Two-layer on-line parameter estimation for adaptive incremental backstepping flight control for a transport aircraft in uncertain conditions

Presence of uncertainties caused by unforeseen malfunctions of the actuator or changes in aircraft behavior could lead to aircraft loss of control during flight. The paper presents two-layer parameter estimation procedure augmenting Incremental Backstepping (IBKS) control algorithm designed for a large transport aircraft. IBKS uses angular accelerations and current control deflections to reduce the dependency on the aircraft model. However, it requires knowledge of the control effectiveness. The proposed identification technique is capable to detect possible problems such as a failure or presence of unknown actuator dynamics even in case of redundancy of control actuation. At the first layer, the system performs monitoring of possible failures. If a problem in one of the control direction is detected the algorithm initiates the second-layer identification determining the individual effectiveness of the each control surface involved in this control direction. Analysis revealed a high robustness of the IBKS to actuator failures. However, in severe conditions with a combination of multiple failures and presence of unmodelled actuator dynamics IBKS could lost stability. Meanwhile, proposed control derivative estimation procedure augmenting the IBKS control helps to sustain stability

Two-layer adaptive augmentation for incremental backstepping flight control of transport aircraft in uncertain conditions

Presence of uncertainties caused by unforeseen malfunctions in actuation system or changes in aircraft behaviour could lead to aircraft loss-of-control during flight. The paper presents Two-Layer Adaptive augmentation for Incremental Backstepping (TLA-IBKS) control algorithm designed for a large transport aircraft. IBKS uses angular accelerations and current control deflections to reduce the dependency on the aircraft model. However, it requires knowledge of control effectiveness. The proposed technique is capable to detect possible failures for an overactuated system. At the first layer, the system performs monitoring of a combined effectiveness and detects possible failures via an innovation process. If a problem is detected the algorithm initiates the second-layer algorithm for adaptation of effectiveness of individual control effectors. Filippov generalization for nonlinear differential equations with discontinuous right-hand sides is utilized to develop Lyapunov based tuning function adaptive law for the second layer adaptation and to prove uniform asymptotic stability of the resultant closed-loop system. Conducted simulation manifests that if the input-affine property of the IBKS is violated, e.g., in severe conditions with a combination of multiple failures, the IBKS can lose stability. Meanwhile, the proposed TLA-IBKS algorithm demonstrates improved stability and tracking performance

Understandings of the incremental backstepping control through theoretical analysis under the model uncertainties

In this paper, theoretical analysis on the incremental backstepping control is suggested especially under the existence of model uncertainties. This algorithm is proposed in the previous studies by modifying the backstepping method to reduce model dependency. Because this method is a type of nonlinear control and the model uncertainties are assumed to be considered, it is difficult to have theoretical analysis, which causes lack of understandings about this algorithm. Therefore, this paper suggests closed-loop analysis with simplified dynamics under the model uncertainty. Transfer function is derived and poles, stability condition, steady state error, and settling time are presented. In addition, the effects of model uncertainties and gains are identified through analysis. Proposed analysis is meaningful in terms of establishing critical understandings about the algorithm, even though the simplified dynamics is applied for analysis purpose

Gaussian process adaptive incremental backstepping flight control

The presence of uncertainties caused by unforeseen malfunctions in the actuation system or changes in aircraft behaviour could lead to aircraft loss of control during flight. The paper proposes almost model-independent control law combining recent developments in nonlinear control theory, data-driven methods, and sensor technologies by considering Gaussian Processes Adaptive augmentation for Incremental Backstepping control (IBKS) algorithm. IBKS uses angular accelerations and current control deflections to reduce the dependency on the aircraft model. However, it requires knowledge of control effectiveness. Conducted research shows that if the input-affine property of the IBKS is violated, e.g., in severe conditions with a combination of multiple failures, the IBKS can lose stability. Meanwhile, the GP-based estimator provides fast identification and the resultant GP-adaptive IBKS algorithm demonstrates improved stability and tracking performance. The performance of the algorithm is validated using a large transport aircraft flight dynamics model

Sparse online Gaussian process adaptation for incremental backstepping flight control

