Loss of control is considered as the primary cause of fatal accidents in aviation, which occurs when the aircraft has left the safe flight envelope. To reduce loss-of-control-related accidents, it is important to estimate the safe flight envelope at the current flight condition and integrate it into flight control system design. This task is known as envelope estimation and protection. This project investigates this task on the Innovative Control Effectors aircraft, an over-actuated tailless fighter aircraft with complex aerodynamic coupling between control effectors. It has been observed that this aircraft can easily steer outside the flight envelope and lose control due to its huge control authority. This thesis proposes a novel and practical framework for safe flight envelope estimation and protection, in order to reduce loss-of-control-related accidents. Despite that multiple envelope estimation methods exist in literature, conventional analytical estimation methods fail to function efficiently for systems with high dimensionality and complex dynamics, which is often the case for high-fidelity aircraft models. In this way, this paper develops a probabilistic envelope estimation method based on Monte Carlo simulation. This method generates a probabilistic estimation of the flight envelope with kernel density estimation by simulating a sample of flight trajectories with extreme control effectiveness, which describes the envelope more practically with fuzzy sets instead of conventional crisp sets. It is shown that this method can significantly reduce the computational load compared with previous optimization-based methods and guarantee feasible and conservative envelope estimation of no less than seven dimensions. This method was applied to the Innovative Control Effectors aircraft developed by Lockheed Martin. The estimation results are demonstrated by comparing different flight conditions and covariance analysis. The estimated probabilistic flight envelope is used for online envelope protection by a database approach, which estimates the flight envelope offline and carries the results onboard for protection. Both a conventional state-constraint-based and a novel predictive probabilistic flight envelope protection systems were implemented on a multi-loop nonlinear dynamic inversion controller by extending the concept of pseudo control hedging. No systematic framework was available to apply envelope protection to such controller. Real-time simulation results prove that the proposed framework can protect the aircraft within the estimated envelope and save the aircraft from maneuvers that otherwise would result in loss of control. Possibilities were also explored to employ parametric models in envelope protection to simplify the database. This work, however, is still limited to offline estimation with open-loop commands. Future work can extend this framework to aircraft damage models and closed-loop commands.

Flight control of the DelFly is challenging, because of its complex dynamics and variability due to manufacturing inconsistencies. Machine Learning algorithms can be used to tackle these challenges. A Policy Gradient algorithm is used to tune the gains of a Proportional-Integral controller using Reinforcement Learning. Furthermore, a novel Classification Algorithm for Machine Learning control (CAML) is presented, which uses model identification and a neural network classifier to select from several predefined gain sets. The algorithms show comparable performance when considering variability only, but the Policy Gradient algorithm is more robust to noise, disturbances, nonlinearities and flapping motion.

To solve the problem of optimal control for nonlinear system, Actor Critic Designs (ACD) can be utilized which use the concept of Reinforcement learning (RL) and function approximators such as Neural networks (NN). Traditional ACD methods require a model NN that needs to be trained offline. Recently, research focus has been shifted to model-free approaches that do not require any model information beforehand and can be applied for online control. This thesis furthers the online methods in ACD by developing Incremental Model based Action Dependent Dual Heuristic Programming (IADDHP). In IADDHP, local system dynamics is identified online which does not require any priori knowledge about the system thus making it essentially ‘model-free’. Experiments are performed using missile model for reference tracking control and the results show that the IADDHP is capable of finding near-optimal control policy for the tasks with noise and system failure. It also outperforms the already existing model-free ADDHP which uses finite difference method (FDM) and has advantage over it in failure detection and adaptation. Being a model-free approach, IADDHP should be applicable for reference tracking control of any system.

