Rotorcraft-based Unmanned Aerial Vehicle Research Articles

Steering Behavior-based Multiple RUAV Obstacle Avoidance Control

In recent years, the applications of rotorcraft-based unmanned aerial vehicles (RUAV) have increased rapidly. In particular, the integration of bio-inspired techniques to enhance intelligence in coordinating multiple Rotorcraft-based Unmanned Aerial Vehicles (RUAVs) has been a focus of recent research and development. Due to the limitation in intelligence, these RUAVs are restricted in flying low altitude with high maneuverability. To make it possible, the RUAVs must have the ability to avoid both static and dynamic obstacles while operating at low altitudes. Therefore, developing a state-of-the-art intelligent control algorithm is necessary to avoid low altitude obstacles and coordinate without collision while executing the desired task. This paper proposes an Artificial Intelligence (AI) based steering behavior algorithm that generates a smooth trajectory by avoiding static and dynamic obstacles. The proposed method uses an obstacle detection algorithm (ODA) and obstacle avoidance algorithm (OAA). An obstacle detection algorithm is used to detect potential threats that fall in the course of flight while reaching the desired target, and obstacle avoidance algorithms use the steering behavior method to generate avoidance trajectory. A PC104 embedded board-based HIL (Hardware in the Loop) simulation environment is developed to validate the proposed algorithm. The proposed algorithm is evaluated by experimenting with a variety of nontrivial scenarios involving static and dynamic obstacles.

Nonlinear system modeling and identification of small helicopter based on genetic algorithm

PurposeThe purpose of this paper is to build nonlinear model of a small rotorcraft‐based unmanned aerial vehicles (RUAV), using nonlinear system identification method to estimate the parameters of the model. The nonlinear model will be used in robust control system design and aerodynamic analysis.Design/methodology/approachThe nonlinear model is built based on mechanism theory, aerodynamics and mechanics, which can reflect most dynamics in large flight envelop. Genetic algorithm (GA) and time domain flight data is adopted to estimate unknown parameters of the model. The flight data were collected from a series of fight tests. The identification results were also analyzed and validated.FindingsThe nonlinear model of RUAV has better accuracy, the parameters are physical quantities, and having distinctly recognizable values. The GA is suitable for nonlinear system identification. And the results proved the identified model can reflect the dynamic characteristics in extensive area of flight envelop.Research limitations/implicationsThe GA requires much more computing power, to identify 12 unknown parameters with 30 iterations, will takes more than 18 hours of a four cores desktop computer. Because of this is an off‐line identification process, and has more accuracy, extra time is acceptable.Originality/valueGA method has significantly increased the accuracy of the model. The previous work of system identification used a ten states linear model, and using PEM identified 23 coefficients. By carefully building the nonlinear model, it has only 21 unknown parameters, but if the model is linearized, it will get a linear model more than 35 states, which shows nonlinear model contain more dynamics than linear model.

Reconfigurable Intelligent Control Architecture of a Small-Scale Unmanned Helicopter

Over the past decades, substantial research has been undertaken in the design of intelligent architecture for the rotorcraft-based unmanned aerial vehicles (RUAV). Designing intelligent architecture is a challenging problem because future RUAVs are utterly autonomous and their performance is comparable with that of manned vehicles. This paper deals with the design and development of a layered architectural framework that addresses the issue arising in autonomous intelligent control systems. The architecture consists of two layers. The high-level layer is occupied by planning routines. In this level, the waypoints and mission tasks from the command center are executed. The function of the low-level layer is to stabilize the flight and follow the commanded trajectory from the upper layer. These layers integrate the following functionalities: (1) waypoint navigation and control, which includes auto-landing; (2) obstacle detection and avoidance; (3) fault detection and identification; and (4) system reconfiguration in two levels (high-level and low-level controllers). The resulting layered architecture is discussed in detail. Moreover, the novel fault detection and identification method is developed to address multiplicative and additive faults. A testing environment for RUAV is developed to validate this architecture. Complete setup is carried out using an embedded board run under a real-time operating system. The algorithms are tested and evaluated using hardware-in-the-loop simulation (HILS). The simulation result proves that the proposed architecture demonstrates the desired efficiency and reliability.

Autonomous Flight of the Rotorcraft-Based UAV Using RISE Feedback and NN Feedforward Terms

A position tracking control system is developed for a rotorcraft-based unmanned aerial vehicle (RUAV) using robust integral of the signum of the error (RISE) feedback and neural network (NN) feedforward terms. While the typical NN-based adaptive controller guarantees uniformly ultimately bounded stability, the proposed NN-based adaptive control system guarantees semi-global asymptotic tracking of the RUAV using the RISE feedback control. The developed control system consists of an inner-loop and outer-loop. The inner-loop control system determines the attitude of the RUAV based on an adaptive NN-based linear dynamic model inversion (LDI) method with the RISE feedback. The outer-loop control system generates the attitude reference corresponding to the given position, velocity, and heading references, and controls the altitude of the RUAV by the LDI method with the RISE feedback. The linear model for the LDI is obtained by a linearization of the nonlinear RUAV dynamics during hover flight. Asymptotic tracking of the attitude and altitude states is proven by a Lyapunov-based stability analysis, and a numerical simulation is performed on the nonlinear RUAV model to validate the effectiveness of the controller.

