Improving 3D Path Tracking of Unmanned Aerial Vehicles through Optimization of Compensated PD and PID Controllers

The development of quadrotor unmanned aerial vehicles (QUAVs) is a growing field due to their wide range of applications. QUAVs are complex nonlinear systems with a chaotic nature that require a controller with extended dynamics. PD and PID controllers can be successfully applied when the parameters are accurate. However, this parameterization process is complicated and time-consuming; most of the time, parameters are chosen by trial and error without guaranteeing good performance. The originality of this work is to present a novel nonlinear mathematical model with aerodynamic moments and forces in the Newton–Euler formulation, and identify metaheuristic algorithms applied to parameter optimization of compensated PD and PID controls for tracking the trajectories of a QUAV. Eight metaheuristic algorithms (PSO, GWO, HGS, LSHADE, LSPACMA, MPA, SMA and WOA) are reported, and RMSE is used to measure each dynamic performance of the simulations. For the PD control, the best performance is obtained with the HGS algorithm with an RMSE = 0.037247252379126. For the PID control, the best performance is obtained with the HGS algorithm with an RMSE = 0.032594309723623. Trajectory tracking was successful for the QUAV by minimizing the error between the desired and actual dynamics.

Compound robust tracking control of disturbed quadrotor unmanned aerial vehicles: A data-driven cascade control approach

In this paper, the robust trajectory tracking control problem of disturbed quadrotor unmanned aerial vehicles (UAVs) with disturbances, uncertainties and unmodeled dynamics is addressed, by devising a novel compound robust tracking control (CRTC) approach via data-driven cascade control technique. By deploying the data-driven philosophy, a data-based sliding-mode surface is proposed, and thereby contributing to strong adaptability to nonlinearity and model-unknown properties of the UAVs. By utilizing the backstepping technique, virtual control strategy and a novel cascaded compound robust PD control structure, the attitude and position subsystems are efficiently cohered such that a data-driven cascaded compound robust controller containing both PD control and sliding-mode control can be developed to conquer the lumped disturbances induced by uncertainties, disturbances and unmodeled dynamics. Eventually, the asymptotic convergence of the tracking errors with respect to both attitude and position subsystems can be guaranteed rigorously. Simulation studies on a prototype quadrotor UAV are performed to evaluate the efficacy and superiority of the devised CRTC method.

Fault-Tolerant Control of Quadrotor UAVs Based on Back-Stepping Integral Sliding Mode Approach and Iterative Learning Algorithm

In this paper, a fault-tolerant control system based on back-stepping integral sliding mode controller (BISMC) is designed and analyzed for both nonlinear translational and rotational subsystems of the quadrotor unmanned aerial vehicles (UAVs). The novelty of this paper is about combination of a classic controller with a repetitive algorithm to reduce the response time to actuator faults and have better tracking performance. The actuator fault is defined based on the loss of effectiveness and bias fault. Next, the iterative learning control algorithm (ILCA) is used to compensate for the unknown fault input according to previous recorded experiences. In the normal condition (without actuators fault), BISMC can force the actual trajectories toward the desired commands and reduce chattering about control signals, and in the presence of the actuators fault or external disturbances, the mentioned learning algorithm can incline the accuracy of the tracking performance and compensate for the occurred error. The Lyapunov theory illustrates that the proposed control strategy can stabilize the system despite the actuators’ fault and external disturbances. The simulation results show the effectiveness of the proposed scheme in comparison with another method.

RBFNN-Based Singularity-Free Terminal Sliding Mode Control for Uncertain Quadrotor UAVs

In this article, a singularity-free terminal sliding mode (SFTSM) control scheme based on the radial basis function neural network (RBFNN) is proposed for the quadrotor unmanned aerial vehicles (QUAVs) under the presence of inertia uncertainties and external disturbances. Firstly, a singularity-free terminal sliding mode surface (SFTSMS) is constructed to achieve the finite-time convergence without any piecewise continuous function. Then, the adaptive finite-time control is designed with an auxiliary function to avoid the singularity in the error-related inverse matrix. Moreover, the RBFNN and extended state observer (ESO) are introduced to estimate the unknown disturbances, respectively, such that prior knowledge on system model uncertainties is not required for designing attitude controllers. Finally, the attitude and angular velocity errors are finite-time uniformly ultimately bounded (FTUUB), and numerical simulations illustrated the satisfactory performance of the designed control scheme.

