Small Unmanned Helicopter Research Articles

Hovering Control on a Variable-Parameters Model of a Small Unmanned Helicopter Based on a Backstepping Sliding Mode Method

In order to strengthen the hovering precision of a small unmanned helicopter with a manipulator for completing a hovering operation, in this study, a double-loop control strategy of position and attitude is designed. In the position loop, a sliding mode controller is proposed. It attains high precision and strong robustness under continuous disturbance. With the manipulator continuously operating during helicopter hovering, the overall center of gravity and the moment of inertia of the system will also change. Therefore, a backstepping sliding mode controller is proposed in the attitude loop for controlling the hovering attitude of the helicopter. The high precision and strong robustness of the control system are proven. The digital simulation presents a position steady-state error of less than 2% under constant disturbance, and the hovering attitude angle fluctuation amplitude of the helicopter is less than 0.05 rad.

Dynamic Double Event-Triggered Anti-Disturbance Tracking Control for a 2-DOF Small Unmanned Helicopter.

This article devises a new dynamic double event-triggered anti-disturbance tracking control scheme for a 2-degree of freedom (DOF) laboratory helicopter subject to external time-varying disturbances and load fluctuation by using the generalized proportional-integral observer technique. The helicopter system is separated into two subsystems in the proposed control method, i.e., the pitch subsystem and the yaw subsystem. Each subsystem includes a discrete-time dynamic double event-triggering mechanism (DDETM), and the control laws of the two subsystems are independent of each other. There are two triggering conditions in the designed double triggering mechanism: one is designed based on the system states and the other is based on the lumped disturbance estimation. These two triggering conditions form a competitive relationship such that the controller updates the control signal as long as one of the triggering conditions is satisfied. Theoretical analysis is provided for achieving the better communication and control performance of the proposed DDETM-based robust control method. Through rigorous stability analysis, it is proved that the closed-loop hybrid system is globally ultimately bounded. At last, numerical simulations show that the suggested control strategy not only reduces the event-triggering number, but also improves the initial dynamic performance of the system.

Modeling and control strategy of small unmanned helicopter rotation based on deep learning

Unmanned Aerial Vehicle (UAV) can serve as a substitute for workers in some hazardous environments, but the presence of ground effects makes UAVs prone to hardware damage during the recycling process. To study the rotation phenomenon of small UAV landing process and propose control strategy, the study completes the rotation modeling of small unmanned helicopter based on deep learning algorithm and proposes the control strategy. The results show that the CNN with ReLU function has the best performance, and the model converges in the 5th iteration with this function, while the model with Sigmoid function converges in the 36th iteration, and the fitting effect of the rotation model constructed by the study is higher than that of the traditional rotation model. The actual trajectory of the research-constructed rotation model starts to coincide with the expected trajectory at the 5th s of the landing process, while the actual trajectory of the Cheeseman-Bennett model starts to coincide with the expected trajectory only at the 26th s of the landing process. Under the control strategy model proposed in the study, the roll angle and pitch angle of UAV are stabilized at 46s, and the fluctuation of yaw angle is also minimal. The rotation model constructed in the study can completely reflect the rotation process of the small UAV, and the designed control system can help the UAV recover stability faster.

Aerodynamic and Structural Design Procedures Supported by Fabrication and Flight Testing of a Small Unmanned Helicopter

In this work, typical design, production, and testing procedures for a small unmanned helicopter are explained and performed. In doing so, preliminary sizing of the helicopter and three main disciplines are conducted: aerodynamic analytical and numerical simulations, power calculations, and structure analysis assessment. First, a thorough survey is implemented to obtain the trends for the maximum take-off weight versus some design constraints such as rotor diameter, motor power, payload, and empty weight. Performance calculation results are obtained to figure out all aspects that correspond to the specified mission. The designed rotor geometry along with the aerodynamic characteristics and flight performance variables is then validated using the blade element theory and numerical simulations. Second, based on the power curves obtained for different flight regimes, an electric brushless motor is selected. The numerical simulations (Computational Fluid Dynamics) analysis is used to enhance the selection which implies that the motor power should be greater than 5.4 kW to overcome the drag forces. The motor power selection corresponds to a maximum rotor pitch angle of 15∘ and a maximum rotor speed of 1450 RPM. Then, the aerodynamic loads are used as an input for the structural analysis using one-way coupling of fluid–structure interaction (FSI) and consequently designing the internal structure of the blade. Eventually, the internal structure manufactured using carbon fiber-reinforced polymer (CFRP) by applying a combined technique between wet layup and compression molding. The blade is statically tested compared with numerical finite element model results. The fuselage structure along with hub and tail units is manufactured and assembled with the existing on-shelf components to examine the helicopter lift capability with different payloads up to 9 kg. The results show that the detailed design process is significant for manufacturing such blades and the helicopter is capable of lifting off the ground with various payloads depending on the rotor pitch angles (8∘, 12∘, and 15∘) at a constant rotor speed of 1450 RPM.

