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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.

The purpose of this paper is to develop a real‐time simulation environment for the validation of controller for an autonomous small‐scale helicopter.

The real‐time simulation platform is developed based on the nonlinear model of a series of small‐scale helicopters. Dynamics of small‐scale helicopter is analyzed through simulation. The controller is designed based on the extracted linear model.

The model‐based linear controller can be effectively designed and tested using real‐time simulation platform. The hover controller is demonstrated to be robust against wind disturbance.

To use the real‐time simulation environment to test and validate controllers for small‐scale helicopters, basic helicopter parameters need to be measured, calculated or estimated.

The real‐time simulation environment can be used generically to test and validate controllers for small‐scale helicopters.

The paper presents the design and development of a low‐cost hardware in the loop simulation environment using xPC target critical for validating controllers for small‐scale helicopters.

The purpose of this paper is to conduct the design, development and testing of a controller for an autonomous small‐scale helicopter.

The hardware in the loop simulation (HILS) platform is developed based on the nonlinear model of JR Voyager G‐260 small‐scale helicopter. Autonomous controllers are verified using the HILS environment prior to flight experiments.

The gains of the multi‐loop cascaded control architecture can be effectively optimized within the HILS environment. Various autonomous flight operations are achieved and it is demonstrated that the prediction from the simulations is in a good agreement with the result from the flight test.

The synthesized controller is effective for the particular test‐bed. For other small‐scale helicopters (with different size and engine specifications), the controller gains must be tuned again.

This work represents a practical control design and testing procedures for an autonomous small‐scale helicopter flight control. The autonomous helicopter can be used for various missions ranging from film making, agriculture and volcanic surveillance to power line inspection.

The research addresses the need for systematic design, development and testing of controller for a small‐scale autonomous helicopter by utilizing HILS environment.

The purpose of this paper is to design a model‐based robust controller for autonomous hovering of a small‐scale helicopter.

The model is developed using prediction error minimization (PEM) system identification method implemented to flight data. Based on the extracted linear model, an H_∞ controller is synthesized for robustness against parametric uncertainties and disturbances.

The proposed techniques for modelling provide a linear state‐space model which correlates well with the recorded flight data. The synthesized H_∞ controller demonstrates an effective performance which rejects both sinusoidal and step input disturbances. The controller enables the attitude angle follow the reference target while keeping the attitude rate constant about zero for hover flight condition.

The synthesized controller is effective for hovering and low‐speed flight condition.

This work provides an efficient hovering/low‐speed autonomous helicopter flight control required in many civilian UAV applications such as aerial surveillance and photography.

The paper addresses the challenges of controlling a small‐scale helicopter during hover with inherent modelling uncertainties and disturbances.

The purpose of this paper is to investigate the stability of a small scale six-degree-of-freedom nonlinear helicopter model at translator velocities and angular displacements while it is transiting to hover with different initial conditions.

In this study, model predictive controller and linear quadratic regulator are designed and compared within each other for the stabilization of the open loop unstable nonlinear helicopter model.

This study shows that the helicopter is able to reach to the desired target with good robustness, low control effort and small steady-state error under disturbances such as parameter uncertainties, mistuned controller.

The purpose of using model predictive control for three axes of the autopilot is to decrease the control effort and to make the close-loop system insensitive against modeling uncertainties.

During flight, a small-size autonomous helicopter will suffer external disturbance that is wind gust. Moreover, the small-size helicopter can carries limited payload or battery. Therefore control system of an autonomous helicopter should be able to eliminate external disturbance and optimize energy consumption. The purpose of this paper is to propose a hybrid controller structure to control a small-size autonomous helicopter capable to eliminate external disturbance and optimize energy consumption. The proposed control strategy comprise of two components, a linear component to stabilize the nominal linear system and a discontinuous component to guarantee the robustness. An integral control is included in the system to eliminate steady state error and tracking reference input.

This research started with derived mathematic model of the small-size helicopter that will be controlled. Based on the obtained mathematic model, then design of a hybrid controller to control the autonomous helicopter. The hybrid controller was designed based on optimal controller and sliding mode controller. The optimal controller as main controller is used to stabilize the nominal linear system and a discontinuous component based on sliding mode controller to guarantee the robustness.

Performance of the proposed controller was tested in simulation. The hybrid controller performance was compared with optimal controller performance. The hybrid controller has better performance compared with optimal controller. Results of the simulation shows that the proposed controller has good performance and robust against external disturbances. The proposed controller has better performance in rise time, settling time and overshoot compared with optimal controller response both for step input response and tracking capability.