Presence of uncertainties caused by unforeseen malfunctions in actuation or measurement systems or changes in aircraft behaviour could lead to aircraft loss-of-control during flight. This paper considers sparse online Gaussian Processes (GP) adaptive augmentation for Incremental Backstepping (IBKS) flight control. IBKS uses angular accelerations and control deflections to reduce the dependency on the aircraft model. However, it requires knowledge of the relationship between inner and outer loops and control effectiveness. Proposed indirect adaptation significantly reduces model dependency. Global uniform ultimate boundness is proved for the resultant GP adaptive IBKS. Conducted research shows that if the input-affine property is violated, e.g., in severe conditions with a combination of multiple failures, the IBKS can lose stability. Meanwhile, the proposed sparse GP-based estimator provides fast online identification and the resultant controller demonstrates improved stability and tracking performance

A Contribution to the Design of Highly Redundant Compliant Aerial Manipulation Systems

Es ist vorhersehbar, dass die Luftmanipulatoren in den nächsten Jahrzehnten für viele Aufgaben eingesetzt werden, die entweder zu gefährlich oder zu teuer sind, um sie mit herkömmlichen Methoden zu bewältigen. In dieser Arbeit wird eine neuartige Lösung für die Gesamtsteuerung von hochredundanten Luftmanipulationssystemen vorgestellt. Die Ergebnisse werden auf eine Referenzkonfiguration angewendet, die als universelle Plattform für die Durchführung verschiedener Luftmanipulationsaufgaben etabliert wird. Diese Plattform besteht aus einer omnidirektionalen Drohne und einem seriellen Manipulator. Um den modularen Regelungsentwurf zu gewährleisten, werden zwei rechnerisch effiziente Algorithmen untersucht, um den virtuellen Eingang den Aktuatorbefehlen zuzuordnen. Durch die Integration eines auf einem künstlichen neuronalen Netz basierenden Diagnosemoduls und der rekonfigurierbaren Steuerungszuordnung in den Regelkreis, wird die Fehlertoleranz für die Drohne erzielt. Außerdem wird die Motorsättigung durch Rekonfiguration der Geschwindigkeits- und Beschleunigungsprofile behandelt. Für die Beobachtung der externen Kräfte und Drehmomente werden zwei Filter vorgestellt. Dies ist notwendig, um ein nachgiebiges Verhalten des Endeffektors durch die achsenselektive Impedanzregelung zu erreichen. Unter Ausnutzung der Redundanz des vorgestellten Luftmanipulators wird ein Regler entworfen, der nicht nur die Referenz der Endeffektor-Bewegung verfolgt, sondern auch priorisierte sekundäre Aufgaben ausführt. Die Wirksamkeit der vorgestellten Lösungen wird durch umfangreiche Tests überprüft, und das vorgestellte Steuerungssystem wird als sehr vielseitig und effektiv bewertet.:1 Introduction 2 Fundamentals 3 System Design and Modeling 4 Reconfigurable Control Allocation 5 Fault Diagnostics For Free Flight 6 Force and Torque Observer 7 Trajectory Generation 8 Hybrid Task Priority Control 9 System Integration and Performance Evaluation 10 ConclusionIn the following decades, aerial manipulators are expected to be deployed in scenarios that are either too dangerous for human beings or too expensive to be accomplished by traditional methods. This thesis presents a novel solution for the overall control of highly redundant aerial manipulation systems. The results are applied to a reference configuration established as a universal platform for performing various aerial manipulation tasks. The platform consists of an omnidirectional multirotor UAV and a serial manipulator. To ensure modular control design, two computationally efficient algorithms are studied to allocate the virtual input to actuator commands. Fault tolerance of the aerial vehicle is achieved by integrating a diagnostic module based on an artificial neural network and the reconfigurable control allocation into the control loop. Besides, the risk of input saturation of individual rotors is minimized by predicting and reconfiguring the speed and acceleration responses. Two filter-based observers are presented to provide the knowledge of external forces and torques, which is necessary to achieve compliant behavior of the end-effector through an axis-selective impedance control in the outer loop. Exploiting the redundancy of the proposed aerial manipulator, the author has designed a control law to achieve the desired end-effector motion and execute secondary tasks in order of priority. The effectiveness of the proposed designs is verified with extensive tests generated by following Monte Carlo method, and the presented control scheme is proved to be versatile and effective.:1 Introduction 2 Fundamentals 3 System Design and Modeling 4 Reconfigurable Control Allocation 5 Fault Diagnostics For Free Flight 6 Force and Torque Observer 7 Trajectory Generation 8 Hybrid Task Priority Control 9 System Integration and Performance Evaluation 10 Conclusio

