Control Surface Damage Research Articles

Fin failure tolerant control of agile air vehicle using feedback linearization

The probability of actuator lock, control surface damage, and thermal insulation failure on supersonic aircraft due to high speed and temperature is significant. Additionally, foldable fins are often used in missiles to increase the number of missiles that can be loaded onto a launcher and facilitate transportation, but this design presents the potential for malfunction and failure to open during flight. This study focuses on scenarios where control surfaces do not open or are partially damaged, leading to asymmetries and changes in the vehicle’s dynamics and aerodynamic model. The aim is to detect such failures and design a control system that can withstand these issues. To achieve this, the paper proposes an equivalent aerodynamic model representing the vehicle’s dynamics. The health of each fin is monitored using a nonlinear filter to estimate a parameter. Using separation theory, the dynamic system is divided into fast and slow subsystems, and a control signal for the faulty dynamics is designed based on back-stepping theory principles. Furthermore, the control allocation method is modified to accommodate the condition of the fins and generate the desired control moment. The proposed technique can quickly detect and isolate fin failures within seconds, while the designed controller effectively compensates for these failures.

Adaptive fault‐tolerant control for aircraft against control surface failure diagnosed by set‐membership estimation

AbstractThis paper focuses on the fault diagnosis method and the fault‐tolerant control method of the aircraft with control surface damage. The fault diagnosis method is based on the set‐membership estimation. To diagnose the fault of control surface under the condition of data containing noise and uncertainty, a multi‐index optimized fault residual observer based on sensitivity and robustness is designed for the observation of aircraft fault information. Based on the observed fault residual information, the set‐membership estimation theory is used to determine whether the aircraft control surface fault occurs. The fault‐tolerant control method is based on dynamic inverse control via aerodynamic parameter identification. Furthermore, to reduce the nonlinear effects of unmodeled dynamics and aerodynamic identification errors after a fault, an adaptive disturbance rejection observer is designed for control compensation. The proposed methods are evaluated by numerical simulation. Results demonstrate that the fault diagnosis method can effectively diagnose under the influence of uncertain noise and the attitude control has the robustness of different faults.

A comprehensive RFD-FTC-DCA system for hypersonic vehicles with saturation constraints

A comprehensive RFD-FTC-DCA system is proposed for the hypersonic vehicle with saturation constraints to resist the control surface damage, actuator faults, disturbances and parameter perturbation. Firstly, the hypersonic vehicle fault model is established. Secondly, the robust fault detection (RFD) observer is designed to detect the control surface damage and actuator faults accurately under the influences of disturbances and parameter perturbation. Thirdly, the fault-tolerant control (FTC) law is designed to resist the control surface damage, actuator faults, disturbances and parameter perturbation simultaneously. Finally, a dynamic control allocation (DCA) algorithm is designed to reallocate the FTC law and limit the deflection angle and velocity within saturation constraint. The comparison simulation results are presented to demonstrate that the RFD observer can detect the faults accurately and reduce false alarm rates effectively, the FTC law can resist the control surface damage, actuator faults, disturbances and parameter perturbation simultaneously, and the tracing errors of attack angle, side-slip angle and roll angle are only , and , respectively, and the DCA algorithm can reallocate the FTC law and limit the deflection angle and velocity within saturation values.

Sliding Mode Disturbance Observer-Based Adaptive Dynamic Inversion Fault-Tolerant Control for Fixed-Wing UAV

Unmanned aerial vehicles (UAVs) have been widely applied over the past decades, especially in the military field. Due to the unpredictability of the flight environment and failures, higher requirements are placed on the design of the control system of the fixed-wing UAV. In this study, a sliding mode disturbance observer-based (SMDO) adaptive dynamic inversion fault-tolerant controller was designed, which includes an outer-loop sliding mode observer-based disturbance suppression dynamic inversion controller and an inner-loop real-time aerodynamic identification-based adaptive fault-tolerant dynamic inversion controller. The sliding mode disturbance observer in the outer-loop controller was designed based on the second-order super-twisting algorithm to alleviate chattering. The aerodynamic identification in the inner-loop controller adopts the recursive least squares algorithm to update the aerodynamic model of the UAV online, thereby realizing the fault-tolerant control for the control surface damage. The effectiveness of the proposed SMDO enhanced adaptive fault-tolerant control method was validated by mathematical simulation.

Adaptive Neural Networks-Based Dynamic Inversion Applied to Reconfigurable Flight Control and Envelope Protection Under Icing Conditions

Aircraft icing can result in degradation of the aerodynamic characteristics and reduction of the control effectiveness, which would pose serious threats to flight safety. Reconfigurable flight control and envelope protection of iced aircraft have become an effective solution to ensure flight safety in icing encounters. In this study, the dynamic model of the iced aircraft was established, and high precision numerical simulation method and wind tunnel virtual flight experiment were applied to obtain the icing aerodynamic database. Furthermore, the reconfigurable flight control law was designed by using the adaptive neural networks based dynamic inversion (ANN-DI) control method. Simulation results demonstrate that the control method behaves well tracking performance and strong robustness in the presence of modeling errors and control surface damage. After that, an icing envelope protection system was designed based on control limiting strategy and the ANN-DI control was applied to calculate the control surface deflection limits based on the limit values of the key flight safety parameters. Finally, the designed icing envelope protection system has been verified through simulation in two autopilot modes under icing conditions. The simulation results obtained here show that the system could keep the key flight safety parameters such as the flight speed, the angle of attack (AOA), the side slip angle, and bank angle within the flight safe region under icing conditions. The method proposed in this study is expected to provide flight safety measures for in-flight icing.