This thesis presents a new control philosophy based on Direct Force Control (DFC) for over-actuated aircraft. It is predicated on simultaneously controlling all six Degrees of Freedom (DoF). The Innovative Control Effectors (ICE) aircraft with its thirteen-part control effector suite is the subject of study. Active-Set (A-S) based Incremental Nonlinear Control Allocation (INCA) studied in previous work is extended and adapted to suit 6DoF incremental control commands. Translational inner loops were designed and connected to various outer loops. DFC provided substantial increases in tracking performance for air-to-air refueling for steady straight and steady turns with varying degrees of turbulence. A hybrid DFC strategy for vertical control paired with lateral control through roll showed stability and controllability improvements for high crosswind landings.

In this paper a range-based relative localization solution is proposed and demonstrated in practice. The approach is based on wireless range measurements between robots, along with the communication of their velocities, accelerations, yaw rates, and height. It can be implemented on many robotic platforms without the need for dedicated sensors. With respect to previous work, we remove the dependency on a common heading reference between robots. The main advantage of this is that it makes the relative localization approach independent of magnetometer readings, which are notoriously unreliable in an indoor environment. A theoretical observability analysis shows that it may also have two disadvantages: the motion of the robots must meet more stringent conditions and the relative localization method becomes more susceptible to noise on the range measurements. However, simulation results have shown that in the presence of significant magnetic disturbances that are common to indoor environments, removing the heading dependency is beneficial. We conclude the paper by implementing the heading-independent method on real Micro Aerial Vehicles (MAVs) and performing leader-follower flight in an indoor environment. Despite the observability analysis showing leader-follower flight to be an especially difficult task, we still manage to successfully fly for over 3 minutes with two fully autonomous followers using only on-board sensing.

In recent years Adaptive Critic Designs (ACDs) have been applied to adaptive flight control of uncertain, nonlinear systems. However, these algorithms often rely on representative models as they require an offline training stage. Therefore, they have limited applicability to a system for which no accurate system model is available, nor readily identifiable. Inspired by recent work on Incremental Dual Heuristic Programming (IDHP), this paper derives and analyzes a Reinforcement Learning (RL) based framework for adaptive flight control of a CS-25 class fixed-wing aircraft. The proposed framework utilizes Artificial Neural Networks (ANNs) and includes an additional network structure to improve learning stability. The designed learning controller is implemented to control a high-fidelity, six-degree-of-freedom simulation of the Cessna 550 Citation II PH-LAB research aircraft. It is demonstrated that the proposed framework is able to learn a near-optimal control policy online without a priori knowledge of the system dynamics nor an offline training phase. Furthermore, it is able to generalize and operate the aircraft in not previously encountered flight regimes as well as identify and adapt to unforeseen changes to the aircraft’s dynamics.

Conventional linear control allocation (LCA) methods fail to provide satisfactory performance in flight control systems (FCS) for aircraft with highly nonlinear and coupled control effector suites, especially for tailless aircraft with strong interactions between control effectors. This thesis implements an incremental nonlinear control allocation (INCA) approach that can capture nonlinearities and interactions of control effectors, while being solvable with computationally efficient LCA algorithms. This makes INCA suitable for real-time control allocation in FCS. This incremental reformulation of the control allocation problem is based on a Jacobian model of the control effectors, and relies on angular acceleration measurements to reduce model dependency. In addition, real-time measurements of the actuator positions mitigate typical problems related to couplings between control allocators and actuator dynamics. In this paper, LCA- and INCA-based nonlinear FCS are designed for the Innovative Control Effectors (ICE) aircraft, a highly maneuverable tailless aircraft with 13 highly nonlinear, interacting and axis-coupled control effectors. Real-time simulation results showed that INCA dramatically improves tracking and control allocation performance with respect to LCA methods, thus improving maneuverability and exploiting the full potential of innovative control effector suites. Additionally, a sensitivity analysis revealed that the INCA method is highly robust against Jacobian model mismatch.

