Incremental Nonlinear Dynamic Inversion (INDI) has received substantial interest in the recent years as a nonlinear flight control law design methodology that features inherent robustness against bare airframe aerodynamic variations. However, systematic studies into the robust design benefits of INDI-based control over the classical divide-and-conquer philosophy have been scarce. To bridge this gap, this paper compares the setup of hybrid INDI with a standard industry benchmark that is based on two-degree-of-freedom gain-scheduled proportional-integral-derivative control. This is done on an architectural basis and in terms of achievable robust stability and performance levels with respect to a common set of design requirements. To this end, a non-smooth, multi-objective H_∞-synthesis algorithm is used that incorporates mixed parametric and dynamic uncertainties in the design objective and constraints. It is shown that close similarities exist between hybrid INDI design and gain-scheduled PID control, which leads to virtually equivalent robustness and performance outcomes in both linear time-invariant and linear time-varying contexts. It is therefore concluded that the main benefit of the hybrid INDI does not lie in improved robustness properties per se, but in the opportunity to perform modular robust design in an implicit model-following context. Specifically, this implies that the areas of flying qualities, robustness, and nonlinear implementation are directly visible and accessible in the control law structure.

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Airborne wind energy is an emerging technology that uses tethered flying devices to capture stronger and more steady winds at higher altitudes. Compared to smaller systems, megawatt-scale systems are substantially affected by gravity during flight operation, resulting in power fluctuations. MegAWES, a 3 MW reference model, experiences power fluctuations between -5.8 MW and +20.5 MW every 12.5 seconds during the traction phase when using its baseline controller at a wind speed of 22 m/s. The baseline controller does not have a power limit, leading to high peak power, and aims to keep the tether force constant, causing it to consume power when the kite is flying upwards. In this paper, we implement an optimal torque controller in the MegAWES framework and show that this eliminates the power consumption during the traction phase. Furthermore, we propose a kite tether force controller that allows setting a power limit when combined with the 2-phase reeling strategy, which decreases the peak power. Our new architecture reduces the power output range by 75% to between +3.7 MW and +9.4 MW in strong wind conditions.
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To reduce the impact of aviation on the environment, technological innovations, such as the Flying-V are required. The Flying-V is a proposed commercial flying wing, which uses the Airbus A350-900 as reference aircraft. In this work, a Flight Control system for the Flying-V is proposed with a longitudinal C ^* control law, and a Rate Control Attitude Hold roll control law. This Flight Control System also includes a Flight Envelope Protection law to prevent reaching angles of attack higher than 30 degrees, where the Flying-V becomes statically unstable. The FEP also prevents the Flying-V from reaching load factors above 2.5 and limits the roll angle. The control laws are tuned to be within level 1 handling qualities in the selected approach and cruise conditions, with the presence of sensor dynamics, and a digital control system. Robustness for aerodynamic uncertainties is also shown. Finally, it is shown that the FEP is able to prevent the angle of attack from becoming too large.

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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.@en

MegAWES is a reference design and simulation framework for ground-generation, fixed-wing airborne wind energy systems with a nominal power output of 3 MW. The winch size of MegAWES is based on a smaller system and needs to be scaled up because the current size leads to unrealistically fast dynamics, which require saturation. However, there is no available method to select an appropriate size for the winch. Additionally, while it has been hypothesized that the size of the winch has a significant effect on the dynamics of the overall system for ground-generation concepts, this effect has not been quantified. In this work, we first analyze the effects of the winch size on the system dynamics using a linearized model. Second, we present a method to find the upper bound for the size of the winch based on a selected maximum tether force overshoot during nominal operation. Third, we apply this method to find an upper bound for the winch size for the MegAWES reference design. Using the nonlinear MegAWES simulation framework, we validated this upper bound. At the upper bound, the system accurately tracked the reference tether force without overshoot and when exceeding our upper bound, the tether force response was oscillatory and overshot its ideal value.