An improved dynamic model identification method for small unmanned helicopter

PurposeThe purpose of this paper is to introduce an improved system identification method for small unmanned helicopters combining adaptive ant colony optimization algorithm and Levy’s method and to solve the problem of low model prediction accuracy caused by low-frequency domain curve fitting in the small unmanned helicopter frequency domain parameter identification method.Design/methodology/approachThis method uses the Levy method to obtain the initial parameters of the fitting model, uses the global optimization characteristics of the adaptive ant colony algorithm and the advantages of avoiding the “premature” phenomenon to optimize the initial parameters and finally obtains a small unmanned helicopter through computational optimization Kinetic models under lateral channel and longitudinal channel.FindingsThe algorithm is verified by flight test data. The verification results show that the established dynamic model has high identification accuracy and can accurately reflect the dynamic characteristics of small unmanned helicopter flight.Originality/valueThis paper presents a novel and improved frequency domain identification method for small unmanned helicopters. Compared with the conventional method, this method improves the identification accuracy and reduces the identification error.

Attitude tracking backstepping control for small unmanned helicopters based on adaptive neural network under unknown environment disturbances

To solve the attitude tracking control problem of small unmanned helicopters under unknown bounded disturbances, an attitude tracking backstepping controller based on adaptive radial basis function (RBF) neural network disturbance compensation is proposed in this paper. The unknown disturbance is estimated online and in real time through RBF neural network with a novel gradient descent weight update rate. A novel attitude tracking backstepping controller based on virtual control variables is designed, and the system stability is analyzed using the Lyapunov method. The experimental verification of the backstepping controller is carried out on the three-degrees-of-freedom helicopter. The experiments show the effectiveness and advancedness of the proposed adaptive neural network backstepping controller in suppressing unknown external disturbances compared with the robust adaptive integral backstepping.

Small unmanned helicopter modeling method based on a hybrid kernel function PSO-LSSVM

Small unmanned helicopter modeling method based on a hybrid kernel function PSO-LSSVM

An Intelligence Attitude Controller Based on Active Disturbance Rejection Control Technology for an Unmanned Helicopter

To solve the problems of complex parameter tuning of traditional active disturbance rejection controller (ADRC) and low adaptive ability of extended state observer, this paper designs a novel intelligent attitude controller based on active disturbance rejection control technology. The adaptive radial basis function neural network can improve the adaptability of the extended state observer, and the fuzzy control can adjust the parameters of the state error nonlinear control law online. The intelligent ADRC controller can eliminate the influence of internal and external disturbances on the unmanned helicopter, and reduce the difficulty of controller parameter tuning. Based on the intelligent ADRC controller, this paper designs an intelligent attitude control system for a small unmanned helicopter and completes the attitude control test. The corresponding test results demonstrate that the intelligent attitude controller has excellent anti-disturbances, robustness and tracking ability, and the control effect is better than that of the traditional ADRC controller.

Robust Adaptive Control for a Small Unmanned Helicopter Using Reinforcement Learning.

This article presents a novel adaptive controller for a small-size unmanned helicopter using the reinforcement learning (RL) control methodology. The helicopter is subject to system uncertainties and unknown external disturbances. The dynamic unmodeling uncertainties of the system are estimated online by the actor network, and the tracking performance function is optimized via the critic network. The estimation error of the actor-critic network and the external unknown disturbances are compensated via the nonlinear robust component based on the sliding mode control method. The stability of the closed-loop system and the asymptotic convergence of the attitude tracking error are proved via the Lyapunov-based stability analysis. Finally, real-time experiments are performed on a helicopter control testbed. The experimental results show that the proposed controller achieves good control performance.

