Linear Parameter-Varying (LPV) models provide means to approximate complex, nonlinear, and time-varying system dynamics using a set of Linear Time-Invariant (LTI) models, interpolated by a scheduling function to ensure smooth transitions across the system’s operating envelope. This study demonstrates that multivariate simplex B-splines can serve as such function, evaluated for State-Space quasi-LPV (SS-qLPV) models by providing a global approximation using local basis functions. The Inverted Pendulum on a Cart Model (IPCM) is used as a demonstrator in an open-loop setting, with an affine LPV representation based on cart velocity and pendulum angle as scheduling parameters. Several scheduling function estimation methods: piecewise constant Zero-Order Hold (ZOH), polynomial uni and multi-variate Ordinary Least Squares (OLS), and multivariate simplex B-splines are evaluated. Results indicate that, at the same polynomial order, B-splines show higher approximation capabilities compared to polynomial methods, as shown by the root mean squared error (RMSE) of the residuals. However, under broader simulation conditions, LPV-ZOH can be computationally less expensive and can achieve lower RMSE, although piecewise constant methods have discontinuities at the switching points, which can have an impact to closed-loop performance. The study highlights trade-offs in scheduling
function selection and suggests future research in optimizing simplices for improved performance. Applying B-spline scheduling functions with gain scheduled controllers in closed-loop control is the next direction for increasing control performance in complex, high-dimensional systems.

The thesis, conducted in collaboration with INNIO Jenbacher GmbH & Co OG (INNIO), presents two investigations advancing industrial gas engine control systems. The first investigation develops and compares four control strategies for engine speed regulation: iterative Linear Quadratic Regulator (iLQR), pole placement, Nonlinear Dynamic Inversion (NDI), and Incremental Nonlinear Dynamic Inversion (INDI). Each controller was derived from INNIO nonlinear Jenbacher engine models and evaluated through simulation under consistent tuning methodology. The analysis emphasized multi-variable control coordination, cross-coupling effects, robustness against model mismatch, and practical implementation considerations for commercial deployment.
The second investigation establishes a comprehensive methodology for identifying and characterizing fuel transport delays using GT-SUITE/MATLAB co-simulation calibrated with Jenbacher engine data. Virtual sensors at multiple locations quantified delay behavior across load acceptance, rejection, and steady-state operating conditions. Quadratic models were developed to enable real-time delay prediction based on boost pressure and operating mode. Both investigations provide control-theoretic frameworks and empirical models directly applicable to INNIO’s current and future engine platforms.

Loss of Control In-flight (LOC-I) remains a leading cause of fatal accidents in commercial aviation, with aerodynamic stall identified as a frequent precursor. Effective Upset Prevention and Recovery Training (UPRT) requires representative Flight Simulation Training Devices (FSTDs), which in turn depend on accurate stall models. Current models are typically derived through in-flight system identification, but rely solely on global aircraft states and thus depend on assumptions about the onset and progression of flow separation. This study introduces a methodology to derive quantitative flow-separation maps from in-flight recordings of tufts and flow cones on the PH-LAB research aircraft during stall maneuvers.
High-speed video was analyzed through a dedicated image-processing pipeline to classify tuft states and reconstruct separation patterns. The resulting maps provide direct, spatially resolved evidence of stall onset and progression. This allows for a direct validation of existing Kirchhoff-inspired formulations, while highlighting their shortcomings in capturing spanwise variations and local effects of separation. Furthermore, synchronization with flight-test data allows for correlation of tuft measurements with global aircraft states and supports the development of a logistic model to describe local separation behavior. Beyond validating existing models, the proposed approach establishes a robust experimental framework for integrating flow visualization into stall model identification, with direct implications for the improvement of stall models and, ultimately, flight safety.