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The functional architecture of a flight control system (FCS) is driven by multiple objectives related to the aircraft’s operational mission and in-service performance targets. Control allocation (CA) is a common method to ensure adequate use of the available effector architecture once the number of control effectors linked to the FCS increases and the control problem becomes overdetermined. A primary CA design objective is to ensure that high-level motion control demands are met. However, the additional degrees of freedom offered by the control effector suite can also be exploited to perform secondary control tasks. In this light, this article focuses on the Incremental Control Allocation (INCA) framework in the context of in-flight optimization of arbitrary secondary flight control design objectives. An extension to the existing INCA concept is formulated that isolates this secondary control task from generating primary control demands. Moreover, an alternative, but closely related design method based on optimal control principles is proposed that extents the role of the control allocator to active control of the system dynamics. Design examples are demonstrated for least-squares minimization of total drag and control activity. These are analyzed in linear and nonlinear simulation scenarios based on an open-source General Dynamics F-16 simulation model.

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Incremental nonlinear dynamic inversion (INDI) is a sensor-based control law design strategy that is based on the principles of feedback linearization. Contrary to its nonincremental counterpart (nonlinear dynamic inversion), this design method does not require a complete onboard model of the airframe dynamics and is therefore more robust against regular perturbations arising from aerodynamic variations. Therefore, INDI brings a natural design approach to desirable flying qualities. However, robustness against singular perturbations, which may arise due to transport delays, elastic airframe effects, or other types of badly modeled or unknown dynamics, is a known challenge for INDI-based control laws. Therefore, this paper addresses the question of robust stability and performance for INDI and its linear form (incremental dynamic inversion [IDI]) in the context of mixed regular and singular perturbations. This is done through analytical insights and by performing quantitative robustness assessments based on the structured singular value framework. Additionally, inversion loop augmentation solutions are investigated using robust synthesis techniques to improve the robustness characteristics of basic IDI designs.

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Incremental Nonlinear Dynamic Inversion (INDI) is a sensor-based control law design strategy that is based on the principles of feedback linearization. Contrary to its non-incremental counterpart (NDI), this design method does not rely on the availability of a high-fidelity on-board model of the airframe dynamics and is robust to aerodynamic variations. Consequently, INDI brings a natural and robust design approach to desirable flying qualities. However, robustness to singular perturbations, which may arise due to transportation lags, elastic airframe effects, or other types of badly modelled or unknown dynamics, is a known challenge for INDI-based control laws. In this article, the general stability and performance robustness properties of INDI and its linear form (IDI) are described analytically and analyzed in a flight control law design study by means of the structured singular value frame framework. In addition, inversion loop augmentation solutions are investigated using automated synthesis to further improve the robustness characteristics of basic IDI designs. @en

Control augmentation systems based on Incremental Nonlinear Dynamic Inversion (INDI) are able to provide high-performance nonlinear control without a holistic model. Considering an angular rate control law for a fixed-wing aircraft, only a control effectiveness (CE) model and angular acceleration measurement feedback is required. Despite enhanced robustness against parametric model mismatches due to reduced model dependency, the performance of INDI-based control laws can still vary due to inaccurate CE models. This paper confirms that longitudinal centre of gravity (CG) shifts and CE uncertainty result in varying handling qualities and stability (HQ\&S) characteristics. An adaptive solution using Least-Mean-Square (LMS) based parameter estimation is investigated to address these variations. The results demonstrate that online CE model correction result in reduced HQ\&S variation. However, it was found that some flight conditions together with adverse CG shifts could lead to violation of the time-scale separation assumption that underlies the adaptive control law design. As this assumption is inherent to the INDI control design itself as well, online CE model correction is only partly able to resolve the resulting performance variations.@en

Safe Curriculum Learning aims at improving safety and efficiency aspects of Reinforcement Learning (RL). Curricular RL approaches divide a task into stages of increasing complexity in order to increase efficiency. This paper proposes a black box safe curriculum learning architecture applicable to systems with parametric unknowns. The agent domain solely requires knowledge of the state and action spaces’ dimensions for a given task and system. By adding system identification capabilities to existing safe curriculum learning paradigms, the proposed architecture ensures safe learning of tracking tasks without requiring initial knowledge of the system dynamics. A model estimate is generated online to complement safety filters that rely on uncertain models for their safety guarantees. This research explicitly targets linearised systems with decoupled dynamics. The paradigm is initially verified on a mass-spring-damper system, after which it is applied to a quadrotor altitude and attitude tracking task. The RL agent is able to safely learn an optimal policy that can track an independent reference on each degree of freedom.@en

