Unmanned Helicopter System Research Articles

Adaptive Finite-Time Constrained Attitude Stabilization for an Unmanned Helicopter System under Input Delay and Saturation

Adaptive Finite-Time Constrained Attitude Stabilization for an Unmanned Helicopter System under Input Delay and Saturation

Time-varying gain extended state observer-based adaptive optimal control for disturbed unmanned helicopter

In this paper, the robust adaptive optimal tracking control problem is addressed for the disturbed unmanned helicopter based on the time-varying gain extended state observer (TVGESO) and adaptive dynamic programming (ADP) methods. Firstly, a novel TVGESO is developed to tackle the unknown disturbance, which can overcome the drawback of initial peaking phenomenon in the traditional linear ESO method. Meanwhile, compared with the nonlinear ESO, the proposed TVGESO possesses easier and rigorous stability analysis process. Subsequently, the optimal tracking control issue for the original unmanned helicopter system is transformed into an optimization stabilization problem. By means of the ADP and neural network techniques, the feedforward controller and optimal feedback controller are skillfully designed. Compared with the conventional backstepping approach, the designed anti-disturbance optimal controller can make the unmanned helicopter accomplish the tracking task with less energy. Finally, simulation comparisons demonstrate the validity of the developed control scheme.

Full State Constrained Flight Tracking Control for Helicopter Systems with Disturbances

In this paper, a full state-constrained anti-disturbance dynamic surface control method is proposed for six-degree-of-freedom unmanned helicopter systems under full state constraints and disturbances. Firstly, due to the underactuated characteristics of six-degree-of-freedom unmanned helicopter systems, an input–output feedback linearization method is used to transform the complex nonlinear systems into facilitated-control nonlinear ones. Based on the transformed systems, the nonlinear disturbance-observer-based control, backstepping control and Barrier Lyapunov function methods are used to construct the flight controller via uniting the state constraint control and dynamic surface control technologies. Then, Lyapunov stability theory is adopted for analysing the closed-loop tracking error systems, which confirms that the tracking errors are bounded under the proposed flight control scheme. Finally, a simulation in the MATLAB/Simulink environment verifies that the unmanned helicopter system can constrain all states under the action of the designed controller, with good dynamic performance.

Stealthy replay attack detection of 3-DOF helicopter benchmark system using dynamic watermarking approach

Cyber security for unmanned systems has drawn tremendous research attention in recent years owing to its priority in ensuring system performance and safety. This paper presents a dynamic watermarking-based active detection approach for unmanned helicopter system under stealthy replay attacks. First, the dynamic model of the 3-DOF helicopter and the replay attack model are constructed, respectively. Statistical analysis is conducted using Kalman filter residuals to interpret the concealment of stealthy replay attacks for a system under intrusion. Then, as traditional chi-square detector is unable to detect stealthy replay attacks, an active detector by incorporating dynamic watermarking with the chi-square detector is explored. Finally, by conducting simulations of the detection procedures under different test scenarios comparatively using traditional chi-square detector and the proposed approach, the effectiveness and merits of the watermarking approach is verified sufficiently. The watermarking-based approach is promising in comprehensive detections against stealthy replay attacks for enhancing system security.

Adaptive neural network control of a non‐linear two‐degree‐of‐freedom helicopter system with prescribed performance

AbstractThis study develops a neural network control for a non‐linear two‐degree‐of‐freedom unmanned helicopter system satisfying prescribed performance constraints is developed. By introducing a prescribed performance function to express the system constraints, a constrained tracking control problem is transformed into an equivalent unconstrained stability problem through error transformations. The neural network controller is designed to ensure that the tracking error converges to a small neighborhood of zero and that the prescribed performance constraints are satisfied. Furthermore, the stability and error convergence of the system are analysed through the Lyapunov direct approach. The effectiveness of the proposed control scheme is verified by simulation and experimental results.

