Implementation of disturbance rejection control based on the integration of model predictive control and nonlinear disturbance observer for unmanned aerial vehicles

As part of our recent research to assess the potential of low-cost navigation sensors for Unmanned Aerial Vehicle (UAV) applications, we investigated the potential of carrier-phase Global Navigation Satellite System (GNSS) for attitude determination and control of small size UAVs. Recursive optimal estimation algorithms were developed for combining multiple attitude measurements obtained from different observation points (i.e., antenna locations), and their efficiencies were tested in various dynamic conditions. The proposed algorithms converged rapidly and produced the required output even during high dynamics manoeuvres. Results of theoretical performance analysis and simulation activities are presented in this paper, with emphasis on the advantages of the GNSS interferometric approach in UAV applications (i.e., low cost, high data-rate, low volume/weight, low signal processing requirements, etc.). The simulation activities focussed on the AEROSONDE UAV platform and considered the possible augmentation provided by interferometric GNSS techniques to a low-cost and low-weight/volume integrated navigation system (presented in the first part of this series) which employed a Vision-Based Navigation (VBN) system, a Micro- Electro-Mechanical Sensor (MEMS) based Inertial Measurement Unit (IMU) and code-range GNSS (i.e., GPS and GALILEO) for position and velocity computations. The integrated VBN-IMU-GNSS (VIG) system was augmented using the inteferometric GNSS Attitude Determination (GAD)sensor data and a comparison of the performance achieved with the VIG and VIG/GAD integrated Navigation and Guidance Systems (NGS) is presented in this paper. Finally, the data provided by these NGS are used to optimise the design of a hybrid controller employing Fuzzy Logic and Proportional-Integral-Derivative (PID) techniques for the AEROSONDE UAV.

Quadrotor unmanned aerial vehicles (UAVs) have been actively used in various fields. However, only the altitude and the attitude in three degrees of freedom can be independently controlled since quadrotor UAVs are underactuated systems. A quad tilt rotor UAV solves the problem of an underactuated system in a general quadrotor UAV. The quad tilt rotor UAV can control both position and attitude independently by tilting the directions of the propellers. However, the flight control system in a wide range of attitudes has not been discussed yet, for example a UAV can fly and hover with a 90 pitch angle, and it can even flip over when the thrust direction is tilted in a wide enough range. In this paper, we present the attitude transition flight control system for pitch angles ranging from 0 to 90 since flight conditions with a 90 pitch angle significantly differs from that in a conventional quadrotor UAV flight. We construct an adequate control system for a flight with a wide range of attitude conditions.

For the control of unmanned aerial vehicles (UAVs) in GPS-denied environments, cameras have been widely exploited as the main sensory modality for addressing the UAV state estimation problem. However, the use of visual information for ego-motion estimation presents several theoretical and practical difficulties, such as data association, occlusions, and lack of direct metric information when exploiting monocular cameras. In this paper, we address these issues by considering a quadrotor UAV equipped with an onboard monocular camera and an inertial measurement unit (IMU). First, we propose a robust ego-motion estimation algorithm for recovering the UAV scaled linear velocity and angular velocity from optical flow by exploiting the so-called continuous homography constraint in the presence of planar scenes. Then, we address the problem of retrieving the (unknown) metric scale by fusing the visual information with measurements from the onboard IMU. To this end, two different estimation strategies are proposed and critically compared: a first exploiting the classical extended Kalman filter (EKF) formulation, and a second one based on a novel nonlinear estimation framework. The main advantage of the latter scheme lies in the possibility of imposing a desired transient response to the estimation error when the camera moves with a constant acceleration norm with respect to the observed plane. We indeed show that, when compared against the EKF on the same trajectory and sensory data, the nonlinear scheme yields considerably superior performance in terms of convergence rate and predictability of the estimation. The paper is then concluded by an extensive experimental validation, including an onboard closed-loop control of a real quadrotor UAV meant to demonstrate the robustness of our approach in real-world conditions.

Quadrotor unmanned aerial vehicles (UAVs) operating in agricultural fields for aerial photography are susceptible to external disturbances. The disturbances result in trajectory deviation and irregular image overlapping that considerably degrade image quality. Disturbance observers (DOs) are commonly researched for counteracting these effects but may have delays and limitations in handling diverse and high-frequency disturbances. To this end, this work proposes a continuous high-order sliding mode-assisted DO (HSMDO) with limited time convergence characteristics for the estimation of disturbances in systems. The observer consists of a classical nonlinear DO (NDO) and a sliding mode-assisted system (SMAS). The NDO is used to estimate disturbances preliminarily. The SMAS is utilised to assist the NDO in estimating the high-frequency component of disturbances and ensure that the entire DO is finite-time convergent. Finally, the tracking controller is designed on the basis of the HSMDO, which enables UAVs to track the prescribed trajectories under disturbances stably. Simulation results show that the proposed HSMDO can accurately estimate various types of disturbances. Moreover, the tracking controller based on the HSMDO can improve the antidisturbance performance of systems and ensure the trajectory tracking accuracy of UAVs.