Flight control systems enable the improvement of natural flying qualities and airframe performance of an aircraft. In this article, an incremental optimization control scheme is proposed to optimize a given performance objective set by the designer in an online fashion using limited model information. This scheme is applied to improve the aerodynamic efficiency levels of the General Dynamics F-16 by optimizing symmetric movement of the leading edge flap (LEF) devices, based on an open-source nonlinear simulation model. Other design goals are addressed by refining the control objective, which explicitly embeds design trade-offs in the control law. A complete control architecture is arrived at through the design of a parallel INDI control law that performs the function
of angular rate control to improve natural flying qualities. A nonlinear simulation scenario shows that the proposed control framework is capable of meeting desired handling quality characteristics while simultaneously improving aerodynamic efficiency levels and control activity. In addition, a robustness assessment is performed to gain insight into the sensitivity of the design to
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Incremental Nonlinear Dynamic Inversion (INDI) is a sensor-based control strategy, which has shown robustness against model uncertainties on various aerospace platforms. The sensor-based nature of the method brings attractive properties, which has made it popular in the last decade. INDI globally linearizes the system by making use of control input and state derivative feedback. Despite the enhanced robustness against parametric system uncertainties compared to traditional NDI, mitigating the effects of time lag between the control input and state derivative feedback paths represents an important challenge for INDI. Past research has shown that this can be addressed by synchronizing these feedback signals, although the method remains vulnerable to unexpected measurement delays. This paper proposes a hybrid INDI approach based on complementary filtering to further mitigate this robustness issue. The approach fuses the system model and sensor measurement to generate an estimate of the angular acceleration of the system. The estimation responds rapidly to the system input thanks to the on-board model, whereas adequate accuracy in the low-to-medium frequency range is maintained by the sensor measurement. The control law is found to retain good performance in case of model mismatches and measurement delays. To demonstrate the method, a hybrid INDI-based attitude control law is designed for a nonlinear F-16 aircraft model. The robustness properties of the resulting control system are analyzed using time-domain simulations. @en

Reinforcement learning (RL) enables the autonomous formation of optimal, adaptive control laws for systems with complex, uncertain dynamics. This process generally requires a learning agent to directly interact with the system in an online fashion. However, if the system is safety-critical, such as an Unmanned Aerial Vehicle (UAV), learning may result in unsafe behavior. Moreover, irrespective of the safety aspect, learning optimal control policies from scratch can be inefficient and therefore time-consuming. In this research, the safe curriculum learning paradigm is proposed to address the problems of learning safety and efficiency simultaneously. Curriculum learning makes the process of learning more tractable, thereby allowing the intelligent agent to learn desired behavior more effectively. This is achieved by presenting the agent with a series of intermediate learning tasks, where the knowledge gained from earlier tasks is used to expedite learning in succeeding tasks of higher complexity. This framework is united with views from safe learning to ensure that safety constraints are adhered to during the learning curriculum. This principle is first investigated in the context of optimal regulation of a generic mass-spring-damper system using neural networks and is subsequently applied in the context of optimal attitude control of a quadrotor UAV with uncertain dynamics.@en

Traditionally, aircraft actuator control systems are commanded by means of control surface position or rate references from the flight control laws. This is natural for hydraulic actuators, but less so for electro-mechanical actuators (EMAs). EMAs apply forces or moments that are proportional to their current inputs. For this reason, it has been investigated if this principle can be used within an aircraft flight control system. This has several distinct advantages, including prevention of force fighting between parallel actuators, natural alleviation of structural and actuator loads in the face of atmospheric disturbances, and the possibility to restore reversible control behavior for irreversible systems. A complete design and a flight test campaign were performed using a Cessna Citation II with experimental fly-by-wire system. As a first step, current control loops were designed and tested for the electric aircraft servos. In a second step, an existing set of flight control laws based on incremental nonlinear dynamic inversion was adapted to command current instead of control surface angle commands. After implementation on the aircraft, the integrated system was intensively tested in flight. Direct comparisons with open-loop control were made. The tests were highly successful and very encouraging to further investigate this integrated approach for future applications.

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