Loss of control inflight is the most common cause of fatal accidents in aviation. Aerodynamic stall models are utilized in pilot training to enhance safety and prevent accidents. This research presents an advanced longitudinal stall model for the Cessna Citation II, achieved through innovations in modeling methodologies and experimental design. By introducing dynamic stall maneuvers with step inputs, the study mitigated $\tau_1$ and $\tau_2$ parameter correlation, enabling more reliable parameter identification. A separable nonlinear least squares method significantly reduced computational time for nonlinear stall model estimation, decreasing it from hours to seconds. This approach revealed two minimally correlated flow separation states, offering deeper insights into wing flow characteristics and improving model accuracy. The lift model was refined to incorporate pitch rate and elevator deflection effects, while the drag model was enhanced with a lift-induced drag component. Additionally, a center of pressure model was derived from pitching moment data, advancing the understanding of stability during stall. A novel structure for characterizing degraded elevator control effectiveness was also developed. These advancements resulted in substantial performance improvements, with mean squared errors for lift, drag, and pitch moment coefficients reduced by 32\%, 29\%, and 27\%, respectively. The models also demonstrated greater consistency across diverse maneuvers, evidenced by reduced variability in $R^2$ values. This work contributes to more accurate stall modeling, enhancing both aerodynamic understanding and aviation safety.

One of the most widely applied identification methods for stall modelling from flight test data is based on Kirchoff’s method of flow separation. This approach has not lead to a satisfactory aerodynamic pitching moment model. The introduction of the so-called X-variable, representing the point of flow separation on the wing, interferes with identification of a pitch damping term, that is needed for dynamic stability. Moreover, flow separation is only a small contributor to the pitching moment, leading to a lack of physical interpretability. In general, Kirchoff methods lead to models incompatible with the nominal flight envelope. This paper presents a nonlinear unsteady model of the pitching moment using lag states of the angle of attack measurements, identified from flight test data collected with a Cessna Citation II. The model is formulated in terms of well-known stability derivatives and is a one-on-one extension of the nominal envelope model. Model regressors are selected from a large pool of candidates using Multivariate Orthogonal Function Modeling. The candidate pool is based on a newly formulated mathematical model, such that each model contribution has a clear physical interpretation. This has lead to a 𝐶𝑚𝛼 contribution depending on various lag states of the angle of attack, and pitch and downwash lag damping contributions as univariate splines. The model has good predictive abilities and can report a reduction of 55.9% in validation MSE compared to a Kirchoff based pitching moment model by van Ingen et al..

Loss-of-Control (LoC) is the primary cause of drone crashes, necessitating efficient onboard prevention systems that are effective in terms of sensor requirements, computing power, and memory. This study introduces a data-driven approach for detecting LoC in quadrotors, using Critical Slowing Down (CSD) theory as an Early Warning Signal (EWS) of approaching a critical transition. This paper employs a Fuzzy Logic Inference System (FLIS) to aggregate the CSD metrics alongside other EWS indicators, such as actuator phase delay, to provide a fuzzy indicator that quantifies the quadrotor’s stability. The proposed FLIS is applied to two LoC modes: the first is a yaw-induced LoC event during free-flight of the quadrotor in which growing off-axis instabilities during the maneuver culminate in LoC. The second is a roll-induced LoC event during a gimballed flight of the quadrotor in which growing off-axis instabilities during the maneuver also culminate in LoC. This approach proposes novel EWS indicators and a LoC detector and is generalizable across varying mass/size without needing precise state estimation of the quadrotor, instead only relying on onboard gyro and rotor speed data. Using real flight data from a GEPRO quadrotor, and a custom-built drone mounted on a 3-axis quadrotor gimbal testing rig, this paper demonstrates that various EWS indicators inferred with a FLIS can provide accurate, and timely detection of an upcoming LoC event, regardless of their specific causes or the maneuvers involved. This novel approach significantly enhances LoC detection rates relative to previous studies, and improves detection times, providing crucial additional seconds for corrective action.