This study proposes a novel nonlinear resilient trajectory control for a quadrotor unmanned aerial vehicle (UAV) using backstepping control and nonlinear disturbance observer. First, a nonlinear dynamic model for the quadrotor UAV that considers external disturbances from wind model uncertainties is developed. A nonlinear disturbance observer is then constructed separately from the controller to estimate the external disturbances and compensate for the negative effects of the disturbances. Based on the estimates from the given observer, a nominal nonlinear backstepping trajectory-tracking position controller is designed to stabilize the subsystems step by step until the ultimate control law is obtained. An extra term is added to the nominal controller to address the problem of actuator effectiveness loss and to ensure system resilience. The stability of the resilient controller is analyzed using Lyapunov stability theory. Simulation results are presented to demonstrate the effectiveness and robustness of the proposed nonlinear resilient controller.

One of the most significant disadvantages of electric multirotor unmanned aerial vehicles is their short flight time compared to fuel-powered unmanned aerial vehicles. This is mainly due to the low energy density of electric batteries. Fuel has much more energy density when compared to batteries. Electric-powered motors in multirotor unmanned aerial vehicles cannot be replaced with fuel-based engines because the stability and control of multirotor unmanned aerial vehicles rely on the high response rates of electric motors. One of the possible solutions to overcome this problem of short endurance times is by using hybrid thrusting systems that combine the advantages of both fuel and electrical propulsion systems, where high maneuverability and long endurance flight time could be achieved. In this work, hybrid thrusting and power systems for multirotor unmanned aerial vehicles are studied. Targeted hybrid thrusting systems consist of combustion engines, electric motors, and their power sources. Then a hybrid thrusting system-based quadrotor unmanned aerial vehicle model is developed. The article presents the altitude and attitude control systems of the developed hybrid thrusting system-based unmanned aerial vehicle. The presented hybrid quadcopter model comprises four electric motors and one fuel engine. The fuel engine used in this work is a 4.07 cc internal combustion engine targeting 2–3 kg unmanned aerial vehicles with up to 5 kg maximum takeoff weight. The developed hybrid quadrotor unmanned aerial vehicle achieved a 139% improvement in flight time when compared with traditional electric-based quadrotor unmanned aerial vehicles. The article also reports on other flight time-related issues such as the optimal fuel mass to battery size ratio to maximize the endurance time of the quadrotor unmanned aerial vehicles.

This paper introduces a robust dynamic sliding mode control algorithm using a nonlinear disturbance observer for system dynamics. The proposed method is applied to provide a rapid adaptation and strictly robust performance for the attitude and altitude control of unmanned aerial vehicles (UAVs). The procedure of the proposed method consists of two stages. First, a nonlinear disturbance observer is applied to estimate the exogenous perturbation. Second, a robust dynamic sliding mode controller integrated with the estimated values of disturbances is presented by a combination of a proportional–integral–derivative (PID) sliding surface and super twisting technique to compensate for the effect of these perturbations on the system. In addition, the stability of a control system is established by Lyapunov theory. A numerical simulation was performed and compared to recently alternative methods. An excellent tracking performance and superior stability of the attitude and altitude control of UAVs, exhibiting a fast response, good adaptation, and no chattering effect in the simulation results proved the robustness and effectiveness of the proposed method.

The construction of a six-degree-of-freedom (6-DOF) model for the composite motion of the actual mechanical structure (defined as an all-true composite motion model) of unmanned aerial vehicles (UAVs) is a prerequisite for achieving stable control of rotorcraft UAVs. Therefore, this paper proposes a construction approach for a nonlinear 6-DOF model of quadrotor and dual-rotor coaxial UAVs based on all-true composite motion. Two types of attitude–altitude control systems for rotorcraft based on a self-optimizing intelligent proportional–integral–derivative (PID) control method are constructed. Three-dimensional geometric models of the two rotorcraft types, incorporating their physical characteristics, are built. The attitude responses to different pulse width modulation (PWM) inputs are tested, thereby verifying the accuracy of the all-true composite model and analyzing the stability of the two types of UAVs. Furthermore, two types of attitude–altitude control inner loop controllers are designed, and the intelligent PID control algorithm is used to optimize the control parameters. Further verification of the robustness of the optimized parameters is carried out, and the designed attitude controllers are verified via experiment using a turntable. The simulation and experimental results show that the proposed all-true composite motion model and controller design method can accurately simulate the dynamic characteristics of the two types of UAVs and maintain stable attitude control, thus providing a valuable reference for the accurate attitude control of rotorcraft UAVs based on all-true composite motion.