System identification and improved internal model control for yaw of unmanned helicopter

AbstractThe sea breeze is a low‐frequency disturbance that severely damages the stability of small unmanned helicopters operating over the sea, especially for the yaw control, which is highly sensitive to disturbance. General internal model control is an appropriate method for dealing with this kind of operation conditions, whereas conventional internal model control cannot eliminate the tracking errors between a nominal model and a real model. In coping with unknown dynamics and low‐frequency gust disturbances for small helicopters, this paper proposes a novel robust controller constructed with system identification and integrator‐based improved general internal model. As a refinement of the conventional frame, the proposed control scheme extends the applicable scope of a controlled plant from a priori known dynamic to an unknown dynamic. Furthermore, under the proposed controller, it is guaranteed that the tracking error between the actual model and the nominal model converges to zero asymptotically. Finally, the effectiveness and advantage of the proposed control scheme are verified through comparative practical flight tests.

Adaptive smooth disturbance observer-based fast finite-time attitude tracking control of a small unmanned helicopter

In this paper, a novel control scheme, which includes an improved fast finite-time adaptive backstepping controller based on a newly designed adaptive smooth disturbance observer, is presented for the attitude tracking of a small unmanned helicopter system subject to lumped disturbances. First, an adaptive smooth disturbance observer (ASDO) is proposed to approximate the lumped disturbances, which owns the characteristics of smooth output, fast finite-time convergence, and adaptability to disturbances. Then, a finite-time backstepping control protocol is constructed to achieve the attitude tracking objective. By virtue of our newly proposed inequality, a singularity-free virtual control law is designed. To tackle the “explosion of complexity” problem, a fast finite-time command filter (FFTCF) is utilized to estimate the virtual control signals and their derivatives. In addition, an auxiliary dynamic system is introduced to attenuate the impact of the errors caused by FFTCF estimation. Moreover, an adaptive law with σ-modification term is developed to compensate the ASDO approximation error. Theoretical analysis proves that all signals of the closed-loop system are fast finite-time bounded, while the attitude tracking errors fast converge to a small region of the origin in finite time. Finally, two contrastive numerical simulations are carried out to validate the effectiveness and superiority of the designed control scheme.

Data driven adaptive robust attitude control for a small size unmanned helicopter

In this paper, the data driven based control design problem for a small size unmanned helicopter is investigated. The helicopter is subjected to the effects associated with modeling uncertainties and unknown external disturbances. A new model free robust adaptive attitude control law is proposed with the combination of the model-free-adaptive-control (MFAC) design, the immersion and invariance (I&I) approach, and the super-twisting technique. Different from the most existing linear or nonlinear model-dependent control designs for unmanned helicopters, the proposed control strategy requires very less model knowledge. The controller depends mainly on the input and output data of the helicopter, and no boundedness knowledge of the unknown disturbances is required. Stability of the closed-loop system has been proved. Real-time flight experiments have been performed on the self-developed helicopter testbed only with the available feedback data of the helicopter. The experimental results have validated the good robustness of the proposed control methodology.

Design of Yaw Controller for a Small Unmanned Helicopter Based on Improved ADRC

Yaw control is significant to the attitude control of small unmanned helicopters (SUHs). Since the existing robust control method cannot be applied to the SUH with unknown dynamics and disturbances, this paper proposes an improved active disturbance rejection control (IADRC) to solve the problem. The IADRC obtains the optimal solution of the actuator gain ([Formula: see text]) by gradient descent. Besides, this paper summarizes some experiences during the tuning process of ADRC, which significantly reduces the difficulty of designing ADRC. Finally, the experimental results show that the proposed method is better than the traditional PID in robust and tracking control performance.

Development of the automatic autorotation landing control system by using a small unmanned helicopter

Development of the automatic autorotation landing control system by using a small unmanned helicopter

Fixed Time Disturbance Observer Based Sliding Mode Control for a Miniature Unmanned Helicopter Hover Operations in Presence of External Disturbances

This paper presents a novel fixed time sliding mode disturbance observer (FTSMDO) based second-order fixed time sliding mode control (FTSMC) for small scale unmanned helicopter to do hover operations in the presence of external disturbances. Sliding mode control is insensitive to matched uncertainties but sensitive to mismatched uncertainties. The novel FTSMDO exactly estimates the total mismatched uncertainties effecting the sliding mode control performance. To counter the mismatched uncertainties a new sliding surface augmented by the total mismatched disturbance approximated by the FTSMDO is defined. The second-order FTSMC forces the system states to reach the sliding surface in fixed time and then on the sliding surface the system states exponentially converge to the desired equilibrium point. The control performance is compared with the disturbance observer based sliding mode controller (DOB-SMC) and shows superior performance.