Neglecting actuator dynamics in nonlinear control and control allocation can lead to performance degradation, especially when considering fast dynamic systems. This thesis provides a novel method to account for actuator dynamics in the control allocation solution, dynamic incremental nonlinear control allocation, or D-INCA. The incremental approach allows for the implementation of a first order discrete-time actuator dynamics model in the quadratic programming (QP) solver. This model is used to find the optimal command inputs in addition to the desired physical actuator deflections, hereby compensating for actuator dynamics delays. Whereas, the baseline incremental nonlinear control allocation (INCA) approach requires pseudo-control hedging of the outer loop reference to increase closed loop stability margins under actuator dynamics delays. To its advantage, D-INCA does not require feedback of higher order output derivatives than INCA and can be used with nonlinear non-control affine systems. Furthermore, with adaptive D-INCA, or AD-INCA, an actuator dynamics parameter estimator is introduced to adapt the actuator model online, minimizing actuator tracking errors after actuator failures. The proposed methods are applied to a fighter aircraft model with an over-actuated innovative control effectors suite and results are compared to the baseline INCA controller.

Aerodynamic stall has been a critical factor in recent aircraft crashes, leading to revisions in the regulations on the fidelity of stall models in flight simulation training devices. However, the updated regulations still lack a clearly defined accuracy required for effective pilot training. To determine the required accuracy, this research investigates how the Just Noticeable Difference (JND) thresholds for the stall abruptness parameter translate from a passive, observer task to an active flying task. An experiment was performed in the SIMONA Research Simulator with 16 active pilots, who performed two separate experiments. In one experiment, a stall autopilot flew the maneuver during which a staircase procedure was used to determine the passive JND threshold. The JND thresholds of a1 = 0.11 ± 0.094 found in this experiment were lower than the JND thresholds of a1 = 0.16 ± 0.14 found in a similar experiment by previous research. In the other experiment, the method of constant stimuli was used to determine the JND threshold for the active flying scenario. A psychometric curve, based on the Gaussian cumulative distribution function, was fitted using the combined responses of the participants. The resulting psychometric function of the active experiment lies entirely to the right of the passive psychometric function, and, when comparing the 75% thresholds for both experiments, the active threshold was found to be five times higher than the passive threshold. This indicates a decreased sensitivity to changes in stall abruptness when pilots are flying a stall themselves.

This research evaluates various linear quadratic control techniques, with a particular focus on loop transfer recovery methods, to enhance the safety and robustness of flight control systems. It aims to address the limitations of classical control strategies in managing complex, multivariable systems by implementing advanced recovery mechanisms. The study utilizes a simulation model of the Cessna Citation PH-LAB aircraft to explore the effectiveness of linear quadratic regulator (LQR), linear quadratic Gaussian (LQG), and loop transfer recovery (LTR) control methods. It examines their time and frequency-domain characteristics, robustness against uncertainties, and performance in tracking normal acceleration commands. The research identifies the strengths and weaknesses of each method, contributing to the development of more reliable and efficient control systems for aviation.

The rise in high-speed quadrotor applications demands aerodynamic models that explain the flight characteristics at these broader flight regimes. For developing these models using system identification methods, having information-rich data at these flight conditions is crucial. While there have been sufficient works in the input design for identification procedures in quadrotors, there are limited guidelines for high-speed flights. This work focuses on two key contributions to aid the high-speed quadrotor model identification process. First, an automated flight testing pipeline using various software tools has been developed to support the iterative input design process. Second, various inputs such as position tracking, trajectory tracking, and attitude injection while trajectory tracking have been designed. The goal of these input designs was to push the quadrotor velocities while trying to maximize the excitation in forces and moments. The HITL simulation tool, being part of the testing pipeline, was used to iteratively design and verify inputs before actual flight tests. Finally, automated high-speed flight tests of the designed inputs were conducted at the Cyberzoo indoor facility, reaching maximum velocities of up to 7 m/s. The results show that attitude injection while trajectory tracking provides a method to excite forces and moments during high-speed flights. This work is an initial attempt at understanding the effects of input design at high-speed flights with the goal of obtaining data for quadrotor model identification. The methodology and pipeline developed here serve as a platform to build and explore various other identification inputs with the scope to push the quadrotor flight speeds even further.

