Model Predictive Control for Micro Aerial Vehicles: A Survey

This paper presents a review of the design and application of model predictive control strategies for Micro Aerial Vehicles and specifically multirotor configurations such as quadrotors. The diverse set of works in the domain is organized based on the control law being optimized over linear or nonlinear dynamics, the integration of state and input constraints, possible fault-tolerant design, if reinforcement learning methods have been utilized and if the controller refers to free-flight or other tasks such as physical interaction or load transportation. A selected set of comparison results are also presented and serve to provide insight for the selection between linear and nonlinear schemes, the tuning of the prediction horizon, the importance of disturbance observer-based offset-free tracking and the intrinsic robustness of such methods to parameter uncertainty. Furthermore, an overview of recent research trends on the combined application of modern deep reinforcement learning techniques and model predictive control for multirotor vehicles is presented. Finally, this review concludes with explicit discussion regarding selected open-source software packages that deliver off-the-shelf model predictive control functionality applicable to a wide variety of Micro Aerial Vehicle configurations

ヘクサコプターのための耐故障制御と視覚に基づくナビゲーション

学位の種別:課程博士University of Tokyo(東京大学

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

Safe and accurate MAV Control, navigation and manipulation

This work focuses on the problem of precise, aggressive and safe Micro Aerial Vehicle (MAV) navigation as well as deployment in applications which require physical interaction with the environment. To address these issues, we propose three different MAV model based control algorithms that rely on the concept of receding horizon control. As a starting point, we present a computationally cheap algorithm which utilizes an approximate linear model of the system around hover and is thus maximally accurate for slow reference maneuvers. Aiming at overcoming the limitations of the linear model parameterisation, we present an extension to the first controller which relies on the true nonlinear dynamics of the system. This approach, even though computationally more intense, ensures that the control model is always valid and allows tracking of full state aggressive trajectories. The last controller addresses the topic of aerial manipulation in which the versatility of aerial vehicles is combined with the manipulation capabilities of robotic arms. The proposed method relies on the formulation of a hybrid nonlinear MAV-arm model which also takes into account the effects of contact with the environment. Finally, in order to enable safe operation despite the potential loss of an actuator, we propose a supervisory algorithm which estimates the health status of each motor. We further showcase how this can be used in conjunction with the nonlinear controllers described above for fault tolerant MAV flight. While all the developed algorithms are formulated and tested using our specific MAV platforms (consisting of underactuated hexacopters for the free flight experiments, hexacopter-delta arm system for the manipulation experiments), we further discuss how these can be applied to other underactuated/overactuated MAVs and robotic arm platforms. The same applies to the fault tolerant control where we discuss different stabilisation techniques depending on the capabilities of the available hardware. Even though the primary focus of this work is on feedback control, we thoroughly describe the custom hardware platforms used for the experimental evaluation, the state estimation algorithms which provide the basis for control as well as the parameter identification required for the formulation of the various control models. We showcase all the developed algorithms in experimental scenarios designed to highlight the corresponding strengths and weaknesses as well as show that the proposed methods can run in realtime on commercially available hardware.Open Acces

Hybrid active force control for fixed based rotorcraft

Disturbances are considered major challenges faced in the deployment of rotorcraft unmanned aerial vehicle (UAV) systems. Among different types of rotorcraft systems, the twin-rotor helicopter and quadrotor models are considered the most versatile flying machines nowadays due to their range of applications in the civilian and military sectors. However, these systems are multivariate and highly non-linear, making them difficult to be accurately controlled. Their performance could be further compromised when they are operated in the presence of disturbances or uncertainties. This dissertation presents an innovative hybrid control scheme for rotorcraft systems to improve disturbance rejection capability while maintaining system stability, based on a technique called active force control (AFC) via simulation and experimental works. A detailed dynamic model of each aerial system was derived based on the Euler–Lagrange and Newton-Euler methods, taking into account various assumptions and conditions. As a result of the derived models, a proportional-integral-derivative (PID) controller was designed to achieve the required altitude and attitude motions. Due to the PID's inability to reject applied disturbances, the AFC strategy was incorporated with the designed PID controller, to be known as the PID-AFC scheme. To estimate control parameters automatically, a number of artificial intelligence algorithms were employed in this study, namely the iterative learning algorithm and fuzzy logic. Intelligent rules of these AI algorithms were designed and embedded into the AFC loop, identified as intelligent active force control (IAFC)-based methods. This involved, PID-iterative learning active force control (PID-ILAFC) and PID-fuzzy logic active force control (PID-FLAFC) schemes. To test the performance and robustness of these proposed hybrid control systems, several disturbance models were introduced, namely the sinusoidal wave, pulsating, and Dryden wind gust model disturbances. Integral square error was selected as the index performance to compare between the proposed control schemes. In this study, the effectiveness of the PID-ILAFC strategy in connection with the body jerk performance was investigated in the presence of applied disturbance. In terms of experimental work, hardware-in-the-loop (HIL) experimental tests were conducted for a fixed-base rotorcraft UAV system to investigate how effective are the proposed hybrid PID-ILAFC schemes in disturbance rejection. Simulated results, in time domains, reveal the efficacy of the proposed hybrid IAFC-based control methods in the cancellation of different applied disturbances, while preserving the stability of the rotorcraft system, as compared to the conventional PID controller. In most of the cases, the simulated results show a reduction of more than 55% in settling time. In terms of body jerk performance, it was improved by around 65%, for twin-rotor helicopter system, and by a 45%, for quadrotor system. To achieve the best possible performance, results recommend using the full output signal produced by the AFC strategy according to the sensitivity analysis. The HIL experimental tests results demonstrate that the PID-ILAFC method can improve the disturbance rejection capability when compared to other control systems and show good agreement with the simulated counterpart. However, the selection of the appropriate learning parameters and initial conditions is viewed as a crucial step toward this improved performance

Autonomous Flight Control for Multi-Rotor UAVs Flying at Low Altitude

Unmanned aerial vehicles (UAVs) at low altitude flight may significantly degrade their performance and the safety under wind disturbances and incorrect operations. This paper presents a robust control strategy for UAVs to achieve good performance of low altitude flight and disturbance rejection. First, a novel second-order hexacopter dynamics is established and the position tracking is translated to the altitude and the rotational angle tracking problem. An integrated control scheme is created to deal with the challenges faced by hexacopter at low altitude flight, in which the influence of near-ground threshold distance and the desired roll, pitch, and yaw are analyzed. Moreover, an improved flying altitude planner and an attitude planner for low altitude conditions are designed respectively to avoid the overturning risk due to the big reaction torque and external disturbances. Second, a sliding-mode-based altitude tracking controller and an attitude tracking controller are designed to reduce the tracking errors and improve the robustness of the system. Finally, the proposed control scheme is tested on simulation and experiment platforms of multi-rotor UAV to show the feasibility and accurate trajectory tracking at low altitude flight

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