This thesis describes the main research activity developed in a three years PhD program on flight dynamics. Optimization and UAVs flight control have been the main focus with methodological contributions on optimization, numerical and experimental work. Unmanned Aerial Vehicles (UAV) captured the attention of both research and industrial worlds as a replacement for expensive human-piloted vehicles. In the last decade, they became widely used for several applications in which humans could be unnecessary or in some cases too in danger. Many laboratories in the area of flight control, but also in the areas of robotics and control engineering in general, made significant research experiences on quadrotors. A collaboration between University of Naples Parthenope and the Second University of Naples is aimed at designing and using UAVs for educational and research purposes. More than one quadrotor was built, tested in flight and used as a platform for testing flight control and navigation systems. Several optimization problems may be encountered in the design of an UAV. During the design phase, they arise from the choice of the hardware, the design and layout of the structure, the aerodynamics. On the other hand, for the Guidance Navigation and Control system, the management of single or fleets of UAVs requires the solution of many non-linear optimization problems. For this reason a multi-objective general purpose optimization software has been developed, integrating evolutionary methods, as genetic algorithm and ant colony, with game theory paradigms, as Nash and Stackelberg equilibria. These methods have been primarily used to solve trajectory optimization problems with the scope of searching efficient flight trajectories in the presence of constraints. The thesis is developed around the flight control of a quadrotor UAV. The following are the main steps of the work described in this thesis: - dynamic and aerodynamic modelling oriented to flight control design; - development of a distributed general purpose optimization software implementing Game Theory based paradigms and Ant Colony algorithm hybridization; - Application of the above optimization methods to trajectory planning; - Numerical simulations and flight experiments. In Chapter 2, the quadrotor platform is described, together with the mathematical modelling and the design of the low level flight control system (attitude and speed control). In Chapter 3 the structure of the general purpose optimization software, mainly focused on the game theory layer and the ant colony algorithm is presented. In Chapter 4 the objectives of the optimization software are described and solved. Finally, in the Chapter 5, numerical simulations and flight tests are are shown.

Control and path planning are two essential and challenging issues in quadrotor unmanned aerial vehicle (UAV). In this paper, an approach for moving around the nearest obstacle is integrated into an artificial potential field (APF) to avoid the trap of local minimum of APF. The advantage of this approach is that it can help the UAV successfully escape from the local minimum without collision with any obstacles. Moreover, the UAV may encounter the problem of unreachable target when there are too many obstacles near its target. To address the problem, a parallel search algorithm is proposed, which requires UAV to simultaneously detect obstacles between current point and target point when it moves around the nearest obstacle to approach the target. Then, to achieve tracking of the planned path, the desired attitude states are calculated. Considering the external disturbance acting on the quadrotor, a nonlinear disturbance observer (NDO) is developed to guarantee observation error to exponentially converge to zero. Furthermore, a backstepping controller synthesized with the NDO is designed to eliminate tracking errors of attitude. Finally, comparative simulations are carried out to illustrate the effectiveness of the proposed path planning algorithm and controller.

Quadrotor Unmanned Aerial Vehicle (UAV) is an unstable system, so it needs to be controlled efficiently and intelligently. Moreover, due to its non-linear, coupled, and under-actuated nature, the quadrotor has become an important research platform to study and validate various control theories. Different control approaches have been used to control the quadrotor UAV. In this context, a comprehensive study of different control schemes is presented in this research. First, an overview of the working and different applications of quadrotor UAVs is presented. Second, a mathematical model of the quadrotor is discussed. Later, the experimental results of various existing control techniques are discussed and compared. The various control schemes discussed and described for quadrotors are; Proportional Integral and Derivative (PID), Linear Quadratic Regulator (LQR), H-infinity ( H ∞ ), Sliding Mode Control (SMC), Feedback Linearization (FBL), Model Predictive Control (MPC), Fuzzy Logic Control (FLC), Artificial Neural Network (ANN), Iterative Learning Control (ILC), Reinforcement Learning Control (RLC), Brain Emotional Learning Control (BELC), Memory Based Control (MBC), Nested Saturation Control (NSC), and Hybrid Controllers (HC). Comparison is done among all the control techniques and it is concluded that the hybrid control method gives improved results. This survey presents a broad overview of the state-of-the-art in UAV design, control, and implementation for real-life applications.