Distributed wind measurements with multiple quadrotor unmanned aerial vehicles in the atmospheric boundary layer

In this study, a fleet of quadrotor unmanned aerial vehicles (UAVs) is presented as a system to measure the spatial distribution of atmospheric boundary layer flow. The big advantage of this approach is that multiple and flexible measurement points in space can be sampled synchronously. The algorithm to obtain horizontal wind speed and direction is designed for hovering flight phases and is based on the principle of aerodynamic drag and the related quadrotor dynamics. During the FESST@MOL campaign at the boundary layer field site (Grenzschichtmessfeld, GM) Falkenberg of the Lindenberg Meteorological Observatory – Richard Assmann Observatory (MOL-RAO), 76 calibration and validation flights were performed. The 99 m tower equipped with cup and sonic anemometers at the site is used as the reference for the calibration of the wind measurements. The validation with an independent dataset against the tower anemometers reveals that an average accuracy of σrms<0.3 m s−1 for the wind speed and σrms,ψ<8∘ for the wind direction was achieved. Furthermore, we compare the spatial distribution of wind measurements with the fleet of quadrotors to the tower vertical profiles and Doppler wind lidar scans. We show that the observed shear in the vertical profiles matches well with the tower and the fluctuations on short timescales agree between the systems. Flow structures that appear in the time series of a line-of-sight measurement and a two-dimensional vertical scan of the lidar can be observed with the fleet of quadrotors and are even sampled with a higher resolution than the deployed lidar can provide.

Quadrotor UAV control : online learning approach

Quadrotor unmanned aerial vehicles (UAVs) are recognized to be capable of various tasks including search and surveillance for their agilities and small sizes. This thesis proposes a simple and robust trajectory tracking controller for a quadrotor UAV utilizing online Iterative Learning Control (ILC) that is known to be effective for tasks performed repeatedly. Based on a nonlinear model which considers basic aerogynamic and gyroscopic effects, the quadrotor UAV model is simulated to perform a variety of maneuvering such as take-off, landing, smooth translation and horizontal and spatial circular trajectory motions, PD online ILCs wirh switching gain (SPD ILCs) are studies, tested and compared. Simulation results prove the ability of the online ILCs to successfully perform certain missions in the presence of considerably large disturbances and SPD ILCs can obtain faster convergence rates.

Quadrotor UAV control : online learning approach

Quadrotor unmanned aerial vehicles (UAVs) are recognized to be capable of various tasks including search and surveillance for their agilities and small sizes. This thesis proposes a simple and robust trajectory tracking controller for a quadrotor UAV utilizing online Iterative Learning Control (ILC) that is known to be effective for tasks performed repeatedly. Based on a nonlinear model which considers basic aerogynamic and gyroscopic effects, the quadrotor UAV model is simulated to perform a variety of maneuvering such as take-off, landing, smooth translation and horizontal and spatial circular trajectory motions, PD online ILCs wirh switching gain (SPD ILCs) are studies, tested and compared. Simulation results prove the ability of the online ILCs to successfully perform certain missions in the presence of considerably large disturbances and SPD ILCs can obtain faster convergence rates.

Automatic Control Optimization for Large-Load Plant-Protection Quadrotor

In this article, methods for the attitude control optimization of large-load plant-protection quadrotor unmanned aerial vehicles (UAVs) are presented. Large-load plant-protection quadrotors can be defined as quadrotors equipped with sprayers and a tank containing a large amount of water or pesticide, allowing the quadrotors to water plants or spray pesticide during flight. Compared to the control of common small quadrotors, two main points need to be considered in the control of large-load plant-protection quadrotors—first, the water in the tank gradually diminishes during flight and the physical parameters change during this process. Second, the size and mass of the rotors are especially large, which greatly slows the response rate of the rotors. We present an extended-state reinforcement learning (RL) algorithm to solve these problems. The moment of inertia (MOI) of the three axes and the dynamic response constant of the rotors are included in the state list of the quadrotor during the training process, so that the controller can learn these changes in the models. The controlling laws are automatically generated and optimized, which greatly simplifies the tuning process compared to those of traditional control algorithms. The controller in this article is tested on a 10 kg class large-load plant-protection quadrotor, and the flight performance verifies the effectiveness of our work.