Research on attitude control strategy of unmanned helicopter based on hybrid particle swarm optimization algorithm

For the unmanned helicopter attitude control problem, an optimal control strategy based on hybrid particle swarm optimization (PSO) algorithm has been proposed. Firstly, a coupled dynamics model of the system has been established based on the structural characteristics of the unmanned helicopter system; secondly, a gain scheduling control method has been adopted to select multiple system stable operating points with real-time error as the scheduling variable, and a gain scheduling attitude controller has been designed; further, the inertia weights and learning factors are dynamically adjusted on the basis of the traditional PSO algorithm, and a hybrid PSO algorithm has been proposed for unmanned helicopter attitude control; finally, the improved controller has been compared and verified by the semi-physical. The experimental results show that the improved algorithm improves the attitude control effect and anti-interference capability of the unmanned helicopter, resulting in a reduction of the adjustment time by about 3.8s and the maximum steady-state amplitude by about 0.154°.

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.

Research on the Control Method of Unmanned Helicopter Under the Background of Artificial Intelligence

A synopsis of a multidisciplinary research initiative focused on critical strategies for the genuine independent flight of tiny, vertical flying take-off (VTOL) unmanned aerial vehicles (UAVs). The research activities are the flight testbed, a simulation and test environment, and integrated components for onboard navigation, perception, design, and control. The necessity to create an unmanned helicopter system in different new civil applications cannot be overlooked. A highly reliable model may be used in the design, analysis, and implementation. The helicopter is fitted with a reference system for flight test data measurement and recording attitude heading reference system (AHRS) and the accompanying data storage modules. Recently, artificial intelligence-based deep learning (DL) has demonstrated excellent outcomes for a wide range of robotic activities in the areas of perception, planning, location, and management. Its remarkable skills to learn from complex data obtained in actual surroundings make it appropriate for many autonomous robotic applications. At the same time, UHS is currently widely utilized in various civil tasks in security, cinematography, disaster assistance, package delivery, or warehouse management (Unmanned Helicopter System). This paper conducted detailed work on current applications and the most significant advances and their performance and limits for the DL-UHS method. Furthermore, the essential strategies for deep learning are explained in depth — finally, discussing the principal hurdles of applying deep learning for UHS solutions. The proposed DL-UHS enhance outcome to evaluate the control strategies for the unmanned helicopter to achieve the low signal to noise error ratio of 31.3%, the error rate of 33.6%, the high-performance ratio of 91.4%, enhance accurate path planning 97.5%, prediction ratio of 96.3%, less trajectory cost ratio of 17.8% and increased safety tracking rate 93.6% when compared to other popular methods.

Aggressive maneuver oriented integrated fault-tolerant control of a 3-DOF helicopter with experimental validation

In this paper, an integrated fault-tolerant control (FTC) strategy is proposed for unmanned helicopter system under aggressive maneuvers. In order to handle the effect of strong nonlinearities caused by large pitch angle, linear parameter varying (LPV) technique is adopted in the helicopter system modeling. Then, LPV model-based unknown input observer (UIO) design is conducted to simultaneously realize actuator faults and system state estimation, based on which an active fault-tolerant controlleris also constructed. Considering bi-directional robustness interactions between fault as well as state observer and active fault-tolerant controller, an integrated fault-tolerant controller design is further developed. Besides, actuator saturation is also considered in LPV modeling as well as integrated fault-tolerant controller design of the helicopter system. In order to guarantee the robust performance of proposed integrated fault tolerant controller design of helicopter system, energy-to-energy strategy is adopted. And the linear matrix inequality (LMI) toolbox is utilized for gain calculation. Finally, comparative experimental tests are carried out to show the effectiveness and advantages of the proposed FTC strategy.