This research presents a novel method to define the Barrier Function (BF) used for the synthesis of a Control Barrier Function (CBF). The proposed method uses multivariate B-spline estimators as the BF which allows for the creation of a more data-driven CBF. Additionally usage of B-splines offer a local basis, linearity in parameters and allow for additional constraints to be added to the regression problem. New theory is derived in order to utilize the power of multivariate B-splines for the creation of CBFs. The proposed methodology is implemented on Dubin's car. In the experiments two distinct multivariate B-splines are used as BFs. The first of these is created based on data sampled from a more conventional continuous reference function. The second one is based on data sampled from a reference function which is based on a combination of step functions. Numerical simulations using the created multivariate B-splines as BFs show that the the proposed methodology can be used to create a functional CBF. This is under the condition that the used multivariate B-spline has a degree of continuity equal to the relative degree of the system state which is to be controlled. In addition, the spline BFs estimated from discontinuous data show that the proposed methodology offers significant flexibility with regards to the types of safe sets which can be turned into functional CBFs using the proposed method.

In recent years, quadrotors have emerged as versatile aerial vehicles, serving both recreational and critical industrial purposes in fields such as surveillance, search and rescue, agriculture, and transportation. However, ensuring their reliability and safety in the presence of operational faults or disturbances remains a significant challenge. This research aims to address these concerns by developing a robust controller that integrates Incremental Nonlinear Dynamic Inversion (INDI) with H-infinity loop shaping design process (LSDP), resulting in enhanced disturbance rejection and improved robustness to model uncertainty. A nonlinear aerodynamic model, based on flight test data, is used to enhance the fidelity of the quadrotor’s motion equations. The proposed controller is tested and compared with a traditional PD controller. Simulations of attitude control were conducted under different compensators to evaluate the impact of various robust control strategies, particularly in terms of disturbance rejection when combined with the INDI implementation in a 6-DOF nonlinear quadrotor simulator. Results demonstrate the superior disturbance rejection capabilities of the robust controlled system, highlighting the effectiveness of the proposed control strategy in improving performance and reliability under uncertain conditions.

In this article a novel Control Barrier Function (CBF) named the fault tolerant Control Barrier Function (ftCBF) is introduced. The ftCBF is able to keep a vehicle within a predefined safe set with changing control bounds and changing system dynamics. The ftCBF is shown to be feasible in fault tolerant control applications, as opposed to existing CBF methods. This novel constraint is tested on a double integrator system, and on a non-linear Dubin’s Car system with changing system dynamics and changing control bounds. In the simulations it is shown that the ftCBF is able to keep the vehicle in the safe set with failure events occurring at any place in the timeline. The ftCBF contains design parameters that allow a trade-off between safety and performance.

Digital fly-by-wire (FBW) control technology has had - and continuous to make - a great impact on modern-day aviation. In particular, it enables stability and control augmentation of the dynamic characteristics of the bare airframe and brings opportunities for substantial automation of tasks related to flight. At the heart of FBW control technology are the control laws, which are embedded in a flight control computer. The so-called divide-and-conquer paradigm long prevailed in the development of flight control laws, which is based on trim-and-linearization of the nonlinear dynamics over a wide range of conditions over the flight envelope. This process enables the use of powerful Linear Time-Invariant (LTI) control design and synthesis tools. Gain scheduling is performed in the subsequent nonlinear implementation step. This is a critical task, for which different techniques can be used to arrive at successful designs. However, the process can be intensive and time-consuming, especially in the case of highly nonlinear aircraft.

Nonlinear Dynamic Inversion (NDI) was developed as an alternative to the divide-and-conquer strategy. Instead of subdividing the operating domain into many different local regions, NDI brings the notional benefit of automatic gain scheduling. This drastically simplifies the nonlinear implementation step. Moreover, as it enables a decoupling of different parts of the control design, NDI brings advantages in terms of design modularity. In its classical form, these aspects are achieved through the use of an on-board model embedded in the control law. Alternatively, in an effort to reduce this model-dependency, a sensor-based incremental variant of NDI (INDI) was proposed in the past. This form aims to increase control law robustness in the face of parametric modeling offsets by relying more directly on sensor measurements instead. Accordingly, different inversion strategies (model-based, sensor-based, or combinations of these) lead to vastly different robust stability and performance characteristics. However, a systematic understanding of these robustness implications has long been missing. In this thesis, this problem is approached using H_∞-based multivariable analysis and synthesis techniques.

