Control Of Quadrotor Unmanned Aerial Vehicle Research Articles

Highly maneuvrable quadrotor unmanned aerial vehicle control with unknown disturbances

For the problems of highly maneuverable quadrotor unmanned aerial vehicle (UAV), such as drastic attitude changes, unknown model disturbances, and accurate velocity tracks, the paper proposes new model compensation control based on SO(3) according to the idea of model compensation control (MCC) theory and SO(3) theory. Accurate estimation for unknown disturbances and precise extraction of the reference signal are achieved by introducing compensation function observer and high-order differentiator. For the attitude solution, the desired attitude solutions based on acceleration are designed, which guarantees the range of accelerations is safely limited. At the same time, the relationship between attitude control, velocity control, and the desired attitude solutions is given. The proposed method not only has the global stability of SO(3) and the anti-disturbance stability of MCC but also improves the maneuverability of UAV. Through verifications of simulations and experiments, the proposed method not only has no loss of anti-disturbance performance but also significantly reduces overshooting and response time compared to traditional MCC methods. Providing a control strategy for highly maneuverable UAV.

Event-triggered finite-time adaptive neural network control for quadrotor UAV with input saturation and tracking error constraints

In this article, an event-triggered finite-time adaptive neural network control tracking strategy is proposed for quadrotor unmanned aerial vehicle (UAV) with input saturation and error constraints. Firstly, the radial basis function neural networks (RBFNNs) are adopted to identify the unknown uncertainty of quadrotor UAV model from the installation errors, gyroscope errors and so on. An auxiliary equation is constructed to deal with input physical saturation from the actuator motors. Additionally, by combining the performance function and error transformation, the issue of error constraint is solved. Based on the Lyapunov stability theory and event-triggered mechanisms, a finite-time adaptive neural network scheme is developed to ensure that the closed-loop quadrotor UAV system is semi-globally practically finite-time stable, and save the computation, resources, and transmission load. Finally, the simulation results illustrate the good tracking performance of quadrotor UAV by using the proposed control strategy.

Dynamic event-triggered neuroadaptive fault-tolerant control of quadrotor UAV with a novel cosine kernel

The fault-tolerant control (FTC) for trajectory tracking of a quadrotor unmanned aerial vehicle (UAV) has attracted researchers. The non-linear model of the UAV, coupled with model uncertainties, external disturbances, and actuator failures, requires the function approximation of the lumped non-linearity for controller design. One of the most efficient ways to approximate non-linearity is using radial basis function neural networks (RBFNNs). To date, RBFNNs have been formulated, trained, and used directly for function approximation, which requires considerable computation to derive the control laws. The proposed control and parameter estimation laws approximate the non-linearity by using RBFNNs indirectly. The proposed laws with virtual parameter estimation do not require the actual formulation of RBFNNs and their weights, thus saving on computational resources. For kernel optimization, the Gaussian kernels with exponential terms in RBFNNs are replaced by cosine kernels with algebraic terms, which shows faster convergence as per simulation results. To save on communication bandwidth, static event-triggering communication mechanisms (SECM) and dynamic event-triggering mechanisms (DECM) have been proposed. As DECM works on dynamically changing variables, it saves more communication bandwidth, as tested in simulation. Lyapunov stability analysis proves that errors are uniformly ultimately bounded (UUB). The performance of the proposed algorithm has been tested through numerical simulations, which show superior performance when compared with similar studies. The proposed algorithm has been validated in a real-time Gazebo simulator.

Finite Time-Adaptive Full-State Quantitative Control of Quadrotor Aircraft and QDrone Experimental Platform Verification

This paper proposes a novel adaptive finite-time controller for a quadrotor unmanned aerial vehicle (UAV) model with stochastic perturbations and parameter-unknown terms, under the constraints of a state-constrained system. The controller is designed based on full-state quantization, where the error system is defined to be a function of the quantized error signal. An adaptive method is employed to address the quadrotor UAV system model with nonlinear terms and unknown perturbations. The controller utilizes Barrier Lyapunov function (BLF) bounds with adaptive effective time performance to ensure full-state constraint of the system. The stability of the system is proven using Lyapunov’s stability theorem. The effectiveness of the designed full-state constrained controller for quadrotor UAV based on full-state quantization is verified through a physical experimental simulation platform.

