Helicopter System Research Articles

Vibration Qualification Campaign on Main Landing Gear System for High-Speed Compound Helicopter

Vibrations in helicopters have strong implications for their performance and safety, leading to the increased fatigue of components and reduced operational efficiency. As helicopters are designed to land on several types of surfaces, the landing gear system dissipates the impact on the ground and maintains stability during landing and take-off. These vibrations can arise from a variety of sources, such as aerodynamic loads, mechanical imbalances, and engine instabilities. In the present work, the authors describe the vibration qualification process of the main landing gear tailored to fast helicopters within the Clean Sky 2 Racer program. The method entails devising preliminary load sets that deform the structure in its key excited mode shapes to assess stresses and address the experimental campaign. A full-scale prototype model is then tested for sine sweep and random vibrations as per the Airbus Helicopter requirements in order to reach the final qualification and acceptance stage. Although the discussion centers on a landing gear structure, the described process could be extended to other critical equipment as well.

Design of a Helicopter System Recovery for a Sounding Rocket

In this article, the feasibility of spacecraft recovery using rotary wings is analysed. This technology possesses the ability to perform a safe and controlled landing, allowing the spacecraft to be reused and utilised in missions with different requirements. A computational model that simulates the descent of a sounding rocket was developed. It is based on rotor’s aerodynamic theories and kinematics and computes several rotor parameters that allow the analysis of the system’s performance. The model’s verification was successfully carried out by comparing its behaviour with literature data. To understand how each design parameter influences the system, a parametric study of the rotor design variables was held. It was concluded that the blade’s pitch angle is extremely important for the system’s performance and should be carefully selected, and that weight penalties resultant from excessive blade’s number or radius can be prejudicial. Furthermore, a design optimisation was conducted, aiming to minimise the rocket’s terminal velocity. Finally, a flare manoeuvre was disclosed to reduce the touchdown velocity. This recovery system is concluded to be highly promising, though requiring further work in several fields of study.

Event-triggered adaptive neural fault-tolerant control for a 2-DOF helicopter system with prescribed performance

In this paper, we focus on an event-triggered adaptive neural fault-tolerant control for a two-degree of freedom (2-DOF) helicopter system with actuator failures. In this design, a radial basis function neural network (NN) is exploited to estimate the uncertainty terms present in the system. A new error transformation technique is employed to make the tracking error of the system satisfy a prescribed performance function. Then, an event triggering mechanism is introduced to reduce the communication burden of the system. Smoothing functions and bounded estimation methods are then used to compensate for actuator failures and measurement errors. The helicopter system is proved to be consistently bounded by the direct method of Lyapunov functions. Finally, simulation and experimental results verify the effectiveness of the control strategy.

Neural-Network-Based Adaptive Fixed-Time Control for a 2-DOF Helicopter System With Input Quantization and Output Constraints.

This study proposes a neural-network (NN)-based adaptive fixed-time control method for a two-degree-of-freedom (2-DOF) nonlinear helicopter system with input quantization and output constraints. First, a hysteresis quantizer is employed to mitigate chattering during signal quantization, and adaptive variables are utilized to eliminate errors in the quantization process. Subsequently, the system uncertainties are approximated using a radial basis function NN. Simultaneously, a logarithmic barrier Lyapunov function (BLF) is constructed to prevent the system outputs from violating the constraint boundaries. Based on a rigorous Lyapunov stability analysis and the fixed-time stability criterion, the signals of the closed-loop system are proven to be bounded within a fixed time. Finally, numerical simulations and experiments verified the feasibility of the proposed method.

Reinforcement Learning Control for a 2-DOF Helicopter With State Constraints: Theory and Experiments

This study focuses on the novel reinforcement learning control strategy of a nonlinear two-degrees-of-freedom (2-DOF) helicopter system for tracking the desired trajectory while minimizing the tracking error. First, gradient descent algorithm is incorporated in the context of the reinforcement learning control scheme to obtain the adaptive laws. Subsequently, considering the uncertainties in the nonlinear system, radial basis function (RBF) neural networks (NNs) are exploited to approximate the unknown internal dynamics. In contrast to the previous studies, aiming at accelerating the convergence in reinforcement learning control, a barrier Lyapunov function is constructed to constrain the states to ensure that the tracking error rapidly converges to a neighborhood of zero. Under the proposed control strategy, the states of the closed-loop system are proven to be semi-globally uniformly ultimately bounded through rigorous Lyapunov analyses, and the state constraints are satisfied. Furthermore, the simulations and experiments conducted on a Quanser laboratory platform reveal that the proposed control functions are suitable and effective. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by designing a reinforcement learning control strategy to enhance online learning capability and control performance of the controller for a nonlinear 2-DOF helicopter system. The control framework is divided into the design of the critic and actor NNs, responsible primarily for evaluating the control performance and approximating uncertainties in the system separately. Unlike the adaptive NN control, the actor NN weights are updated by combining information of states and inputs from the critic NN. In addition, aiming at accelerating the convergence, a barrier Lyapunov function is constructed to constrain the states to ensure that the tracking error rapidly converges to a neighborhood of zero. Finally, the proposed control strategy is validated in simulation and experiment on the Quanser laboratory platform.