With the increasing demand for unmanned aerial vehicles (UAVs) in both military and civilian applications, critical safety issues need to be specially considered in order to make better and wider use of them. UAVs are usually employed to work in hazardous and complex environments, which may seriously threaten the safety and reliability of UAVs. Therefore, the safety and reliability of UAVs are becoming imperative for development of advanced intelligent control systems. The key challenge now is the lack of fully autonomous and reliable control techniques in face of different operation conditions and sophisticated environments. Further development of unmanned aerial vehicle (UAV) control systems is required to be reliable in the presence of system component faults and to be insensitive to model uncertainties and external environmental disturbances. This thesis research aims to design and develop novel control schemes for UAVs with consideration of all the factors that may threaten their safety and reliability. A novel adaptive sliding mode control (SMC) strategy is proposed to accommodate model uncertainties and actuator faults for an unmanned quadrotor helicopter. Compared with the existing adaptive SMC strategies in the literature, the proposed adaptive scheme can tolerate larger actuator faults without stimulating control chattering due to the use of adaptation parameters in both continuous and discontinuous control parts. Furthermore, a fuzzy logic-based boundary layer and a nonlinear disturbance observer are synthesized to further improve the capability of the designed control scheme for tolerating model uncertainties, actuator faults, and unknown external disturbances while preventing overestimation of the adaptive control parameters and suppressing the control chattering effect. Then, a cost-effective fault estimation scheme with a parallel bank of recurrent neural networks (RNNs) is proposed to accurately estimate actuator fault magnitude and an active fault-tolerant control (FTC) framework is established for a closed-loop quadrotor helicopter system. Finally, a reconfigurable control allocation approach is combined with adaptive SMC to achieve the capability of tolerating complete actuator failures with application to a modified octorotor helicopter. The significance of this proposed control scheme is that the stability of the closed-loop system is theoretically guaranteed in the presence of both single and simultaneous actuator faults.

A quadrotor UAV (unmanned aerial vehicle) has four symmetrically distributed propellers, which are small in size, high in stability and simple in operation. In recent years, the integrated level and load capacity of a quadrotor UAV have been continuously improved. However, the quadrotor UAV is an underactuated system, and it is poor in anti-disturbance control. Especially when a quadrotor UAV is used in canyons, ocean and other areas that are accompanied with strong wind environment. Therefore, on the basis of summarizing the current key technologies and application of quadrotor UAV, this paper focuses on its dynamics modeling and anti-disturbance control method of a quadrotor UAV. The corresponding experiments are performed to validate the presented control methods.

In this paper, a robust trajectory tracking control strategy using disturbance observer is developed for a quadrotor Unmanned Aerial Vehicle (UAV) in the presence of external aerodynamic disturbance. Considering the external aerodynamic disturbance force, a backstepping control scheme is designed for the position subsystem to ensure that the quadrotor UAV can track the reference position signal rapidly. In order to obtain the strong robustness, nonlinear disturbance observers are introduced to the position control and attitude control, which are used to estimate the unknown aerodynamic disturbances. The simulation results show that the proposed control strategy can make the quadrotor UAV track position signal and yaw angle signal quickly and smoothly with guaranteeing the stability of roll angle and pitch angle, which has strong robustness to external disturbance.

This paper presents a model predictive controller (MPC) for position control of a vertical take-off and landing (VTOL) tail-sitter unmanned aerial vehicle (UAV) in hover flight. A ‘cross’ configuration quad-rotor tail-sitter UAV is designed with the capabilities for both hover and high efficiency level flight. The six-degree-of-freedom (DOF) nonlinear dynamic model of the UAV is built based on aerodynamic data obtained from wind tunnel experiments. The model predictive position controller is then developed with the augmented linearized state-space model. Measured and unmeasured disturbance model are introduced into the modeling and optimization process to improve disturbance rejection ability. The MPC controller is first verified and tuned in the hardware-in-loop (HIL) simulation environment and then implemented in an on-board flight computer for real-time indoor experiments. The simulation and experimental results show that the proposed MPC position controller has good trajectory tracking performance and robust position holding capability under the conditions of prevailing and gusty winds.