Understandings of classical and incremental backstepping controllers with model uncertainties

This paper suggests closed-loop analysis results for both classical and incremental backstepping controllers considering model uncertainties. First, transfer functions with each control algorithm under the model uncertainties, are compared with the ones for the nominal case. The effects of the model uncertainties on the closed-loop systems are critically assessed via investigations on stability conditions and performance metrics. Second, closed-loop characteristics with classical and incremental backstepping controllers under the model uncertainties are directly compared using derived common metrics from their transfer functions. This comparative study clarifies how the effects of the model uncertainties to the closed-loop system become different depending on the applied control algorithm. It also enables understandings about the effects of additional measurements in the incremental algorithm. Third, case studies are conducted assuming that the uncertainty exists only in one aerodynamic derivative estimate while the other estimates have true values. This facilitates systematic interpretations on the impacts of the uncertainty on the specific aerodynamic derivative estimate to the closed-loop system

Understandings of incremental backstepping controller considering measurement delay with model uncertainty

In this paper, closed loop characteristics with an incremental backstepping (IBKS) controller are investigated with consideration of measurement delays and model uncertainties. To judge absolute stability of the system, a systematic analysis framework is proposed which examines the existence of unstable poles from a derived characteristic equation with high nonlinearity due to the considered measurement delays. One of the key findings from the analysis results is that the system is stable only when a specific relationship between the measurement delays is satisfied and this stability condition is affected by the model uncertainty. Critical understandings about individual and integrated effects of the measurement delays and the model uncertainties to the system are suggested through a comparative study. Verification and validation of the obtained properties from the framework are performed through simulations

Interpolated reference model based fault tolerant sliding mode control for an octorotor

This is the final version. Available on open access from Wiley via the DOI in this recordData availability statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.In this paper, a quasi-linear parameter varying sliding mode control allocation law is proposed for the fault tolerant control of an octorotor. In the event of rotor faults/failures an allocation law redistributes the control effort among the remaining healthy rotors. The sliding mode control law is designed to guarantee asymptotic tracking of a reference model which is tuned on-line, through an interpolated feedback gain, to ensure that the control signals remain within their saturation limits. A method for designing the parameterised feedback gain is proposed which is shown to maximise a defined stability criteria whilst preventing undesirable performance characteristics in the reference model. The proposed scheme is tested on a non-linear octorotor model in the presence of severe rotor failures and uncertainty/disturbances

PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles

There exists an increasing demand for a flexible and computationally efficient controller for micro aerial vehicles (MAVs) due to a high degree of environmental perturbations. In this work, an evolving neuro-fuzzy controller, namely Parsimonious Controller (PAC) is proposed. It features fewer network parameters than conventional approaches due to the absence of rule premise parameters. PAC is built upon a recently developed evolving neuro-fuzzy system known as parsimonious learning machine (PALM) and adopts new rule growing and pruning modules derived from the approximation of bias and variance. These rule adaptation methods have no reliance on user-defined thresholds, thereby increasing the PAC's autonomy for real-time deployment. PAC adapts the consequent parameters with the sliding mode control (SMC) theory in the single-pass fashion. The boundedness and convergence of the closed-loop control system's tracking error and the controller's consequent parameters are confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's efficacy is evaluated by observing various trajectory tracking performance from a bio-inspired flapping-wing micro aerial vehicle (BI-FWMAV) and a rotary wing micro aerial vehicle called hexacopter. Furthermore, it is compared to three distinctive controllers. Our PAC outperforms the linear PID controller and feed-forward neural network (FFNN) based nonlinear adaptive controller. Compared to its predecessor, G-controller, the tracking accuracy is comparable, but the PAC incurs significantly fewer parameters to attain similar or better performance than the G-controller.Comment: This paper has been accepted for publication in Information Science Journal 201