Formation Control of Quadrotor UAVs

In recent years, the rapid development of quadrotor unmanned aerial vehicles (UAVs) has resulted in their widespread applications across various scenarios. However, single UAVs encounter multiple challenges during mission execution. Consequently, an increasing number of tasks are being conducted in the form of UAV formations. This paper focuses on quadrotor UAVs and proposes a formation control scheme that can be validated through simulation and experimentation. The research begins with the dynamic modeling of quadrotor UAVs and the design of an experimental platform for UAVs. The formation structure of UAVs is then developed based on the Leader-Follower theory, and a multi-type PID controller is employed to design the formation control algorithm.

Adaptive Backstepping Control of Quadrotor UAVs with Output Constraints and Input Saturation

The control performance of quadrotor unmanned aerial vehicles (UAVs) in complex environments can be affected by external disturbances and other factors. In this paper, an adaptive neural network backstepping controller based on the barrier Lyapunov function (BLF) is designed for a quadrotor UAV with internal uncertainties, input–output constraints and external disturbances. Radial basis function neural networks are used to approximate the uncertainties in the dynamic model of the UAV, while the minimum parameter learning method is combined to accelerate the adjustment speed of neural network weights. A robust term is designed to balance the total system disturbance and improve the anti-interference performance. The BLF is used to handle the output constraint so that the constrained parameters cannot break the predefined constraints. An auxiliary system is introduced to solve input saturation and avoid the dependence of tracking error on the input amplitude in the method of approximating input saturation using the smoothing function. The stability of the control system is demonstrated by the Lyapunov method. The simulation results show that the proposed method has high tracking accuracy compared with the backstepping dynamic surface control method, and the input and output are in the predefined range.

Bioinspired backstepping sliding mode control and adaptive sliding innovation filter of quadrotor unmanned aerial vehicles

Quadrotor unmanned aerial vehicles have become the most commonly used flying robots with wide applications in recent years. This paper presents a bioinspired control strategy by integrating the backstepping sliding mode control technique and a bioinspired neural dynamics model. The effects of both disturbances and system and measurement noises on the quadrotor unmanned aerial vehicle control performance have been addressed in this paper. The proposed control strategy is robust against disturbances with guaranteed stability proven by the Lyapunov stability theory. In addition, the proposed control strategy is capable of providing smooth control inputs under noises. Considering the modeling uncertainties, the adaptive sliding innovation filter is integrated with the proposed control to provide accurate state estimates to improve tracking effectiveness. Finally, the simulation results demonstrate that the proposed control strategy provides satisfactory tracking performance for a quadrotor unmanned vehicle operating under disturbances and noises.

Finite-Time Height Control of Quadrotor UAVs

The quadrotor Unmanned Aerial Vehicle (UAV) belongs to an open-loop unstable nonlinear system, which also has the characteristics of underdrive, strong coupling and external disturbance. In the height control of quadrotor UAVs, the traditional sliding mode control (SMC) and PID methods cannot quickly and effectively eliminate disturbance effects caused by gust, aerodynamic drag and other factors, which indicates that the quadrotor UAV cannot return to its predetermined trajectory. To this end, this paper proposes a dual closed-loop finite-time height control method for the quadrotor UAV. The proposed method is able to estimate and compensate for the disturbance in the height control and make up for the lack of anti-disturbance ability in the control process. More specifically, a finite-time Extended State Observer (ESO) and a finite-time super-twisting controller are designed for the velocity control system to compensate for the total disturbance and track the rapidly changing expected signal. An integral sliding mode controller is designed for the height control system. Simulation results show that the proposed method can reduce the chattering phenomenon of traditional SMC and improve both control accuracy and convergence speed.

Active fault-tolerant control of quadrotor UAVs with nonlinear observer-based sliding mode control validated through hardware in the loop experiments