In addition to the question of robustness, this thesis also focuses on multi-objective control design in the context of control allocation for input-redundant plants. This concerns over-determined control problems, for which secondary performance criteria can be addressed in addition to the primary motion control task. In particular, it is investigated how the framework of incremental control allocation (INCA) can be used in such multi-objective design scenarios.

Unsteady numerical simulation has been proven to be an essential tool for research. The quality of the results can be improved by using mesh adaptation. Mesh adaptation uses error indicators to refine the mesh in regions with high errors. The error indicators used are output errors with the most accurate output error estimation method being adjoint-based error estimation. However, for this method, the primal solution needs to be stored, which is storage intensive, especially for large unsteady simulations. The method proposed in this thesis uses a neural network autoencoder to compress and reconstruct the primal solution. This solution is compared to a reconstructed solution using Proper Orthogonal Decomposition (POD).

The one-dimensional unsteady Burgers equation is used as validation for the methods using a manufactured solution while the lid-driven cavity flow is investigated using the proposed method. The manufactured solution of the one-dimensional Burgers case could be exactly reconstructed using two POD modes. For the autoencoder a small latent space was used. For low resolutions, the small latent space did not prove to be a problem as the primal and residual could be captured accurately. However, for higher resolutions, the reconstruction error of the autoencoder became dominant for the residuals and resulted in erroneous adjoint-based error estimates while the primal remained qualitatively similar.

For the lid-driven cavity flow, the POD was still able to capture the solution using a low number of modes due to the smoothness of the solution. This resulted in an unfair comparison between the POD and autoencoder reconstructed solutions. The reconstructed autoencoder error estimates for lower resolutions were more accurate due to the latent space being large enough to capture the residual of the discrete primal accurately enough. When moving to higher resolutions, the autoencoder was not able to reconstruct the residual accurately enough leading to erroneous error estimates. Therefore, the latent space of autoencoders should be sufficiently large in order to gain an accurate reconstruction of the residual. If the latent space is large enough, the error estimate is accurate and the local error estimates can be used as a first iteration error indicator for mesh refinement.

Adequate modeling of the unsteady aerodynamics during flow separation is critical for effective pilot training in Flight Simulation Training Devices. Over the years, a stall modeling method rooted in Kirchhoff's theory of flow separation has gained popularity due to its relative simplicity and suitability for parameter identification from flight data. This method describes the lift using a single internal flow separation point variable. A major drawback of Kirchhoff's method comes from the one-dimensionality of the flow separation point, limiting the representation of asymmetric flow separation. The goal of this work is to improve the existing Cessna Citation II dynamic stall model fidelity by applying Kirchhoff's method for each wing surface, separately. The main contribution is the identification of asymmetric flow separation development, using the flight-derived roll moment and a roll moment model based on the differential flow separation between the wing surfaces. Transformations of the flow separation variables were chosen by a Multivariate Orthogonal Functions selection algorithm to capture the stall-related nonlinearities of the roll moment, yaw moment, and lateral force. The longitudinal model structures are adopted from the existing, validated baseline stall model. The lateral-directional model outputs are in good agreement with the validation flight data, showing an average reduction of 48\% in Mean Squared Error (MSE) compared to the baseline stall model. In contrast, the longitudinal model output results in an average MSE increase of 88\%, suggesting that the estimated asymmetric flow separation parameters are unsuitable for longitudinal stall modeling. A promising way to incorporate the benefits of the proposed method is suggested, by adopting a hybrid approach that combines separate sets of flow separation parameters --- symmetric and asymmetric variants --- for the longitudinal and lateral-directional models, respectively.

