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The purpose of this paper is to investigate the feasibility of controlling a small‐scale helicopter by using the model predictive control (MPC) method.

The MPC control synthesis is employed by considering five linear models representing the flight of a small‐scale helicopter from hover to high‐speed cruise. The internal model principle is employed for the trajectory tracking design.

It is found that the MPC handles well the transition problems between the models, yields satisfactory tracking control performance and produces a suitable control signal. The performance of the tracking control of the helicopter is considerably influenced by the parameter selection in the states and inputs weighting matrices of the MPC. Simulation results also showed that faster dynamics, coupling problems, input and output constraints and changing linearized multi‐inputs multi‐outputs dynamics models in the small‐scale helicopter can be handled simultaneously by the MPC controller.

The present study is limited for the application of MPC for the control of small‐scale helicopters with non‐aggressive maneuvers.

The result can be extended to design a full envelope controller for an autonomous small‐scale helicopter without the need to resort to a conventional gain scheduling technique.

Helicopter control system designs using MPC with a single either linear or non‐linear model have been studied and reported in numerous literatures. The main contribution of the paper is in the application of MPC to handle the control problems of a small‐scale helicopter defined as a mathematical model with several different modes during a flight mission.

This paper aims to present a control strategy that eliminates the longitudinal and lateral drifting movements of the coaxial ducted fan unmanned helicopter (UH) during autonomous take-off and landing and reduce the coupling characteristics between channels of the coaxial UH for its special model structure.

Unidirectional auxiliary surfaces (UAS) for terminal sliding mode controller (TSMC) are designed for the flight control system of the coaxial UH, and a hierarchical flight control strategy is proposed to improve the decoupling ability of the coaxial UH.

It is demonstrated that the proposed height control strategy can solve the longitudinal and lateral movements during autonomous take-off and landing phase. The proposed hierarchical controller can decouple vertical and heading coupling problem which exists in coaxial UH. Furthermore, the confronted UAS-TSMC method can guarantee finite-time convergence and meet the quick flight trim requirements during take-off and landing.

The designed flight control strategy has not implemented in real flight test yet, as all the tests are conducted in the numerical simulation and simulation with a hardware-in-the-loop (HIL) platform.

The designed flight control strategy can solve the common problem of coupling characteristics between channels for coaxial UH, and it has important theoretical basis and reference value for engineering application; the control strategy can meet the demands of engineering practice.

In consideration of the TSMC approach, which can increase the convergence speed of the system state effectively, and the high level of response speed requirements to UH flight trim, the UAS-TSMC method is first applied to the coaxial ducted fan UH flight control. The proposed control strategy is implemented on the UH flight control system, and the HIL simulation clearly demonstrates that a much better performance could be achieved.

The purpose of this paper is to propose a new algorithm for pendulum‐like oscillation control of an unmanned rotorcraft (UR) in a reconnaissance mission and improve the stabilizing performance of the UR's hover and stare.

The algorithm is based on linear‐quadratic regulator (LQR), of which the determinable parameters are optimized by the artificial bee colony (ABC) algorithm, a newly developed algorithm inspired by swarm intelligence and motivated by the intelligent behaviour of honey bees.

The proposed algorithm is tested in a UR simulation environment and achieves stabilization of the pendulum oscillation in less than 4s.

The presented algorithm and design strategy can be extended for other types of complex control missions where relative parameters must be optimized to get a better control performance.

The ABC optimized control system developed can be easily applied to practice and can safely stabilize the UR during hover and stare, which will considerably improve the stability of the UR and lead to better reconnaissance performance.

This research presents a new algorithm to control the pendulum‐like oscillation of URs, whose performance of hover and stare is a key issue when carrying out new challenging reconnaissance missions in urban warfare. Simulation results show that the presented algorithm performs better than traditional methods and the design process is simpler and easier.

This paper aims to build an accurate mathematical model which is necessary for control design and attitude estimation of a miniature unmanned rotorcraft and its subsequent conversion to an autonomous vehicle.

Frequency-domain system identification of a small-size flybar-less remote controlled helicopter is carried out based on the input–output data collected from flight tests of the instrumented vehicle. A complete six degrees of freedom quasi-steady dynamic model is derived for hover and cruise flight conditions.

The veracity of the developed model is ascertained by comparing the predicted model responses to the actual responses from flight experiments and from statistical measures. Dynamic stability analysis of the vehicle is carried out using eigenvalues and eigenvectors. The identified model represents the vehicle dynamics very well in the frequency range of interest.

The model needs to be augmented with additional terms to represent the high-frequency dynamics of the vehicle.

Control algorithms developed using the first principles model can be easily reconfigured using the identified model, because the model structure is not altered during identification.

This paper gives a practical solution for model identification and stability analysis of a small-scale flybar-less helicopter. The estimated model can be easily used in developing control algorithms.

This paper aims to propose an environmentally adaptive trajectory planning system considering the dynamic characteristics of unmanned aerial vehicles (UAVs) and the distance between obstacles and the UAV. The system generates a smooth and safe flight trajectory online.

First, the hybrid A* search method considering the dynamic characteristics of the quadrotor is used to find the collision-free initial trajectory. Then, environmentally adaptive velocity cost is designed for environment-adaptive trajectory optimization using environmental gradient data. The proposed method adaptively adjusts the autonomous flight speed of the UAV. Finally, the initial trajectory is applied to the multi-layered optimization framework to make it smooth and dynamically viable.