Multirotor unmanned aerial vehicles (UAV) are highly prone to motor faults, which can arise from defective motors or damaged propellers. Motor faults severely change the multirotor UAV’s dynamics and therefore endanger flight safety and reliability since the controller loses its efficiency. To cope with such crucial problems, a fault-tolerant controller is proposed in this paper for full control of a quadrotor UAV with motor faults. The proposed fault-tolerant approach consists of the nonlinear observer technique and the Sliding Mode Control (SMC). The designed novel nonlinear observer predicts the effects of motor faults on quadrotor dynamics and it is augmented with an SMC to create a fault-tolerant controller. In addition, the nonlinear observer enhances the robustness of the SMC against the uncertainties and disturbances acting on the quadrotor during flight. Any actuator fault will be treated as a disturbance detected by the nonlinear observer and will be attenuated directly by the proposed SMC. The disturbance attenuation capability achieved by the nonlinear observer decreases the amount of control action expected from the SMC, which results in advanced robustness without sacrificing the nominal control performance. The performance of the proposed nonlinear observer SMC (NOSMC) is demonstrated through simulations and testbed experiments. The results show that the proposed fault-tolerant controller effectively recovers the full control of the quadrotor with motor faults up to 40% while tracking the predefined trajectory and rotation angles as desired.

A Comparative Study for Control of Quadrotor UAVs

Modeling and controlling highly nonlinear, multivariable, unstable, coupled and underactuated systems are challenging problems to which a unique solution does not exist. Modeling and control of Unmanned Aerial Vehicles (UAVs) with four rotors fall into that category of problems. In this paper, a nonlinear quadrotor UAV dynamical model is developed with the Newton–Euler method, and a control architecture is proposed for 3D trajectory tracking. The controller design is decoupled into two parts: an inner loop for attitude stabilization and an outer loop for trajectory tracking. A few attitude stabilization methods are discussed, implemented and compared, considering the following control approaches: Proportional–Integral–Derivative (PID), Linear–Quadratic Regulator (LQR), Model Predictive Control (MPC), Feedback Linearization (FL) and Sliding Mode Control (SMC). This paper is intended to serve as a guideline work for selecting quadcopters’ control strategies, both in terms of quantitative and qualitative considerations. PID and LQR controllers are designed, exploiting the model linearized about the hovering condition, while MPC, FL and SMC directly exploit the nonlinear model, with minor simplifications. The fast dynamics ensured by the SMC-based controller together with its robustness and the limited estimated command effort of the controller make it the most promising controller for quadrotor attitude stabilization. The outer loop consists of three independent PID controllers: one for altitude control and the other two, together with a dynamics’ inversion, are entitled to the computation of the reference attitude for the inner loop. The capability of the controlled closed-loop system of executing complex trajectories is demonstrated by means of simulations in MATLAB/Simulink®.

Adaptive Fault-Tolerant Tracking Control of Quadrotor UAVs against Uncertainties of Inertial Matrices and State Constraints

This paper presents a study on a quadrotor unmanned aerial vehicle (UAV) fault-tolerant control scheme. According to the attitude model and safety control of the aircraft under the uncertainty of inertial matrix, the attitude state constraint by reinforcement learning is designed to ensure safety. Even if the boundary is crossed, it can be pulled back to the boundary by means of a designed penalty function with reinforcement learning. Meanwhile, in order to inhibit the oscillation caused by immediate reward as usual, an adaptive update law is proposed. Furthermore, considering the coupled actuator fault and system input saturation due to uncertainty of inertial matrix, the Nussbaum-type function is utilized in this work to handle this challenge, which likely causes the singularity of inertia matrix. As a consequence, combined with the Lyapunov stability theory, it is confirmed that the proposed FTC scheme ensures that all the closed-loop signals are bounded. Simulation results are carried out to illustrate the effectiveness and advantage of the proposed control scheme.

Safety Control for Quadrotor UAV Against Ground Effect and Blade Damage

This article proposes a safety control scheme for quadrotor unmanned aerial vehicle (UAV) to counteract ground effect and blade damage. The impacts of ground effect and blade damage are quantitatively analyzed in the established model. The proposed safety control scheme consists of position and attitude loops. Specifically, a proportional and derivative controller is designed as a baseline control with acceleration feedforward in the position loop. A disturbance observer (DO) using the measured Euler angles is exploited to estimate the lumped disturbance forces. The developed DO can effectively mitigate the estimation deviation caused by the angle tracking error. Subsequently, differential flatness theory is used to obtain the Euler angle references and the derivatives. In the attitude loop, sliding mode control and fixed-time sliding mode observer (SMO) are integrated to deal with ground effect and blade damage promptly. To demonstrate the effectiveness of the proposed safety control scheme, flight experiments are carried out, where the quadrotor UAV flies close to the rugged ground in the presence of blade damage.