Having a reliable flight envelope is of paramount importance for safe flight operations. Parameters that determine the safe flight envelope are airspeed , angle of attack, angle of sideslip, Euler angle and load factor. These parameters need to be fault redundant, In order to address this challenge, an unscented Kalman estimation routine is proposed involving a novel kinematic model which incorporates the effects of the turbulence. An approach in which two unscented Kalman Filter (UKF) filters operate simultaneously, one with additive faults Augmented Fault Filter (AF) and the other with non-additive faults Sensor Noise and Bias Filter (SNBF). The reasoning behind this approach is that the AF filter has the fault estimated state as the additive fault to the state estimation, which would give an accurate fault redundant sensors data estimate. To address the issue where sensor noise and biases cannot be distinguished from faults occurring in the sensor in the case of a more realistic fault, the SNBF filter is used. The proposed approach is validated with the data generated by Citation-550 Simulator 2023. This approach requires the sensor data from pitot tube, angle of attack vanes, angle of sideslip vanes, roll angle, pitch angle and yaw angle data from the Inertial Navigation System (INS) and for the kinematic equation the Inertial Measurement Unit (IMU) data (Accelerometer / Gyroscope) are used to perform the estimation. This approach can produce a fault redundant estimate of all the 6 sensor states, and in this work airspeed fault detection is the main focus. The approach is valid within the entire flight envelope, and there is no need to design a linear parameter- varying system. Through this approach, airspeed fault detection is performed and a fault redundant airspeed is estimated. The Augmented Fault UKF is found to achieve an unbiased state estimation even in the presence of unknown disturbances.

Loss of control (LOC) is the primary cause of failure of Unmanned Aerial Vehicles (UAV). The safety of these systems can be largely improved by facilitating techniques to prevent LOC to occur, such as Flight Envelope Protection, enabling controllers to keep the system within the Safe Flight Envelope (SFE).
The aim of this work is to examine the behaviour of the global SFE of a quadcopter subjected to varying system dynamics, including the effects of longitudinal center of gravity displacements and actuator dynamics.
The analysis has been split into the forward reachable set (FRS) and the backward reachable set (BRS). The FRS is estimated through an optimized Monte-Carlo (MC) simulation approach. Verification shows that the system specific optimized MC simulation approximates the true reachable sets with high accuracy, while exceeding performance on both accuracy as well as computation time compared to the Level-Set method.
The BRS is derived from the FRS directly using a minimum-time optimal control (MTOC) routine including actuator dynamics. This approach guarantees that the BRS is contained within the FRS and bypasses the need to simulate the dynamics backwards in time. Both methods exploit the control affine system structure from which it can be derived that the MTOC for both the FRS and BRS is bang-bang control, which drastically reduces the sampling space and optimal control complexity.
The results show that both the location of the centroid of the FRS and the return time distribution of the BRS are a function of the offset position. A large decrease in the FRS area is seen for larger center of gravity offset positions. Furthermore, the actuator dynamics reduce the FRS by 85%, irrespective of center of gravity location, while the BRS without actuator modelling shows impractical return times as a result of unfeasible instantaneous rotor speed changes.
A novel experimental validation procedure on the quadcopter FRS has been performed. The results show a general overestimation with respect to the flight data, which is expected when comparing an open-loop simulation with closed-loop performed flight maneuvers.

Upon rotor loss, fault-tolerant quadrotors are subjected to a severe restriction of the set of attainable virtual controls, making them vulnerable to loss of control at high speed flight. Due to the high nonlinearity of the reduced attitude controller and incremental nature of the fault-tolerant controller, determination of the safe flight envelope is exceedingly complex. To prevent loss-of-control, a novel method is presented to detect stability degradation due to actuator saturation that can provide an early warning of the onset of loss-of-control. This research presents an analysis of the effects of actuator dynamics and rotor saturation on the stability of fault-tolerant quadrotor control. Expanding on this analysis, a method is presented to predict loss-of-control, prior to its occurrence.