Quadrotor Aerial Robot Research Articles

Classical PID Control of A Quadcopter UAV Under Variable Load Conditions

In recent years, the application of UAVs has become more and more extensive, resulting in a number of special operation UAVs. Quadrotor aerial robots have become one of the most popular vehicles at home and abroad because of their small size, light weight, simple structure, low cost, low requirements for takeoff and landing, and good maneuverability. UAVs are required to perform different tasks in the current complex environment, so variable loads are inevitable, and the stability of the UAV body is especially important when performing variable loads, and there are many ways to help and verify the attitude of the UAV during flight. In this paper, in order to more intuitively see the self-generated stability of the UAV under variable load conditions, the dynamics model of the UAV will be established first, and the simulation results show that the movement of the UAV has a very good stability. However, in the previous research, it is not possible to visualize the stability and flight state of the UAV when it is flying under variable load, this paper will make the UAV keep its own flight stability under two kinds of loads through the PID design model. At the same time, the function of the UAV's own mass change during flight is found, and the design function is used to show the stability curve of the UAV when it flies with its own changing mass. Through the final simulation and calculation, the experiment found that through the PID control, the flight state of the UAV when flying with variable load can be observed more intuitively, and the stability state of the UAV when flying can also be observed.

Adaptive neural network based compensation control of quadrotor for robust trajectory tracking

SummaryNeural network (NN) methods have become increasingly efficient and applicable in control. This article presents a novel nested control strategy based on adaptive radial basis function neural networks (RBFNN) and NN supervised control embedded with integrator back stepping (IBS) for a robust position and attitude trajectory tracking of quadrotor aerial robot in presence of modeling uncertainties, sensing noise, and external bounded disturbance. The control philosophy is derived from the decentralized inverse dynamics, where the adaptive RBFNN is adopted for the outer loop to approximate the unknown external disturbance, and adaptively compensate for their effect. In conjunction with a supervised control that stabilizes the attitude in the inner loop by adaptively compensating the unknown and unmodeled uncertainties, avoiding initial instability of the attitude during NN convergence. Furthermore, noise is attenuated by an adaptive extended Kalman filter. Stability analysis was elaborated using Lyapunov stability theory. Simulation of adaptive RBFNN control philosophy proved robustness and effectiveness compared to proportional integral derivative, IBS, and offline decentralized convolution neural network algorithms as it generates the control law by guarantying a fast convergence of parameters, better external disturbance compensation, and noise attenuation. The proposed scheme of robust tracking was validated.

Variable Baseline and Flexible Configuration Stereo Vision Using Two Aerial Robots.

In this work, a new method for aerial robot remote sensing using stereo vision is proposed. A variable baseline and flexible configuration stereo setup is achieved by separating the left camera and right camera on two separate quadrotor aerial robots. Monocular cameras, one on each aerial robot, are used as a stereo pair, allowing independent adjustment of the pose of the stereo pair. In contrast to conventional stereo vision where two cameras are fixed, having a flexible configuration system allows a large degree of independence in changing the configuration in accordance with various kinds of applications. Larger baselines can be used for stereo vision of farther away targets while using a vertical stereo configuration in tasks where there would be a loss of horizontal overlap caused by a lack of suitable horizontal configuration. Additionally, a method for the practical use of variable baseline stereo vision is introduced, combining multiple point clouds from multiple stereo baselines. Issues from using an inappropriate baseline, such as estimation error induced by insufficient baseline, and occlusions from using too large a baseline can be avoided with this solution.

Non‐linear adaptive tracking control for quadrotor aerial robots under uncertain dynamics

This paper presents the design, analysis, and implementation of an adaptive backstepping controller for underactuated quadrotors to track time-varying trajectories with parameter uncertainties. Quadrotor systems are subject to complicated non-linearity and coupling dynamics, so the ignorance of parameter uncertainties may cause performance degradation and even instability. With the concept of nominal input, the uncertain mass and inertia are decoupled from the lifting force and moment torque. By utilizing the backstepping technique, the design procedure with adaptive laws for dynamic parameters is proposed to ensure stability and convergence of tracking errors to the origin asymptotically. The proposed control scheme is extended to control a quadrotor with velocity motor input while the motor coefficients and geometric parameters are handled by the adaptive laws. A trajectory generation for the proposed adaptive tracking controller is addressed subsequently. The proposed controller requires only the position and orientation of the quadrotor with the twice-differentiable trajectories, while previous work demanded acceleration information. Simulation and experimental results are illustrated to show the efficacy of tracking performance for object transportation.