Broad Reinforcement Learning for Adaptive Control of a 2-DOF Helicopter System With Unknown Dead Zone

Broad Reinforcement Learning for Adaptive Control of a 2-DOF Helicopter System With Unknown Dead Zone

Optimal Second Order Sliding Control for the Robust Tracking of a 2-Degree-of-Freedom Helicopter System based on Metaheuristics and Artificial Neural Networks

Optimal Second Order Sliding Control for the Robust Tracking of a 2-Degree-of-Freedom Helicopter System based on Metaheuristics and Artificial Neural Networks

Adaptive Neural Network Control of an Uncertain 2-DOF Helicopter With Unknown Backlash-Like Hysteresis and Output Constraints.

An adaptive neural network (NN) control is proposed for an unknown two-degree of freedom (2-DOF) helicopter system with unknown backlash-like hysteresis and output constraint in this study. A radial basis function NN is adopted to estimate the unknown dynamics model of the helicopter, adaptive variables are employed to eliminate the effect of unknown backlash-like hysteresis present in the system, and a barrier Lyapunov function is designed to deal with the output constraint. Through the Lyapunov stability analysis, the closed-loop system is proven to be semiglobally and uniformly bounded, and the asymptotic attitude adjustment and tracking of the desired set point and trajectory are achieved. Finally, numerical simulation and experiments on a Quanser's experimental platform verify that the control method is appropriate and effective.

Adaptive Broad Learning Neural Network for Fault-Tolerant Control of 2-DOF Helicopter Systems

Adaptive Broad Learning Neural Network for Fault-Tolerant Control of 2-DOF Helicopter Systems

Adaptive sliding mode attitude control of 2-degrees-of-freedom helicopter system with actuator saturation and disturbances

The modelling uncertainties, external disturbance and actuator saturation issues will degrade the performance and even the safety of flight. To improve control performance, this study proposes an adaptive U-model based double sliding control (UDSMC) algorithm combined with a radial basis function neural network (RBFNN) for a nonlinear two-degrees-of-freedom (2-DOF) helicopter system. Firstly, the adaptive RBFNN is designed to approximate the system dynamics with unknown uncertainties. Furthermore, two adaptive laws are designed to deal with unknown external disturbances and actuator saturation errors. The global stability of the proposed helicopter control system is rigorously guaranteed by the Lyapunov stability analysis, realizing precise attitude tracking control. Finally, the comparative experiments with conventional SMC and adaptive SMC algorithms conducted on the Quanser Aero2 platform demonstrate the effectiveness and feasibility of the proposed 2-DOF helicopter control algorithm.

Particle filter-based adaptive super-twisting sliding mode fault-tolerant control for helicopter systems

Particle filter-based adaptive super-twisting sliding mode fault-tolerant control for helicopter systems

Prescribed-Time Adaptive Backstepping Control of an Uncertain Nonlinear 2-DOF Helicopter

This paper addresses the prescribed-time attitude tracking issue for an uncertain nonlinear 2-Degree of Freedom (DOF) helicopter. Based on the Euler-Lagrange equations, a nonlinear model is developed for the 2-DOF helicopter, where the system parameters and control input constant coefficients are unknown. The proposed prescribed-time control is based on adaptive backstepping and utilized to track the desired pitch and yaw positions separately. Using the theory of Lyapunov stability, we show that the proposed prescribed-time control and adaptive law ensure the boundedness of all the closed-loop signals of the system for all future time. A noticeable advantage of the proposed method is that the upper bound of the settling time can be specified in advance. Finally, to verify the efficacy and control capabilities of the proposed scheme, simulations are conducted on the Quanser 2-DOF helicopter system.