Fixed-time robust trajectory tracking control for quadrotor UAV with disturbances

Trajectory tracking control of nonlinear system with disturbances has been extensively investigated. A novel fixed-time robust trajectory tracking control scheme for quadrotor unmanned aerial vehicle (UAV) with disturbances is proposed in this paper. First, the quadrotor system is split into position and attitude loops. With the purpose of restraining coupling disturbances on position loop forced by imperfect attitude tracking, a novel fixed-time unknown input observer (UIO) is devised to accurately estimate the disturbances, with the unique feature that does not need the upper bound of disturbances or the derivatives. Second, taking unmeasurable velocity and unknown external disturbances into account for attitude loop, a fixed-time extended state observer (ESO) is proposed to precisely observe them within a fixed time. Third, considering control accuracy and convergence rate, robust fixed-time controllers are, respectively, designed for position and attitude loops, and the convergence time of overall system is independent of system initial states. Then, a comprehensive stability proof for the two-loop system is presented with a combination of bi-limit homogeneity and Lyapunov technique. Finally, simulations and comparisons are performed to demonstrate the effectiveness of the designed control scheme.

Command filter-based adaptive fuzzy integral backstepping control for quadrotor UAV with input saturation

This paper presents an adaptive fuzzy integral backstepping control strategy combined with command filter to solve trajectory tracking problem of quadrotor unmanned aerial vehicle (UAV). By using Fuzzy logic system to approximate uncertain disturbance. The tracking error integral term is introduced to reduce the steady-state tracking error. The backstepping approach is combined with the command filter to solve the “computational explosion” problem, and the error compensation mechanism is introduced to reduce the influence of filtering error. The hyperbolic tangent function is used to solve the problem of input saturation. By choosing the appropriate Lyapunov function to calculate the adaptive law for tuning the adjustable parameters of the fuzzy logic system. Morever, the stability of the controller is analyzed by using Lyapunov stability theory. Numerical simulation results are provided to illustrate the effectiveness and superiority of the proposed control method.

Minimum Energy Control of Quadrotor UAV: Synthesis and Performance Analysis of Control System with Neurobiologically Inspired Intelligent Controller (BELBIC)

There is a strong trend in the development of control systems for multi-rotor unmanned aerial vehicles (UAVs), where minimization of a control signal effort is conducted to extend the flight time. The aim of this article is to shed light on the problem of shaping control signals in terms of energy-optimal flights. The synthesis of a UAV autonomous control system with a brain emotional learning based intelligent controller (BELBIC) is presented. The BELBIC, based on information from the feedback loop of the reference signal tracking system, shows a high learning ability to develop an appropriate control action with low computational complexity. This extends the capabilities of commonly used fixed-value proportional–integral–derivative controllers in a simple but efficient manner. The problem of controller tuning is treated here as a problem of optimization of the cost function expressing control signal effort and maximum precision flight. The article introduces several techniques (bio-inspired metaheuristics) that allow for quick self-tuning of the controller parameters. The performance of the system is comprehensively analyzed based on results of the experiments conducted for the quadrotor model.

Modeling and PID control of quadrotor UAV based on machine learning

Abstract The aim of this article was to discuss the modeling and control method of quadrotor unmanned aerial vehicle (UAV). In the process of modeling, mechanism modeling and experimental testing are combined, especially the motor and propeller are modeled in detail. Through the understanding of the body structure and flight principle of the quadrotor UAV, the Newton–Euler method is used to analyze the dynamics of the quadrotor UAV, and the mathematical model of the UAV is established under the small angle rotation. Process identifier (PID) is used to control it. First, the attitude angle of the model is controlled by PID, and based on this, the speed in each direction is controlled by PID. Then, the PID control of the four rotor aircraft with the center of gravity offset is simulated by MATLAB. The results show that the pitch angle and roll angle can be controlled by 5 degrees together without center of gravity deviation, and the PID can effectively control the control quantity and achieve the desired effect in a short time. Classical BP algorithm, classical GA-BP algorithm, and improved GA-BP algorithm were trained, respectively, with a total of 150 sets of training data, training function uses Levenberg-Marquardt (trainlm), and performance function uses mean squared error (MSE). In the background of the same noise, the improved GA-BP algorithm has the highest detection rate, classical GA-BP algorithm is the second, and classical BP algorithm is the worst. The simulation results show that the PID control law can effectively control the attitude angle and speed of the rotor UAV in the case of center of gravity deviation.