Modeling and Simulation of Perching With a Quadrotor Aerial Robot With Passive Bio-inspired Legs and Feet

Abstract Perching in unmanned aerial vehicles is appealing for reconnaissance, monitoring, communications, and charging. This paper focuses on modeling, simulation, and control of bioinspired perching in unmanned aerial vehicles on cylindrical objects, which will be used for future planning and control research. A modular approach is taken where the quadrotor, legs, feet, and toes are modeled separately and then integrated to form a complete simulation system. New models of these components consider kinematics and dynamics of each element and their coupling through tendons that provide actuation. The integrated model is assembled to simulate a physical prototype and then validated based upon physical experiments to provide calibration. Simulation results evaluate the validated model performing perching with different gripper-perch alignments. The simulation environment developed in this research provides a foundation to research control approaches for use with the discussed passive perching mechanism. The simulation was validated to capture the dynamics of the real perching mechanism. This platform will be used in future work to develop a control approach that will be implemented in a quadrotor system to land and take-off from a perch in a reliable manner.

Vision-Based Adaptive Neural Positioning Control of Quadrotor Aerial Robot

In this paper, a new vision-based adaptive control algorithm is proposed for the positioning of a quadrotor aerial robot (QAR) with an onboard pin-hole camera. First, the transformation between the position tracking error and image projection error is constructed through the spherical projection method, and then the regulation of the position error is achieved indirectly by stabilizing the image projection error. To overcome the challenge that the dynamics of QAR is physically underactuated, a backstepping-based approach that synthesizes the Lipschitz condition and natural saturation of the inverse tangent function is proposed. In the proposed adaptive controller, an optimized adaptive neural network (NN) means is designed, where only the square of the NN weight matrix’s maximum singular value, not the weight matrix itself, is estimated. Moreover, to facilitate practical application, a novel inertial matrix estimator is introduced in the tuning laws, so that the accurate QAR rotation inertial information is not required. By Lyapunov theory, it is proved that the image projection error converges to an adjustable region of zero asymptotically. The effectiveness of the proposed algorithm has been confirmed by the experimental results.

Nonlinear Vision-Based Observer for Visual Servo Control of an Aerial Robot in Global Positioning System Denied Environments

This paper presents a nonlinear vision-based observer to estimate 3D translational position and velocity of a quadrotor aerial robot for closed-loop, position-based, visual-servo control in global positioning system (GPS)-denied environments. The method allows for motion control in areas where GPS signals are weak or absent, for example, inside of a building. Herein, the robot uses a low-cost on-board camera to observe at least two feature points fixed in the world frame to self-localize for feedback control, without constraints on the altitude of the robot. The nonlinear observer described takes advantage of the geometry of the perspective projection and is designed to update the translational position and velocity in real-time by exploiting visual information and information from an inertial measurement unit. One key advantage of the algorithm is it does not require constraints or assumptions on the altitude and initial estimation errors. Two new controllers based on the backstepping technique that take advantage of the estimator's output are described and implemented for trajectory tracking. The Lyapunov method is used to show asymptotic stability of the closed-loop system. Simulation and experimental results from an indoor environment where GPS localization is not available are presented to demonstrate feasibility and validate the performance of the observer and control system for hovering and tracking a circular trajectory defined in the world frame.

Leveraging experience for robust, adaptive nonlinear MPC on computationally constrained systems with time-varying state uncertainty

This paper presents a robust-adaptive nonlinear model predictive control (MPC) technique that leverages past experiences to achieve tractability on computationally constrained systems. We propose a robust extension of the Experience-driven Predictive Control (EPC) algorithm via a Gaussian belief propagation strategy that computes an uncertainty set, bounding the evolution of the system state in the presence of time-varying state uncertainty. This uncertainty set is used to tighten the constraints in the predictive control formulation via a chance-constrained approach, thereby providing a probabilistic guarantee of constraint satisfaction. The parameterized form of the controllers produced by EPC coupled with online uncertainty estimates ensures that this robust constraint satisfaction property persists, even as the system switches controllers and experiences variations in the uncertainty model. We validate the online performance and robust constraint satisfaction of the proposed Robust EPC algorithm through a series of trials with a simulated ground robot and three experimental platforms: (1) a small quadrotor aerial robot executing aggressive maneuvers in wind with degraded state estimates, (2) a skid-steer ground robot equipped with a laser-based localization system, and (3) a hexarotor aerial robot equipped with a vision-based localization system.