Randomized algorithms for probabilistic analysis of parametric uncertainties with unmanned helicopters

This paper aims at the development of a randomized algorithm based probabilistic analysis approach for parametric uncertainties in unmanned helicopter systems. The proposed approach is developed considering the stochastic characterization of bounded uncertainty in the system assuming that the plant dynamics are exactly known. This provides a new paradigm for synthesizing the controller gain to solve the problem of trajectory tracking for unmanned. Further, to assess the operation of the proposed randomization algorithm-based probabilistic controller and achieve the controller synthesis, a two degrees of freedom (2DoF) helicopter system is modelled and operated with uncertainties for different trajectories. Besides, the robustness of the controller operating under uncertainties is verified with reachability analysis developed on reach tubes and reach sets of the ellipsoidal method. The results identified the efficiency of the proposed approach with time domain characteristics for both simulation and real-time experiments. Moreover, a comparative assessment of the projected approach with conventional techniques is carried out to validate the controller response on the helicopter model.

Height and attitude tracking control of an unmanned helicopter via a HDOB‐backstepping composite control method

This study studies the height and attitude tracking control problem of an unmanned helicopter system with disturbances. A composite controller based on the combination of harmonic disturbance observers and backstepping technique is proposed to achieve asymptotic tracking for height and attitude. Firstly, harmonic disturbance observers (HDOB) are designed to estimate the unknown disturbances affecting each channel. Then, a composite controller is proposed by integrating the backstepping control technique and the HDOB technique together to achieve the height and attitude tracking goal. Stability analysis of the closed-loop system is given. Finally, numerical simulations are presented to verify the efficiency of the proposed control scheme.

Research on unmanned aerial vehicle modeling and control based on intelligent algorithms

This article designs an automatic flight control system for an unmanned aerial vehicle helicopter. The differential evolution intelligent algorithm is used for a state-space model identification; the differential evolution method has an advantage of choosing initial point randomly. The accuracy of the identified model is verified by comparing the model-predicted responses with the responses collected during flight experiments. The reliability and efficiency of the differential evolution algorithm are demonstrated by the experimental results. A robust controller is designed based on the identified model for the unmanned aerial vehicle helicopter with two-loop control frame: the outer-loop is used to obtain the expected attitude angles through reference path and speed with guidance-based path-following control, and the inner-loop is used to control the attitude angles of helicopter tracking the expected ones with [Formula: see text] loop-shaping method. The greatest common right divisor method is used to choose the weighting matrix in loop shaping, in which the stability margin is larger and has a greater bandwidth of the unmanned aerial vehicle system. Finally, a space spiral curve trajectory tracking simulation is conducted to illustrate the efficiency of the proposed control systems, and the simulation results prove that the unmanned helicopter system achieves a top-level control performance.

High‐order mismatched disturbance rejection control for small‐scale unmanned helicopter via continuous nonsingular terminal sliding‐mode approach

SummaryIn this paper, the problem of finite‐time control for small‐scale unmanned helicopter system with high‐order mismatched disturbance is investigated via continuous nonsingular terminal sliding‐mode control approach. The key idea is to design a novel nonlinear dynamic sliding‐mode surface based on finite‐time disturbance observer. Then, the finite‐time convergence and chattering attenuation capability is guaranteed by the continuous nonsingular terminal sliding‐mode control law. Additionally, rigorous finite‐time stability analysis for the closed‐loop helicopter system is given by means of the Lyapunov theory. Finally, some simulation results demonstrate the effectiveness and predominant properties of the proposed control method for the small‐scale unmanned helicopter even in the presence of high‐order mismatched disturbance.

Adaptive Constrained Control for Uncertain Nonlinear Time-Delay System with Application to Unmanned Helicopter

This paper investigates a class of nonlinear time-delayed systems with output prescribed performance constraint. The neural network and DOB (disturbance observer) are designed to tackle the uncertainties and external disturbance, and prescribed performance function is constructed for the output prescribed performance constrained problem. Then the robust controller is designed by using adaptive backstepping method, and the stability analysis is considered by using Lyapunov-Krasovskii. Furthermore, the proposed method is employed into the unmanned helicopter system with time-delay aerodynamic uncertainty. Finally, the simulation results illustrate that the proposed robust prescribed performance control system achieved a good control performance.

