthesis allows an efficient calculation of the probabilistic constraint boundaries and Pareto frontiers that will govern the design space and influence design decisions relating to optimization and uncertainty mitigation. A key element of the methodology is uncertainty propagation. The methodology uses Systems Sensitivity Analysis (SSA) to estimate the uncertainty of key performance metrics due to uncertainties in design variables and uncertainties in the accuracy of the CAs. A summary of SSA is provided and key rules for properly decomposing a MDA for use with SSA are provided. Verification of SSA uncertainty estimates via Monte Carlo simulations is provided for both an example problem as well as a detailed MDA of a fuel cell UAV. Implementation of the methodology was performed on a small fuel cell UAV designed to carry a 2.2 kg payload with 24 hours of endurance. Uncertainty distributions for both design variables and the CAs were estimated based on experimental results and were found to dominate the design space. To reduce uncertainty and test the flexibility of the MDA framework, CAs were replaced with either empirical, or semi-empirical relationships during the optimization process. The final design was validated via a hardware-in-the loop simulation. Finally, the fuel cell UAV probabilistic design space was studied. A graphical representation of the design space was generated and the optima due to deterministic and probabilistic constraints were identified. The methodology was used to identify Pareto frontiers of the design space which were shown on contour plots of the design space. Unanticipated discontinuities of the Pareto fronts were observed as different constraints became active providing useful information on which to base design and development decisions.Ph.D.Committee Chair: Mavris, Dimitri; Committee Member: Nam, Taewoo; Committee Member: Parekh, David; Committee Member: Soban, Danielle; Committee Member: Volovoi, Vital

Study of Transit Bus Duty Cycle and its Influence on Fuel Economy and Emissions of Diesel-Electric Hybrids

The Center for Alternative Fuels, Engines, and Emissions (CAFEE) of West Virginia University (WVU) is developing the Integrated Bus Information System (IBIS), an information resource on transit bus emissions for vehicle procurement purposes. IBIS provides the transit bus industry with exhaust emissions information, including an emissions database, and predictive models for fuel economy (F.E.) and emissions. Inputs for the models are in the form of drive cycle metrics, but the knowledge of such metrics is not readily available for transit agencies.;The first part of this dissertation was an effort to close the gap between engineering drive cycle metrics and the information available to transit bus operators. In cooperation with WMATA Transit, an extensive evaluation to characterize transit bus operation was performed. This evaluation was based on GPS and ECU logs of diverse bus routes. Instantaneous speed and road grade were determined for all the routes. Transit operation was classified in four main service groups: Inner-City, Urban, Suburban, and Commuter. Characterizing transit bus operation played an important role because it defined the parameters, and their ranges, to be used in F.E. and emissions models.;The second part of the dissertation studied the effects that drive cycles have over emissions and F.E. of diesel-electric hybrid buses, focusing specifically in MY 2007--2009 diesel-electric serieshybrid 40\u27 transit buses. Using ANL\u27s PSAT, the hybrid bus was dynamically modeled and validated against chassis dynamometer test data. As part of the vehicle dynamic model, a model was developed for fuel consumption and NOx emissions of the Cummins ISB 260H diesel engine. The vehicle model was simulated over a variety of duty cycles assuming zero grade, producing a database of instantaneous fuel and NOx rates, with all tests satisfying SAE J2711\u27s restriction for state of charge.;A regression based method was devised for predicting cycle F.E., CO 2, and NOx, in which the inputs were average speed, percentage idle, and characteristic acceleration. Fuel consumption and NOx were broken into the idle and driving contributions. The driving portion was predicted with average speed without idle and characteristic acceleration without grade, and then aggregated with the idle contribution. The proposed approach produced excellent predictions with coefficients of determination of 0.96 for F.E., 0.99 for CO2, and 0.99 for NOx.;A tool was developed to allow transit agencies to place hybrid buses in routes that take the most advantage of the hybrid-electric capabilities and to evaluate emissions impacts in strategic planning and vehicle procurement. The selection of the best routes is based on fuel savings. Depending on the route, hybrid transit buses have the potential for saving between 0.5 and 1.2 gallons of fuel per hour per vehicle and 5 to 12 kg of CO2 per hour

Multi-fidelity surrogate-based optimal design of road vehicle suspension systems

Ride comfort is a relevant performance for road vehicles. The suspension system can filter vibration caused by the uneven road to improve ride comfort. Optimization of the road vehicle suspension system has been extensively studied. As detailed models require significant computational effort, it becomes increasingly important to develop an efficient optimization framework. In this work, a multi-fidelity surrogate-based optimization framework based on the Approximate Normal Constraint method and Extended Kernel Regression surrogate modeling method is proposed and applied. An analytical model and a multi-body model of the suspension system are used as the low-fidelity and high-fidelity models, respectively. Compared with other well-known methods, the proposed method can provide good accuracy and high efficiency. In addition, the proposed method is applied to different types of vehicle suspension optimization problems and shows good robustness and efficiency