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.

Incremental control techniques such as Incremental Nonlinear Dynamic Inversion (INDI) and Incremental Backstepping (IBS) have gained recent popularity, especially in the aerospace community, due to their versatility and effectiveness which entails robustness to imprecise knowledge about the controlled system as well as robustness to external disturbances. Despite a control authority that has been proven, in several applications, to exceed that of classical control techniques, there is yet much to be studied about these control techniques. Theoretical gaps include the effectiveness of these techniques in handling time-delays as well as their robustness to sampling rates. Addressing this theoretical gap has been the focus of the research that is presented in this thesis. To meet this research aim, the control system has been analyzed through the lens of the Time-Delay System (TDS) framework. In particular, the analytic curve frequency sweeping approach as well as a set of suitable matrix inequalities that are based on the discretized Lyapunov functional method have been applied to perform this analysis in the frequency domain and
the time domain, respectively. Moreover, the time-domain results have been extended to the case of neutral time-delay systems, the derivation of which is also presented in this thesis. Furthermore, a new robust stability analysis technique is presented which is based on combining the analytic curve frequency sweeping approach with the edge theorem. This approach is made applicable to systems with different uncertainty structures through determining generator quasipolynomials that form a convex hull that overbounds the family of quasipolynomials considered. The effectiveness of these methods has been shown through their application to an INDI-controlled damped pendulum and to the INDI-controlled short period dynamics of a fixed-wing aircraft, and it is shown that the results from the frequency-domain analyses and the time-domain analyses corroborate.

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.

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 incremental nonlinear dynamic inversion, a sensor based approximate form of feedback linearization with favorable robustness properties, is applied to the traction control and stability augmentation problem of a Le Mans Prototype 1 race car. A cascaded side-slip and yaw-rate envelope protection system is developed in combination with a model following yaw-rate controller which acts inside the safe envelope. The vehicle is controlled through two limited slip differentials featured in the front and rear of the vehicle. A method is presented to account for load tranfer effects in the calculation of the limited control effectiveness associated the actuators . Simulations with a high-fidelity vehicle model demonstrate that the control system is robust against parameter uncertainties and is able to effectively keep the vehicle within the bounds of the safe envelope.

Current flight control systems fail to recover to safe flight conditions in off-nominal flight conditions and require a more advanced upset recovery methodology which is able to provide full control authority. Existing upset recovery methods are focused on fast recovery taking into account the current flight conditions while neglecting nonlinear effects. This paper implements a new approach for upset recovery which aims for maximum control effectiveness of control effectors given the flight conditions by means of incremental nonlinear dynamic inversion. A recovery control strategy based on reducing angular body rates and aerodynamic angles generates inner and outer loop commands. Pseudo control inputs defined as required moment increments are derived using a nonlinear Jacobian model of the control effectors. This pseudo control input and Jacobian determine the direction of control effector effectiveness gradient optimization. The upset recovery system is implemented on the Innovative Control Effectors (ICE) aircraft, a high performance over-actuated aircraft with 13 highly nonlinear, interacting and coupled control effectors. Real-time simulation results show that the upset recovery system is fault tolerant, considerably faster, and more reliable compared to nominal flight control in terms of recovering angular body rates and aerodynamic angles, and is applicable in every aircraft upset condition.

This work presents a vector field based path following method to be used by Multirotor Unmanned Aerial Vehicles (UAVs). The desired path to be followed is a smooth planar path defined in its implicit form. The vector field around the desired path is then constructed using the implicit function, such that the integral curves of the vector field converge to the path. The algorithm takes into account the future change in the trajectory as well as the current state of the UAV in order to calculate the desired linear acceleration, which is then tracked using the Incremental Nonlinear Dynamic Inversion (INDI) controller in the autopilot. The implementation also allows for the velocity of the UAV to be controlled independently. The efficiency of the algorithm is demonstrated using real world flight tests, and the performance is shown to be better than the
traditional carrot-chasing controller.

Tailless flapping wing micro air vehicles (FWMAVs) have
the potential of providing efficient flight at small scale,
with considerable agility. However, this agility also brings
significant control challenges, which are exacerbated by
the fact that the aerodynamics and dynamics of flapping
wing robots are still only partly understood.
In this article, we propose a novel, minimal dynamic
model that is not only validated with experimental data,
but also able to predict the consequences of various important
design changes. Specifically, the model captures
the flapping cycle averaged longitudinal dynamics of a
tailless flapping wing robot, taking into account the main
aerodynamic effects. The model is validated for airspeeds
up to 3.5 m/s (when the forward velocity starts to approximate
the wing velocities). It successfully predicts the effects
of changes to the center of mass and flight at different
pitch angles. Hence, the presented model forms an
important step in accelerating the control design of flapping
wing robots - which can now be done to a greater
extent in simulation. In order to illustrate this, we have
used the model to improve our control design, resulting in
a change of the maximal stable speed of the tailless DelFly
Transformer from 4 m/s to 7 m/s.

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.

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.

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.

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 the near future many tasks could be performed by swarms of flying robots. To successfully implement multiple of these swarms in the same airspace they will have to be decentralised, autonomously cope with high densities and even resolve conflicting objectives of other swarms, while remaining controllable by operators through high-level objectives. This article introduces a novel swarming approach dubbed "Velocity Templates" based on artificial potential fields. These global fields represent the objectives of the swarm, which are balanced with local interaction. Different fields are considered leading to still or sustained motion swarms where conflicting objectives between sub-groups or multiple swarms are gracefully resolved. The approach is implemented on groups of 2 and 4 Parrot Bebop UAVs, using an efficient on-board vision algorithm to locate neighbours and a motion tracking system for guidance. The experiments show promising results for further outdoor tests assessing the scalability of the proposed approach.

L1 adaptive control is a relatively new technique that attempts to tackle the robustness shortcoming of the MRAC controller by applying a low pass filter to the control input. It can be used as an augmentation loop using baseline controllers such as backstepping or PID. This controller has been implemented as an augmentation loop with both these baseline controllers in three models: a quadcopter, a hexacopter and a Bolkow Bo-105 helicopter 3 degree of freedom model. It is then compared to the baseline where it's shown that this implementation does not provide any significant advantages over the baseline controllers.