Novel disturbance-observer-based control for systems with high-order mismatched disturbances

ABSTRACTA novel disturbance-observer-based control method is investigated to attenuate the high-order mismatched disturbances. First, a finite-time disturbance observer (FTDO) is proposed to estimate the disturbances as well as the derivatives. By incorporating the outputs of FTDO, the original system is then reconstructed, where the mismatched disturbances are transformed to the matched ones that are compensated by feed-forward algorithm. Moreover, a feedback control law is developed to achieve the stability and tracking performance requirements for the systems. Finally, the proposed composite control method is applied to an unmanned helicopter system. The simulation results demonstrate that the proposed control method exhibits excellent control performance in the presence of high-order matched and mismatched disturbances.

Robust adaptive tracking control for unmanned helicopter with constraints

In this article, a prescribed performance-based adaptive neural network control scheme is proposed for an uncertain small-scale unmanned helicopter system subject to input saturations and output constraints. The radial basis function neural networks are employed to approximate system uncertainties. A nonlinear disturbance observer is developed to tackle input saturation. Meanwhile, the prescribed performance function is adopted to deal with output constraint. The closed-loop system stability is rigorously proved using Lyapunov synthesis. Finally, simulation results for unmanned helicopter system are presented to demonstrate the effectiveness of developed tracking control scheme using disturbance observer and radial basis function neural network.

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.

Robust Adaptive Control for Unmanned Helicopter with Stochastic Disturbance

In this paper, the problem of robust adaptive control is concerned for a class of small-scale unmanned helicopter systems in the presence of system uncertainty, stochastic disturbance and output constraint. The adaptive neural network approximator is introduced to handle the unknown system function. Meanwhile, a prescribed performance function is employed to deal with output constraint. It is proved that the proposed control method is able to guarantee the ultimately bounded convergence of all closed-loop system signals in mean square via Lyapunov stability theory. The effectiveness of the developed robust controller are illustrated and confirmed by numerical simulations for a class of unmanned helicopter systems.

Deterministic learning enhanced neutral network control of unmanned helicopter

In this article, a neural network–based tracking controller is developed for an unmanned helicopter system with guaranteed global stability in the presence of uncertain system dynamics. Due to the coupling and modeling uncertainties of the helicopter systems, neutral networks approximation techniques are employed to compensate the unknown dynamics of each subsystem. In order to extend the semiglobal stability achieved by conventional neural control to global stability, a switching mechanism is also integrated into the control design, such that the resulted neural controller is always valid without any concern on either initial conditions or range of state variables. In addition, deterministic learning is applied to the neutral network learning control, such that the adaptive neutral networks are able to store the learned knowledge that could be reused to construct neutral network controller with improved control performance. Simulation studies are carried out on a helicopter model to illustrate the effectiveness of the proposed control design.

System Identification Method for Small Unmanned Helicopter Based on Improved Particle Swarm Optimization

This paper proposes a novel method for Small Unmanned Helicopter (SUH) system identification based on Improved Particle Swarm Optimization (IPSO). In the proposed IPSO, every particle will do a local search as a “self-check” before updating the global velocity and position. Then, the global best particle is created by a certain number of elitist particles in order to get a rapid rate of convergence during calculation. Thus both the diversity and convergence speed can be taken into consideration during a search. Formulated by the first principles derivation, a state-space model is built for the analysis of dynamic modes of an experimental SUH. The helicopter is equipped with an Attitude Heading Reference System (AHRS) and the corresponding data storage modules, which are used for flight test data measurement and recording. After data collection and reconstruction, the input and output data are utilized to determine the corresponding aerodynamic parameters of the state-space model. The predictive accuracy and fidelity of the identified model are verified by making a time-domain comparison between the responses from the simulation model and the responses from actual flight experiments. The results show that the characteristics of the experimental SUH can be determined accurately using the identified model and the new method can be used for SUH system identification with high efficiency and reliability.