The feasibility of the designed system is validated by online flight experiments, which are in unknown, complex situations.

The proposed trajectory planning system is integrated into a vision-based quadrotor platform. It is easily implementable onboard and computationally efficient.

A hybrid A* path searching method is proposed to generate feasible motion primitives by dispersing the input space uniformly. The proposed method considers the control input of the UAV and the search time as the heuristic cost. Therefore, the proposed method can provide an initial path with the minimum flying time and energy loss that benefits trajectory optimization. The environmentally adaptive velocity cost is proposed to adaptively adjust the flight speed of the UAV using the distance between obstacles and the UAV. Furthermore, a multi-layered environmentally adaptive trajectory optimization framework is proposed to generate a smooth and safe trajectory.

The growing use of small unmanned rotorcraft in civilian applications means that safe operation is increasingly important. The purpose of this paper is to investigate the fault tolerant properties to faults in the actuators of an C ₁ adaptive controller for a quadrotor vehicle.

C ₁ adaptive control provides fast adaptation along with decoupling between adaptation and robustness. This makes the approach a suitable candidate for fault tolerant control of quadrotor and other multirotor vehicles. In the paper, the design of an C ₁ adaptive controller is presented. The controller is compared to a fixed-gain LQR controller.

The C ₁ adaptive controller is shown to have improved performance when subject to actuator faults, and a higher range of actuator fault tolerance.

The control scheme is tested in simulation of a simple model that ignores aerodynamic and gyroscopic effects. Hence for further work, testing with a more complete model is recommended followed by implementation on an actual platform and flight test. The effect of sensor noise should also be considered along with investigation into the influence of wind disturbances and tolerance to sensor failures. Furthermore, quadrotors cannot tolerate total failure of a rotor without loss of control of one of the degrees of freedom, this aspect requires further investigation.

Applying the C ₁ adaptive controller to a hexrotor or octorotor would increase the reliability of such vehicles without recourse to methods that require fault detection schemes and control reallocation as well as providing tolerance to a total loss of a rotor.

In order for quadrotors and other similar unmanned air vehicles to undertake many proposed roles, a high level of safety is required. Hence the controllers should be fault tolerant.

Fault tolerance to partial actuator/effector faults is demonstrated using an C ₁ adaptive controller.

The 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.

The 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.

The 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.

The 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.

GA 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.

Lockheed Martin has been a premier builder and developer of manned aircraft and fighter jets since 1909. Since then, aircraft design has drastically evolved in many areas including the evolution of manual linkages to fly-by-wire systems, and mechanical gauges to glass cockpits. Lockheed Martin's knowledge of manned aircraft has produced a variety of Unmanned Aerial Vehicles (UAVs) based on size/wingspan, ranging from a micro-UAV (MicroStar) to a hand-launched UAV (Desert Hawk) and up to larger platforms such as the DarkStar. Their control systems vary anywhere between remotely piloted to fully autonomous systems. Remotely piloted control is equivalent to full human involvement with an operator controlling all the decisions of the aircraft. Similarly, fully autonomous operations describe a situation that has the human having minimal contact with the platform. Flight path control relies on a set of waypoints for the vehicle to fly through. This is the most common mode of UAV navigation, and GPS has made this form of navigation practical.

The purpose of this paper is to develop an integrated rotorcraft design and virtual manufacturing framework. The framework consists of two major sub‐frameworks which are e‐design and virtual manufacturing frameworks. This paper aims to describe the process of generating a specific framework for helicopter design and manufacturing in general, and a method for main rotor blade design.

The e‐design process integrates a pre‐conceptual, conceptual and preliminary design phases and includes many high accuracy physics‐based analysis tools and in‐house codes. The development of analysis programs and integration of flow data are discussed under the e‐design process. The virtual manufacturing process discusses physical three‐dimensional (3D) prototypes using rapid prototyping, virtual process simulation model development using Delmia Quest, virtual machine tool simulation and process‐based cost model. Vehicle geometry is modelled parametrically in computer‐aided 3D interactive application (CATIA) V5 to enable integration between the e‐design and virtual manufacturing processes, and then saved in Enovia SmartTeam which is commercial software for product data management (PDM). Data saved in Enovia SmartTeam are used as a database for the virtual manufacturing process.

The integration framework was constructed by using Model Center software. A multi‐disciplinary design optimization loop for rotor blade considering manufacturing factors is discussed to demonstrate the robustness and efficiency of the framework.

The manufacturing (practical factors) could be considered at an early stage of the rotor blades design.

The gap between theoretical (engineering design: aerodynamic, structural, dynamic, design, etc.) and practical aspects (manufacturing) is bridged through integrated product/process development framework. The modern concurrent engineering approach is addressed for helicopter rotor blade design throughout the case study.

The great success of unmanned aerial vehicles (UAVs) in performing near-real time tactical, reconnaissance, intelligence, surveillance and other various missions has attracted broad attention from military and civilian communities. A critical contribution to the increase and extension of UAV applications, resides in the separation of pilot and vehicle allowing the operator to avoid dangerous and harmful situations. However, this apparent benefit has the potential to lead to problems when the role of humans in remotely operating “unmanned” vehicles is not considered. Although, UAVs do not carry humans onboard, they do require human control and maintenance. To control UAVs, skilled and coordinated work of operators on the ground is required.