FEEDFORWARD COMPENSATION OF CONTROL SYSTEM WITH STATIC OUTPUT FEEDBACK FOR EXOGENOUS DISTURBANCE SUPPRESSION OF THE RUAV

The problem of feedforward compensation of control system with static output feedback for exogenous disturbance suppression of the rotorcraft-based unmanned aerial vehicle in hover is considered. Results of the introduced algorithm are evaluated both analytically and with the help of simulation.

동축반전 헬리콥터형 소형 무인항공기 설계 및 제작

The rotorcraft-based unmanned aerial vehicle(UAV) capable of performing close-range surveillance and reconnaissance has been developed. Trade studies on mission feasibility led to the adoption of a coaxial rotorcraft with twin rotors counter-rotating in one axis and driven by electric motors. A commercial off-the-shelf flight control computer(FCC) and a radio frequency modem were adopted for autonomous navigation. In order to achieve an aerial view, commercial charge-coupled device camera was also integrated into the vehicle. The performance of the completed vehicle was proved with manual flight test, and mission capability was verified through waypoint navigation flight after being equipped with FCC. This paper treats the whole process of design and system integration for development of the coaxial rotorcraft UAV.

Yaw Control of RUAVs : an Adaptive Robust H∞ Control Method

The problem of robust H∞ tracking control is considered for a class of linear system with time-varying uncertainties. The bounds of varying uncertainty ellipsoidal are obtained by set membership identification method. Using adaptive method, a new variable gain controller is designed to compensate the effect of uncertainty on systems. Then an application of this result to rotorcraft-based unmanned Aerial Vehicles (RUAVs) mounted on an experiment platform has demonstrated the effectiveness of the proposed method.

CONTRACTIVE MODEL PREDICTIVE CONTROL OF AN UNMANNED AERIAL VEHICLE MODEL

This paper presents the design of guidance and control laws for a small rotorcraft-based Unmanned Aerial Vehicle model using a contractive nonlinear Model Predictive Control scheme. Stability issues are addressed and simulation results are provided for a six degrees of freedom model in the case of trajectory tracking.

PERFORMANCE IMPROVEMENT OF AN ADAPTIVE CONTROLLER USING MODEL PREDICTIVE CONTROL : APPLICATION TO AN UAV MODEL

We present a control strategy based on the simultaneous use of model predictive and adaptive control. This approach guarantees stability in presence of perturbations for a certain class of nonlinear systems without adding stability constraints. It also improves the performances of the sole adaptive controller. This approach is used to design guidance and autopilot laws for a six degrees of freedom model of a small rotorcraft-based Unmanned Aerial Vehicle.

축소형 회전익 항공기의 간략화된 동적 모델링

모형헬리콥터를 이용한 무인항공기 설계를 위해 비선형 형태의 수학적 모델이 선행되어야 한다. 모형헬리콥터는 실기 헬리콥터에 비해 회전수가 훨씬 높으며 따라서 동특성도 실물기에 비해 훨씬 빠르다는 차이점이 있다. 본 논문에서는 축소형 헬리콥터의 수학적 모델링에 필요한 정식화과정으로서 복잡성을 최소화하면서도 실제의 동특성에 잘 부합하도록 각 구성요소별로 계산한 후 전체로 합산하는 방법을 제시하였다. 제자리 비행과 전진비행에서 수치계산을 통해 트림 값들을 계산하고 제자리 비행조건에서 선형 시스템을 해석하여 모형헬리콥터의 비행모드를 분석하였다. 계산결과 일반적인 경향은 몇 가지 작은 부분 이외에는 대체로 다른 연구결과와 비슷하였다. 이 과정을 검증하기 위해서 비행시험을 수행하여 시스템식별에 의한 결과와 비교하는 연구가 후속 수행될 예정이다. It is prerequisite that we have to fomulate the nonlinear mathematical modeling to design the guidance and control system of rotorcraft-based unmanned aerial vehicle using a small-scaled commercial helicopter. The small-scaled helicopters are very different from the full-scale helicopters in dynamic behavior such as high rotation speed and high frequency dynamic characteristics. In this paper, the formulation of the mathematical model of the small-scaled helicopter to minimize the complexity is presented by component and source build-up approach. It is linearized at the trim condition of hovering and forward flight and analyzed the flight modes. The results of this approach have general trends but a little difference. To verify this approach, it is necessary to compare this theoretical model with experimental results by system identification using flight test as a next research topic.

A FLIGHT CONTROL SYSTEM FOR AERIAL ROBOTS: ALGORITHMS AND EXPERIMENTS

This paper presents a flight control system designed as on-board intelligence for rotorcraft-based unmanned aerial vehicles (RUAVs). This hierarchical flight control system, endowed with autonomy resembling sense-reason-act processes of intelligent agents in nature, gradually refines given abstract mission commands into real-time control signals for each vehicle. A tracking control layer is designed on the identified vehicle dynamics and integrated with a trajectory generator for logistical action planning. The proposed structure has been implemented on radio-controlled helicopters and validated in a variety of experiments. Results from way-point navigation, a probabilistic pursuit-evasion game and vision-based tracking of a moving target show the promising potential of intelligent flying robots.