Adaptive Flight Control Law Research Articles

Design of adaptive flight control law for center of gravity and mass variation and HIL real-time simulation verification

Considering the influence of the center of gravity (CG) and mass variations on flight dynamics, a nonlinear L1 adaptive control approach is presented in this paper and applied to design a flight controller to achieve the aim of improving the robustness of the flight system against sudden changes in the CG. Based on L1 adaptive control, this approach introduces a feedback linearization strategy to eliminate the known nonlinear dynamics of the aircraft and realize the rapid decoupling of the aircraft states. The nonlinear L1 adaptive control also addresses the problem of insufficient robustness in conventional L1 adaptive control due to overcoming its own known nonlinearities, and then improves the robustness to external disturbances. Additionally, the modified piecewise constant is employed to design the adaptive law to improve the estimation accuracy. Finally, the control performance and robustness of the designed flight control law are verified in the hardware in the loop (HIL) flight control semi-physical experimental platform. Experimental results indicate that the nonlinear L1 adaptive flight control law has strong robustness, which can effectively overcome the disturbance of the CG and mass sudden variations. Moreover, the designed controller takes into account the transient performance while ensuring the steady-state performance of the aircraft with CG change.

무인 무미익 항공기의 무게중심 변화를 고려한 L1 적응제어 비행제어 법칙 설계 및 성능 검증

무인 무미익 항공기의 무게중심 변화를 고려한 L1 적응제어 비행제어 법칙 설계 및 성능 검증

Adaptive flight control law based on neural networks and dynamic inversion for micro-aerial vehicles

The paper presents two new adaptive systems, for the attitude׳s control of the micro-aerial vehicles (MAV׳s) – insect type. The dynamic model describing the motion of MAV׳s with respect to the Earth tied frame is nonlinear and the design of the new adaptive control system is based on the dynamic inversion technique. The inversion error is calculated with respect to the control law and two matrices (inertia and dynamic damping matrices) which express the deviation of the estimated matrices relative to the calculated ones (the matrices from the nonlinear dynamics of MAV׳s) in conditions of absolute stability in closed loop system by using the Lyapunov theory. To completely compensate this error, an adaptive component (output of a neural network) is added in the control law. The system also includes a second order reference model which provides the desired attitude vector and its derivative. The two variants of the new adaptive control system are validated by complex numerical simulations.

Nonlinear Dynamic Inversion and Neural Networks for a Tilt Tri-Rotor UAV

A small scale tri-rotor test bed with tilting propellers has been built to test flight control laws in view of the construction of a larger tilt rotor UAV. As a first step to achieve autonomous flight capabilities, a nonlinear dynamic inversion based flight controller is developed. This controller is designed on the basis of a time-scale principle with two levels. A lower level, fast control action, designed to achieve attitude control and stability goals, is driven by a higher level trajectory tracking control law. To achieve robust stability and performance in the presence of parametric variations and modelling uncertainties, an adaptive flight control law correction based on neural networks is investigated. A RBF neural network is implemented to mitigate the effects of imprecise inverse dynamics. The overall proposed flight controller performance are tested via numerical simulations on the mathematical model of the small scale tri-rotor. Preliminary results on the full tilt rotor are also shown.

Novel robust control framework for morphing aircraft

This paper develops a robust control methodology for a class of morphing aircraft, which is called innovative control effector (ICE) aircraft. For the ICE morphing aircraft, the distributed arrays of hundreds of shape-change devices are employed to stabilize and maneuver the air vehicle. Because the morphing aircraft have the inherent uncertainty and varying dynamics due to the alteration of their configuration, a desired control performance can not be satisfied with a fixed feedback controller. Therefore, a novel control framework including an adaptive flight control law and an adaptive allocation algorithm is proposed. Firstly, a state feedback adaptive control law is designed to guarantee closed-loop stability and state tracking in the presence of uncertain dynamics caused by the wing shape change due to different flight missions. In the control allocation, many distributed arrays are managed in an optimal way to improve the robustness of the system. The scheme is used to an uncertain morphing aircraft model, and the simulation results demonstrate their performance.

A Study on UCAV Flight Control System Based on Jamming Observer

In order to study the uncertain nonlinear jamming problem in UCAV’s flight control system, a method using jamming observer to check the system’s jamming was designed. Based on jamming observer, a flight control law was constructed, which reduced the restrictive condition for the jamming. The simulation results show that the adaptive flight control law based on jamming observer, make UCAV’s flight control system have good stability and robustness, it’s a great convenience analyzing the system stability.

A simulation environment for design and testing of aircraft adaptive fault‐tolerant control systems

PurposeThe purpose of this paper is to present the development of a Matlab/Simulink‐based simulation environment for the design and testing of indirect and direct adaptive flight control laws with fault tolerant capabilities to deal with the occurrence of actuator and sensor failures.Design/methodology/approachThe simulation environment features a modular architecture and a detailed graphical user interface for simulation scenario set‐up. Indirect adaptive flight control laws are implemented based on an optimal control design and frequency domain‐based online parameter estimation. Direct adaptive flight control laws consist of non‐linear dynamic inversion performed at a reference nominal flight condition augmented with artificial neural networks (NNs) to compensate for inversion errors and abnormal flight conditions following the occurrence of actuator or sensor failures. Failure detection, identification, and accommodation schemes relying on neural estimators are developed and implemented.FindingsThe simulation environment provides a valuable platform for the evaluation and validation of fault‐tolerant flight control laws.Research limitations/implicationsThe modularity of the simulation package allows rapid reconfiguration of control laws, aircraft model, and detection schemes. This flexibility allows the investigation of various design issues such as: the selection of control laws architecture (including the type of the neural augmentation), the tuning of NN parameters, the selection of parameter identification techniques, the effects of anti‐control saturation techniques, the selection and the tuning of the control allocation scheme, as well as the selection and tuning of the failure detection and identification schemes.Originality/valueThe novelty of this research efforts resides in the development and the integration of a comprehensive simulation environment allowing a very detailed validation of a number of control laws for the purpose of verifying the performance of actuator and sensor failure detection, identification, and accommodation schemes.