An improved teaching learning based optimization method to enrich the flight control of a helicopter system

An improved teaching learning based optimization method to enrich the flight control of a helicopter system

Fuel cell hybrid electric propulsion system for a lightweight helicopter: Design and performance analysis in urban air mobility scenario

The main purpose of this work is to investigate the performance of a hybrid fuel cell/battery powertrain concept for a lightweight helicopter in an urban air mobility scenario. Three different hybrid configurations are compared with a battery-electric design and a performance study is carried out over three specific missions, to identify the most appropriate design in each considered operative condition.Results show that a hybrid configuration may outperform the battery-electric one if a suitable balance between power sources is found. The hybrid design with a 70 kW fuel cell triplicates the number of regional transports completed by the battery-electric configuration, but it is less performing on urban routes. On the other hand, a 90 kW fuel cell, turned out to provide enhanced performance for high-power duty cycles, as for an urban mission.In general, the optimal hybrid configuration, in terms of achievable range, strictly depends on the specific duty cycle.

Trajectory tracking control for a quadrotor helicopter in the presence of cyber attacks

In this paper, the secure trajectory tracking issue is investigated for a quadrotor helicopter system with unknown-but-bounded (UBB) noises. It is assumed that the generated reference trajectory is sent to the helicopter via wireless networks which could be vulnerable to malicious attacks. The proposed encoding–decoding approach (EDA) can significantly improve the attack detection rate (DR), which is designed as a monotone and continuous function. Moreover, it is illustrated that both the DR and the tracking error can be affected by the parameter of the EDA. In addition, the zonotopic unknown input observer (ZUIO) is designed to estimate the helicopter’s states, which can be utilized to deal with UBB noises. Then, the gains of both the controller and the ZUIO can be calculated by minimizing the P−radius of the corresponding zonotopes. Finally, numerical examples are provided to demonstrate that the parameter of EDA can increase the DR and decrease the false alarm rate (FAR).

Deterministic Learning From Adaptive Neural Network Control for a 2-DOF Helicopter System With Unknown Backlash and Model Uncertainty

In this study, we develop a deterministic learning control approach using adaptive neural network (NN) for a two-degrees-of-freedom helicopter nonlinear system subject to unknown backlash and model uncertainty. First, by combining the backstepping and direct Lyapunov approaches, a novel adaptive NN control scheme with an inverse compensation method is proposed to address the input backlash nonlinearity, track specified trajectories, and stabilize the closed-loop system. Simultaneously, uncertain system dynamics are accurately identified and stored as learned knowledge in constant radial basis function NN weights, while satisfying partial persistent excitation. Subsequently, by extracting the learned knowledge, a learning-based controller is constructed to operate the same control tasks to achieve a superior control performance, less backlash nonlinearity, and minimal computational burden. Finally, the validity and efficacy of the proposed scheme are demonstrated through numerical examples and experiments.

Neural-Network-Based Adaptive Finite-Time Control for a Two-Degree-of-Freedom Helicopter System With an Event-Triggering Mechanism

Neural-Network-Based Adaptive Finite-Time Control for a Two-Degree-of-Freedom Helicopter System With an Event-Triggering Mechanism

LQR-based attitude controllers design for a 3-DOF helicopter system with comparative experimental tests

LQR-based attitude controllers design for a 3-DOF helicopter system with comparative experimental tests

Prescribed-time constrained feedback control for an uncertain twin rotor helicopter

This article presents a prescribed-time adaptive constraint feedback control strategy for an unmanned twin rotor helicopter system in the presence of parametric uncertainties. The integrator backstepping technique and adaptive estimator are employed to handle these unknown parameters. Additionally, a constrained function is utilized to prevent the possibility of signal amplification as the appointed settling time approaches. Through rigorous mathematical analysis employing Lyapunov theory, we prove that the proposed adaptive time-varying constraint feedback control and parameter estimator ensure the boundedness of all signals in the closed-loop system, promoting stability and preventing erratic behavior. One notable benefit of the proposed approach is that the convergence can be achieved within the time specified in advance. Finally, simulations and experiments are carried out to verify the efficacy of the proposed adaptive prescribed-time constraint feedback control strategy.

Event-triggered nonlinear information fusion preview control of a two-degree-of-freedom helicopter system

This research presents a novel nonlinear information fusion preview control (NIFPC) method for the flight control problem of two-degree-of freedom (2-DOF) helicopter system. First, according to the information fusion optimal estimation methodology, the system performance index and the system dynamic equation are converted to several information equations about the control input, and then the control input is directly derived by fusing these information equations via the centralized information fusion optimal estimation. In the limited bandwidth, transmitting a large number of sampled data through the network will increase the computational and communication burden of the system. In order to overcome this difficulty, event triggering mechanism is introduced. The asymptotic convergence of system performance index is guaranteed by Lyapunov stability theory. Numerical simulation and experiment results have been provided to demonstrate the effectiveness of the proposed control scheme.