In advanced robotic applications such as robotic locomotion, vehicle and flight simulators, and material test devices, there are higher requirements on stiffness, robustness and power ability for the mechanical structure and the actuator. Hence, it is common for such applications to use parallel manipulators and hydraulic actuators, due to their advantages in these aspects over their counterparts of serialmanipulators and electrical actuators. When high-precision motion control is required for such systems, advanced model-based controllers, including feedback linearization and adaptive control, have been proposed in state-of-the-art studies for both hydraulic and parallel mechanical systems. However, the high complexity, nonlinearity and model uncertainty of these systems raise significant challenges for their motion control accuracy. @en

Higher levels of autonomy in aerospace systems is an urgent requirement, considering the increase in control task difficulties, and the need for adaptability of the complex systems. Reinforcement learning (RL) control is one of the promising approaches for adaptive control of air vehicles that are designed for automation. Conventional discrete reinforcement learning methods fail in providing satisfactory performance for flight control systems (FCSs), especially for a complex configuration of a tailless over-actuated aircraft. The lack of efficiency of the discrete controller in exploration for finding the optimal policy, the so-called problem of 'curse of dimensionality', results in an approach that is not suitable for online implementation. Also, the achieved discrete non-smooth control policy usually does not apply to the real world control surfaces. This paper studies the experiments with Heuristic Dynamic Programming (HDP), a method obtained from adaptive critic design (ACDs), as a continuous reinforcement learning approach. ACD methods can capture the nonlinearities in the complex dynamics of the aircraft while solving the control problem computationally efficient by using continuous states and action spaces. Such qualities make ACDs suitable for online FCS design for unstable systems like tailless aircraft. In this paper, the ACD-based controller is developed and implemented for the Innovative Control Effector (ICE) aircraft, a highly maneuverable aircraft with redundancy in its control effectors suite. The coupled control effectors configuration has strong interactions and, therefore, proposes a need for proper control allocation. The online simulation results show the accuracy of the designed continuous RL controller in the longitudinal control of the aircraft using different sets of control effectors. The proposed approach also shows significant improvements in the tracking performance and control policy smoothness (e.g., compared to discrete methods).

As of April 2019, upset prevention and recovery training in flight simulation training devices is a mandatory practice for commercial and civil aircraft pilots. Aircraft stalls are a well-known upset type, therefore simulation of aircraft stall behavior is required. A key characteristic of stalls is the buffeting component, which in current stall models is still insufficiently modeled. In this research, a new methodology to more accurately model stall buffet behavior using swept wing flight test data is presented. Buffet effects occur after exceeding the critical angle of attack, with an aircraft type-specific buffet onset duration to fully develop maximum buffet intensity. The buffet transient behavior is modeled with a frequency response fit and a multivariate second-order polynomial to capture aircraft eigenmode-shape frequencies and buffet intensity respectively. Aircraft recovery and thus receding buffet effects occur at an increasing angle of attack, which is used as buffet offset. Generalization of the results was shown with the validation of data set of a straight wing aircraft, which indicates a step towards a more generic stall buffet model methodology.

Incremental Nonlinear Dynamic Inversion has shown increases in performance and robustness to model mismatches and uncertainties in hydraulic control as compared to other model-based controllers. This work will expand on hydraulic Incremental Nonlinear Dynamic Inversion force control and discuss three of its challenges and accompanying solutions. The solutions are tested experimentally on the SIMONA Research Simulator. First, the importance of the synchronization of the linearization loops of the Incremental Nonlinear Dynamic Inversion controller is shown analytically and through experiments. Secondly, it is found that saturation of the electro-hydraulic servo-valve leads to wind-up when integral action is present. Pseudo Control Hedging is implemented to deal with the wind-up effects due to saturation. The implementation of the Pseudo Control Hedging is evaluated through experiments on the SIMONA Research Simulator. Thirdly, it is shown analytically that the main spool measurements should be used for the control increment of the Incremental Nonlinear Dynamic Inversion controller. Measurements of the main spool position are therefore used in the control, but it is often difficult to extract these from the servo-valve. Through experiments it is found that measurements of the main spool position can be replaced by either a first- or second-order servo-valve model.