Hybrid controller to control small-size helicopter has not reported yet. In this research new hybrid controller structure for a small size autonomous helicopter was proposed.

This paper seeks to present a feedback error learning neuro‐controller for an unstable research helicopter.

Three neural‐aided flight controllers are designed to satisfy the ADS‐33 handling qualities specifications in pitch, roll and yaw axes. The proposed controller scheme is based on feedback error learning strategy in which the outer loop neural controller enhances the inner loop conventional controller by compensating for unknown non‐linearity and parameter uncertainties. The basic building block of the neuro‐controller is a nonlinear auto regressive exogenous (NARX) input neural network. For each neural controller, the parameter update rule is derived using Lyapunov‐like synthesis. An offline finite time training is used to provide asymptotic stability and on‐line learning strategy is employed to handle parameter uncertainty and nonlinearity.

The theoretical results are validated using simulation studies based on a nonlinear six degree‐of‐freedom helicopter undergoing an agile maneuver. The neural controller performs well in disturbance rejection is the presence of gust and sensor noise.

The neuro‐control approach presented in this paper is well suited to unmanned and small‐scale helicopters.

The study shows that the neuro‐controller meets the requirements of ADS‐33 handling qualities specifications of a helicopter.

This paper aims to propose output feedback-based control algorithms for the flight control system of a scaled, un-crewed helicopter in its hover flight mode.

The proposed control schemes are based on H_∞ control and composite nonlinear control. The gains of the output feedback controllers are obtained as the solution of a set of linear matrix inequalities (LMIs).

In the proposed schemes, the finite-time convergence of system states to trim condition is achieved with minimum deviation from the steady-state. As the proposed composite nonlinear output feedback design improves the transient response, it is well suited for a scaled helicopter flight. The use of measured output vector instead of the state vector or its estimate for feedback provides a simple control structure and eliminates the need for an observer in real-time application. The proposed control strategies are relevant to situations in which a simple controller is essential due to economic factors, reliability and hardware implementation constraints.

The proposed control strategies are relevant to situations in which a simple controller is essential due to economic factors, reliability and hardware implementation constraints. They also have significance in applications where the number of measurement quantities needs to be minimized such as in a fully functional rotor-craft unmanned aerial vehicle.

The developed output feedback control algorithms can be used in small-scale helicopters for numerous civilian and military applications.

This work addresses the LMI-based formulation and solution of an output feedback controller for a hovering un-crewed helicopter. The stability and robustness of the closed-loop system are proved mathematically and the performance of the proposed schemes is compared with an existing strategy via simulation studies.

The purpose of this paper is to present the synthesis of a robust controller for autonomous small‐scale helicopter hovering control using extended H_∞ loop shaping design techniques.

This work presents the development of a robust controller for smooth hovering operation required for many autonomous helicopter operations using H_∞ loop shaping technique incorporating the Vinnicombe‐gap (v‐gap) metric for validation of robustness to uncertainties due to parameter variation in the system model. Simulation study was conducted to evaluate the performance of the designed controller for robust stability to uncertainty, disturbance rejection, and time‐domain response in line with ADS‐33E level 1 requirements.

The proposed techniques for a robust controller exhibit an effective performance for both nominal plant and 20 percent variation in the nominal parameters in terms of robustness to uncertainty, disturbance wind gust attenuation up to 95 percent, and transient performance in compliance with ADS‐33E level 1 specifications.

The controller is limited to hovering and low‐speed flight envelope.

This is expected to provide efficient hovering/low‐speed autonomous helicopter flight control required in many civilian unmanned aerial vehicles applications. Also, the technique can be used to simplify the number of robust gain‐scheduled linear controllers required for wide‐envelope flight.

The research will facilitate the deployment of low cost, small‐scale autonomous helicopters in various civilian applications.

The research addresses the challenges of parametric variation inherent in helicopter hovering/low‐speed control using an extended H_∞ loop shaping technique with v‐gap metric.

The purpose of this paper is to develop a hybrid algorithm using differential evolution (DE) and prediction error modeling (PEM) for identification of small-scale autonomous helicopter state-space model.

In this study, flight data were collected and analyzed; MATLAB-based system identification algorithm was developed using DE and PEM; parameterized state-space model parameters were estimated using the developed algorithm and model dynamic analysis.

The proposed hybrid algorithm improves the performance of the PEM algorithm in the identification of an autonomous helicopter model. It gives better results when compared with conventional PEM algorithm inside MATLAB toolboxes.

This study is applicable to only linearized state-space model.

The identification algorithm is expected to facilitate the required model development for model-based control design for autonomous helicopter development.

This study presents a novel hybrid algorithm for system identification of an autonomous helicopter model.