Design and application of an adaptive backstepping sliding mode controller for a six-DOF quadrotor aerial robot

SUMMARYThe quadrotor aerial robot is a complex system and its dynamics involve nonlinearity, uncertainty, and coupling. In this paper, an adaptive backstepping sliding mode control (ABSMC) is presented for stabilizing, tracking, and position control of a quadrotor aerial robot subjected to external disturbances. The developed control structure integrates a backstepping and a sliding mode control approach. A sliding surface is introduced in a Lyapunov function of backstepping design in order to further improve robustness of the system. To attenuate a chattering problem, a saturation function is used to replace a discontinuous sign function. Moreover, to avoid a necessity for knowledge of a bound of external disturbance, an online adaptation law is derived. Particle swarm optimization (PSO) algorithm has been adopted to find parameters of the controller. Simulations using a dynamic model of a six degrees of freedom (DOF) quadrotor aerial robot show the effectiveness of the approach in performing stabilization and position control even in the presence of external disturbances.

Vision-Based Object Tracking Using an Optimally Positioned Cluster of Mobile Tracking Stations

This paper presents a novel, highly capable strategy for utilizing a multirobot network to track a moving target. This method optimizes the configuration of mobile tracking stations in order to produce the position estimate for a target object that yields the smallest estimation error, even when the sensor performance varies. This is verified in both simulation and physical experiments using groups of two and three quadrotor aerial robots as mobile tracking stations controlled using a cluster control approach. These quadrotors track and follow an autonomous robot using only the data provided by the quadrotors’ onboard cameras. This results in a simple, robust system that can accurately follow a moving object.

Optimisation of Trajectories for Wireless Power Transmission to a Quadrotor Aerial Robot

Unmanned aircraft such as multirotors are typically limited in endurance by the need to minimise weight, often sacrificing power plant mass and therefore output. Wireless power transmission is a method of delivering power to such aircraft from an off-vehicle transmitter, reducing weight whilst ensuring long-term endurance. However, transmission of high-powered lasers in operational scenarios carries significant risk. Station-keeping of the laser spot on the receiving surface is crucial to both ensuring the safety of the procedure and maximising efficiency. This paper explores the use of trajectory optimisation to maximise the station-keeping accuracy. A multi-agent model is presented, employing a quadrotor unmanned rotorcraft and energy transmission system, consisting of a two-axis gimbal, camera sensor and laser emitter. Trajectory is parametrised in terms of position and velocity at the extremes of the flight path. The optimisation operates on a cost function which considers target range, beam angle of incidence and laser spot location on the receiving surface. Several cases are presented for a range of variables in the trajectory and different conditions in the model and optimisation algorithm. Results demonstrate the viability of this approach in minimising station-keeping errors.

A Polytopic Observer Design Approach for Landing Control of a Quadrotor UAV

In this paper, a constructive procedure to design functional unknown input observer for nonlinear continuous time systems under the Polytopic Takagi-Sugeno framework (also known as multiple models systems) is proposed. Applying the Lyapunov theory, Linear Matrix Inequalities (LMI)s conditions are deduced which are solved for feasibility to obtain observer design matrices. To reject the effect of unknown input, classical approach of decoupling the unknown input for the linear case is used. A comparative study between single and Polytopic Lyapunov function is made in order to prove the relaxation effect of the Multiple functions. A solver based solution is then proposed.It will be shown through applicative example (a Quadrotor Aerial Robots Landing) that even if the proposed LMIs solver based solution may look conservative, an adequate choice of the solver makes it suitable for the application of the proposed approach.

A new polytopic approach for the unknown input functional observer design

ABSTRACTIn this paper, a constructive procedure to design Functional Unknown Input Observers for nonlinear continuous time systems is proposed under the Polytopic Takagi-Sugeno framework. An equivalent representation for the nonlinear model is achieved using the sector nonlinearity transformation. Applying the Lyapunov theory and the attenuation, linear matrix inequalities conditions are deduced which are solved for feasibility to obtain the observer design matrices. To cope with the effect of unknown inputs, classical approach of decoupling the unknown input for the linear case is used. Both algebraic and solver-based solutions are proposed (relaxed conditions). Necessary and sufficient conditions for the existence of the functional polytopic observer are given. For both approaches, the general and particular cases (measurable premise variables, full state estimation with full and reduced order cases) are considered and it is shown that the proposed conditions correspond to the one presented for standard linear case. To illustrate the proposed theoretical results, detailed numerical simulations are presented for a Quadrotor Aerial Robots Landing and a Waste Water Treatment Plant. Both systems are highly nonlinear and represented in a T-S polytopic form with unmeasurable premise variables and unknown inputs.