Control of a small helicopter with linear matrix inequality-based design assuring stability and performance

This article focuses on linear matrix inequality-based controller designs that can achieve stabilization and reference tracking for a small unmanned helicopter at various flight conditions. A nonlinear mathematical model of a small-scale helicopter is constructed. Then trim conditions are found and linearized around different equilibrium points. Local [Formula: see text] controllers are designed at trim conditions based on the local linear models. The pointwise controllers achieve local stability and performance, but fail at stabilization and tracking over the full envelope. A scheduling controller is built by blending the local controller outputs. In addition, grid-based [Formula: see text] controllers are designed at each operating point with common Lyapunov function. This allows controller scheduling between the adjacent design points with guaranteed stability and performance across the design envelope. Based on the family of linear systems which are obtained from the nonlinear model, an affine parameter-dependent model is built to exploit the approximate linear parameter dependency. Then, a parameter-dependent linear parameter varying controller is synthesized for the affine parameter-dependent model. Although local performance is satisfactory for all given design methods, local [Formula: see text] controllers and affine parameter-dependent controller cannot yield satisfactory performance over the full flight envelope apart from the grid-based controller with common Lyapunov function approach.

A novel dynamic identification model for small unmanned helicopters

A novel dynamic identification model for small unmanned helicopters

Active disturbance rejection control for small unmanned helicopters via Levy flight-based pigeon-inspired optimization

PurposeThe purpose of this paper is to propose a control approach for small unmanned helicopters, and a novel swarm intelligence algorithm is used to optimize the parameters of the proposed controller.Design/methodology/approachSmall unmanned helicopters have many advantages over other unmanned aerial vehicles. However, the manual operation process is difficult because the model is always instable and coupling. In this paper, a novel optimized active disturbance rejection control (ADRC) approach is presented for small unmanned helicopters. First, a linear attitude model is built in hovering condition according to small perturbation linearization. To realize decoupling, this model is divided into two parts, and each part is equipped with an ADRC controller. Finally, a novel Levy flight-based pigeon-inspired optimization (LFPIO) algorithm is developed to find the optimal ADRC parameters and enhance the performance of controller.FindingsThis paper applies ADRC method to the attitude control of small unmanned helicopters so that it can be implemented in practical flight under complex environments. Besides, a novel LFPIO algorithm is proposed to optimize the parameters of ADRC and is proved to be more efficient than other homogenous methods.Research limitations/implicationsThe model of proposed controller is built in the hovering action, whereas it cannot be used in other flight modes.Practical implicationsThe optimized ADRC method can be implemented in actual flight, and the proposed LFPIO algorithm can be developed in other practical optimization problems.Originality/valueADRC method can enhance the response and robustness of unmanned helicopters which make it valuable in actual environments. The proposed LFPIO algorithm is proved to be an effective swarm intelligence optimizer, and it is convenient and valuable to apply it in other optimized systems.

3D Digitisation of Large-Scale Unstructured Great Wall Heritage Sites by a Small Unmanned Helicopter

The ancient Great Wall of China has long suffered from damage due to natural factors and human activities. A small low-cost unmanned helicopter system with a laser scanner and a digital camera is developed to efficiently visualize the status of the huge Great Wall area. The goal of the system is to achieve 3D digitisation of the large-scale Great Wall using a combination of fly-hover-scan and flying-scan modes. However, pose uncertainties of the unmanned helicopter could cause mismatching among point clouds acquired by each hovering-scan. This problem would become more severe as the target area becomes larger and more unstructured. Therefore, a hierarchical optimization framework is proposed in this paper to achieve 3D digitisation of the large-scale unstructured Great Wall with unpredictable pose uncertainties of the unmanned helicopter. In this framework, different optimization methodologies are proposed for the fly-hover-scan and flying-scan modes, respectively, because different scan modes would result in different features of point clouds. Moreover, a user-friendly interface based on WebGL has been developed for 3D model visualization and comparison. Experimental results demonstrate the feasibility of the proposed framework for 3D digitisation of the Great Wall segments.

A real-time H∞ cubature Kalman filter based on SVD and its application to a small unmanned helicopter

In this work, the main objective is to study a real-time H∞ cubature Kalman filter (CKF). Robust CKF combines robust principle and CKF, many advantages in state estimation for a small unmanned helicopter are presented, employing singular value decomposing. Meanwhile, a real-time principle is introduced to overcome the problem of costly calculating time. Unlike many other novel CKF is verified in simulation, the proposed CKF is implemented on the small scale unmanned helicopter's inertial navigation system. A serial of experimental results on a small-scale helicopter confirm the effectiveness scheme and the accuracy state estimation of the proposed filter in high-maneuverability flight.