Pneumatic cylinders provide an environment-friendly actuation means by minimizing the leakage of any harmful industrial fluids, as occurs for hydraulic actuators. Thus, pneumatic actuators require less maintenance, compared to hydraulic actuators. Moreover, for a similar weight of hydraulic actuator, the cost of a pneumatic actuation system is less. However, pneumatic actuation has not been utilized widely for industrial applications due to its highly-nonlinear nature. The compressibility property of air, friction forces in the cylinder and the switching dynamics of air flow-rate through the valve are some of the causes for this non-linearity. Therefore, these characteristics can often make the implementation of a model-dependent controller for a pneumatic system difficult. Incremental nonlinear dynamic inversion (INDI) is a control approach which uses less plant-model information, and is thus inherently robust to mismatches in the known plant-model, and also to external disturbances. INDI has recently gained popularity, especially in the aerospace-control research community, but it has never been implemented for controlling a pneumatic system, which necessitates additional research. Therefore, developing an incremental nonlinear controller for a pneumatic system is the main focus of this research article which is accomplished by utilizing a cascaded-control approach, where the inner-loop INDI tracks a given force and the outer-loop NDI is for controlling the piston-position. Moreover, realistic sensor noises have been added in the simulation and the robustness of incremental approach is demonstrated with respect to a baseline PID controller. Besides this, the external load attached to the cylinder-piston is increased by five times and also made variable, in order to show the effectiveness of the incremental control approach. Furthermore, a first-order filter is used for attenuating the sensor noise and the pneumatic valve is simulated using a first-order model. Finally, a series of recommendations is discussed at the end, for future works.

In this paper an adaptive version of the incremental nonlinear control allocation (INCA), which is able to account for sudden changes in the aerodynamic configuration of an aircraft, is investigated. The controller is designed for the highly maneuverable and tailless innovative control effectors (ICE) aircraft, which has a control suite of 13 nonlinear, interacting and axis-coupled effectors. The least mean squares (LMS) method is used to estimate a delta control effectiveness Jacobian (CEJ) based on the difference between the expected and measured accelerations of the aircraft. This delta CEJ model is then added to the onboard spline CEJ model to achieve fault tolerance. By keeping the nominal spline model intact the nonlinearities and interactions of the effectors remain modeled, while the LMS estimator allows for fast adaptation. Simulations for four different maneuvers and failure cases showed that the estimator is able to stabilize the aircraft for the most demanding maneuver. For two less demanding maneuvers the adaptive controller greatly reduced the control effort while keeping the tracking error similar to the non-adaptive controller. For the remaining fourth maneuver, which operates in a flight region with the most significant interactions and nonlinearities, the adaptive controller had a reduced performance compared to the nonadaptive controller. A sensitivity analysis showed that the choice of design parameters greatly influences the results, and no general set of best performing parameters was found.

With most of the current research in quadrotor Loss-Of-Control (LOC) being focussed on specific failure cases e.g. sensor faults and Single-Rotor Failure (SRF), the growth that is expected in the drone industry will not be able to be sustained, in regards to the safety of individuals in urban areas. Without an assurance of reliability regarding the safety of drones this is just not feasible. With the National Aeronautics and Space Administration (NASA) outlining flight traffic rules for drones it seems to be just a matter of time until it will be normal to see such vehicles flying around. Therefore it is of the utmost importance to improve the overall resilience of quadrotors. This work seeks to show the importance of modelling hazards such as the Vortex Ring State (VRS) and blade flapping to broaden the approach on solving LOC of quadrotors. Through the adaptation of the definition of LOC of aircraft to quadrotors and a comparative analysis of quadrotor flights, of both the nominal and SRF configuration, a Quantitative LOC Definition (QLD) for quadrotors is created. This definition is then validated through the analysis of thrust stand measurements and quadrotor flights. Resulting in a measure for the identification of LOC events.