Ground and Aerial Mutual Localization Using Anonymous Relative-Bearing Measurements

We present a decentralized algorithm for estimating mutual poses (relative positions and orientations) in a group of mobile robots. The algorithm uses relative-bearing measurements, which, for example, can be obtained from onboard cameras, and information about the motion of the robots, such as inertial measurements. It is assumed that all relative-bearing measurements are anonymous; i.e., each specifies a direction along which another robot is located but not its identity. This situation, which is often ignored in the literature, frequently arises in practice and remarkably increases the complexity of the problem. The proposed solution is based on a two-step approach: in the first step, the most likely unscaled relative configurations with identities are computed from anonymous measurements by using geometric arguments, while in the second step, the scale is determined by numeric Bayesian filtering based on the motion model. The solution is first developed for ground robots in SE (2) and then for aerial robots in SE (3). Experiments using Khepera III ground mobile robots and quadrotor aerial robots confirm that the proposed method is effective and robust w.r.t. false positives and negatives of the relative-bearing measuring process.

Modeling and Adaptive Backstepping Control for Quadrotor Robots with Blade Flapping

Dynamics modeling and control for quadrotor aerial robots are considered in this paper. The threedimensional dynamics model of the quadrotor is first derived using the Newton-Euler approach. When the propelling rotor rotating with respect to the moving robot, the rotating blades could be deflected with an effect on the aerial vehicles known as flapping. The modeling with blade flapping is considered. Then a nonlinear stable adaptive control with flapping parameters estimation is proposed using the backstepping technique for the position and yaw angle trajectory tracking. Finally, computer simulation is used to validate the performance of the proposed control strategy. In the simulation study, the desired trajectory is constructed using the cubic spline interpolation.

Modelling and PID Control of a Quadrotor Aerial Robot

This paper elucidates the modeling of a + quadrotor configuration aerial vehicle and the design of its attitude and altitude controllers. The aircraft model consists of four fixed pitch angle propeller, each driven by an electric DC motor. The hovering flight of the quadrotor is governed by the Newton-Euler formulation. The attitude and altitude controls of the aircraft were regulated using heuristically tuned (Proportional-Integral-Derivative) PID controller. It was numerically simulated via Simulink that a PID controller was sufficient to bring the aircraft to the required altitude whereas the attitude of the vehicle is adequately controlled by a PD controller.

Adaptive Sliding Mode Control for Quadrotor Aerial Robot with Ι Type Configuration

This paper presents an adaptive sliding mode control for trajectory tracking of quad rotor aerial robot with I type configuration. A continuous adaptive dynamic vector is used to eliminate the chattering phenomenon in the sliding mode control and the asymptotic stability is achieved. On the base of a nonlinear dynamic model of the quad rotor with I configuration, adaptive sliding mode nonlinear controller is synthesized for the purpose of stabilization and trajectory tracking of quad rotor. In order to validate the robustness of the proposed control method, the model deviations are modeled. It behaves perfectly at the reference altitude and trajectory track in simulation.

2A1-A02 クアッドロータにおける可変アームの実現とその安定性解析シミュレーション(飛行ロボット・メカトロニクス(1))

2A1-A02 クアッドロータにおける可変アームの実現とその安定性解析シミュレーション(飛行ロボット・メカトロニクス(1))

The Design and Implementation of 4 DOF Control of the Quadrotor

This paper deals with the design and implementation of a quadrotor aerial robot. At first, simplified quadrotor model was created. The model describes quadrotor as a MIMO (Multiple Inputs Multiple Outputs) system, which is further divided into four independent SISO (Single Inputs Single Outputs) systems. These systems correspond to rotational movements in all robot axes and one translation movement, which represents flight altitude. Special attention was paid to the identification of actuators. On the basis of the model 4 DOF (Degree of Freedom) stabilization was designed. The stabilization consists of four independent controllers, all of them use auxiliary controlled variable. Inner loops, that control velocities, are controlled by fast P controllers and outer loops use PI controllers due to suppression of disturbances. Designed stabilization was tested by simulation and on a real flying model as well. The paper also describes hardware and software solution.

Controller Design of Quadrotor Aerial Robot

This paper deduced the nonlinear dynamic model of a quadrotor aerial robot, which was a VTOL (vertical tale-off and landing) unmanned air vehicle. Since that is a complex model with the highly nonlinear multivariable strongly coupled and under-actuated property, the controller design of it was very difficult. Aimed at attaining the excellent controller, the whole system can be divided into three interconnected parts: attitude subsystem, vertical subsystem, position subsystem. Then nonlinear control strategy of them has been described, such as SDRE and Backstepping. The controller design was presented to stabilize the whole system. Through simulation result indicates, the various models have shown that the control law stabilize a quadrotor aerial robot with good tracking performance and robotness of the system.