Nonlinear Extended State Observer Based Prescribed Performance Control for Quadrotor UAV with Attitude and Input Saturation Constraints

In this paper, a prescribed performance control scheme of the quadrotor unmanned aerial vehicle (UAV) under attitude and input saturation constraints is introduced. According to the underactuated feature, the quadrotor UAV system can be decomposed into an underactuated subsystem and a fully actuated subsystem. With the feedback linearization technique, a single nonlinear extended state observer (ESO) is proposed, and multiple observations are utilized to estimate both matched and unmatched disturbances, which not only can obtain a uniform convergence, but also reduces the complexity of the observer’s parameter adjustment. To improve system stability, an input saturation algorithm for each single rotor is introduced to modify the final control output. In addition, the limited attitude for the quadrotor UAV is also considered as a saturation constraint in the control scheme with a compensation auxiliary system. On this basis, dynamic surface control (DSC) with prescribed performance is adopted to guarantee the bounded convergence and steady-state error. All state errors of the closed-loop system are proven to be uniformly bounded using the Lyapunov theory, and the simulation results are given to demonstrate the stability, effectiveness, and superiority of the proposed control strategies at last.

Position Control of Quadrotor UAV Based on Cascade Fuzzy Neural Network

In this article, a cascade fuzzy neural network (FNN) control approach is proposed for position control of quadrotor unmanned aerial vehicle (UAV) system with high coupling and underactuated. For the attitude loop with limited range, the FNN controller parameters were trained offline using flight data, whereas for the position loop, the method based on FNN compensation proportional-integral-derivative (PID) was adopted to tune the system online adaptively. This method not only combined the advantages of fuzzy systems and neural networks but also reduced the amount of calculation for cascade neural network control. Simulations of fixed set point flight and spiral and square trajectory tracking flight were then conducted. The comparison of the results showed that our method had advantages in terms of minimizing overshoot and settling time. Finally, flight experiments were carried out on a DJI Tello quadrotor UAV. The experimental results showed that the proposed controller had good performance in position control.

Prescribed performance–based tracking control for quadrotor UAV under input delays and input saturations

In this paper, a semi-global trajectory tracking control scheme is proposed for quadrotor unmanned aerial vehicle (UAV) under time-varying input delays, input saturations, and bounded disturbances. Initially, in order to ensure the desirable control performance that tracking error is limited in a prescribed bound, an error transformation function is presented to transform the constrained problem into a stabilization one. Then, under backstepping control scheme, a state prediction method combined with a dynamic surface control (DSC) is presented to compensate for the effects of input delays, and an auxiliary system is designed to tackle the input saturations, which are further to deal with input delays and input saturations in both position system and attitude system of quadrotor UAV. Particularly, the Euler angle is constrained to avoid the singular points of the attitude system. Besides, based on Lyapunov stability theory, it can be verified that all signals in the closed-loop system of the quadrotor UAV are semi-globally ultimately uniformly bounded (UUB) and the tracking errors are restrained in the prescribed bounds. Finally, by choosing some appropriate parameters, the simulation results based on quadrotor UAV model are given to verify the effectiveness of the proposed control scheme.

Integrated path planning and trajectory tracking control for quadrotor UAVs with obstacle avoidance in the presence of environmental and systematic uncertainties: Theory and experiment

This paper proposes an innovative integrated path planning and trajectory tracking control framework for a quadrotor unmanned aerial vehicle (UAV) in the presence of environmental and systematic uncertainties to achieve integrated guidance and control. Firstly, in order to perform real-time path planning, a computationally cost-effective planning algorithm is designed to find an optimal and smooth path while avoiding both static and dynamic obstacles. Then, by employing the pure-pursuit path following approach, the generated geometric path is converted to a trajectory profile related to time, which serves as the reference commands for the low-level trajectory tracking controller. Finally, a novel adaptive sliding mode trajectory tracking controller is proposed to compensate model uncertainties and maintain the desired tracking performance for the studied quadrotor UAV. With the proposed adaptive schemes, overestimation of uncertain parameters can be avoided, which further contributes to avoiding control chattering of the system. The performance of the proposed framework is validated through comparative simulation and experimental tests based on a quadrotor UAV subject to model uncertainties and environmental obstacles, which confirms the effectiveness and superiority of the proposed approach for practical applications.