A Novel RFDI-FTC System for Thrust-Vectoring Aircraft Undergoing Control Surface Damage and Actuator Faults During Supermaneuverable Flight

In this paper, a novel robust fault detection and identification and fault-tolerant control (RFDI-FTC) system is proposed for thrust-vectoring aircraft (TVA) during supermaneuverable flight. To this end, a TVA model that incorporates control surface damage, actuator faults, disturbances, and aerodynamic parameter uncertainty is described, and a novel RFDI-FTC system is designed for the TVA model, which includes 1) an RFDI subsystem with robustness to disturbances and parameter uncertainty and sensitivity to control surface damage and actuator faults by multiple adaptive observers; 2) a command filter FTC subsystem to eliminate the differential expansion in traditional backstepping control and to compensate for control surface damage, actuator faults, disturbances and parameter uncertainty; and 3) a stability analysis for the RFDI-FTC system. The simulation results are given to demonstrate the effectiveness and potential of this system.

Backstepping Fault-Tolerant Control for Unmanned Thrust-Vectoring Aircraft Based on Sliding-Mode Observer

In this paper, a backstepping fault-tolerant control based on sliding-mode observer is proposed for the unmanned thrust-vectoring aircraft (UTVA) control. First, the UTVA model with the uncertainty, control surface damage and actuator faults is described, which is divided into fast loop and slow loop. Next, the cascade observers including a high-order SMO and the discontinuous projection adaptive law are proposed to estimate the states with compensating the uncertainty and control surface damage, and the sliding-mode observer is designed to identify actuator faults and estimate fault parameters. Then, the backstepping fault-tolerant control combining the estimation of states and fault parameters is proposed to achieve the global fault-tolerant control, which compensates the uncertainty, control surface damage and actuator faults. Finally, simulation results are given to demonstrate the effectiveness for UTVA.

Stability analysis of aircraft’s sudden damage process with ROA overlapping

PurposeThe paper aims to propose a stability analysis method based on region-of-attraction (ROA) overlapping to evaluate the flight stability of the possible sudden damage process and guarantee flight safety in extreme cases.Design/methodology/approachFirst, according to the two flight conditions before and after damage which the aircraft may encounter, flight dynamical models are built and fitted by polynomial equation for subsequent ROA analysis. And then, the ROA overlapping estimation method based on V-s iteration is presented to complete the stability analysis of such airplane sudden damage process.FindingsFinally, in the presence of control surface damage, case aircraft flight stability is analyzed and simulated to verify the effectiveness of the proposed method.Practical implicationsThe proposed method can be used for stability check during the aircraft control law design, or for further completing the design of the emergency stable controller design.Originality/valueCompared with previous studies on sudden damage process of aircraft, to the best of the authors’ knowledge, this is one of the pioneer studies in which the ROA and ROA overlapping concept has been introduced. This study on stability analysis of aircraft sudden damage process could further develop the theory of emergency stable control.

Damage forecasting based on multi-factor fuzzy time series and cloud model

Timely and effective fault forecasting has great significance to guarantee the security of an aircraft, in view of the characteristics of harsh work environment of a flight control system. Based on the forecasting results, we can prevent damages or benefit from the forecasting activities. Fuzzy time series (FTS) forecast which provides a powerful and useful framework to deal with imprecision or ambiguity problems has been widely used in computer science. Many FTS-based forecasting models have been proposed in recent years, and thus the main problems are how to determine the useful interval length and the appropriate window basis size. In this paper, a new method based on multi-factor FTS and a cloud model was presented to predict the trend of aircraft control surface damage (ACSD). The proposed method constructs multi-factor fuzzy logical relationships based on the historical data of ACSD. To handle the uncertainty and vagueness of the ACSD historical data more appropriately, the cloud model is applied to partition the universe of discourse and to build membership functions. Furthermore, a variation forecasting method improved by the cloud model was proposed to compute the forecasting results. The experimental results prove the feasibility and its high forecasting accuracy of the proposed method.

Robust NSV Fault-Tolerant Control System Design Against Actuator Faults and Control Surface Damage Under Actuator Dynamics

In this paper, a decentralized fault-tolerant control (FTC) system is proposed for near-space vehicle (NSV) attitude dynamics. First, NSV reentry attitude dyna mic models with an uncertainty, actuator failure models, and a control surface damage model are described. Next, a new local fault identification algorithm is proposed to iden tify different types of actuator faults, which is based on multiobserver techniques. The local fault identification is constituted by a fault detection observer, fault parameter identification observers, and a decision-making mechanism. Then, a global adaptive sliding-mode observer is used to design the command filter backstepping fault- tolerant controller. Our focus is on the accommodation for actuator faults, control surface damage, uncertainties, and the resulting disturbances of the NSV. Finally, simulation results are given to demonstrate the effectiveness and poten tial of the proposed FTC scheme.

A neural network-based direct adaptive fault tolerance flight control for control surface damage

In order to enhance the reliability of flight control systems, a new neural network-based direct adaptive fault tolerance control was proposed for flight control system in the presence of control surface damage. Based on the accuracy approach of neural network, a fault parameter identification models were built to constitute hybrid compensator in order to ensure the strictly positive real of the failure flight control systems in the inner control loop. In the outer loop, a common direct adaptive controller was designed. The scheme was illustrated through simulations using an aircraft. The results show that an aircraft has also good dynamic performance in the control surface damage.

A method of fault diagnosis and flight envelop assessment for flight control systems

To improve the controllability of an aircraft in the presence of control surface damage, a method of fault diagnosis and flight envelop assessment is proposed in this paper. Firstly, it is considered that the control surface damage could cause the aerodynamic parameter change, so the damage model is built. Then, based on the recursive least square method, the aerodynamic parameters are identified in online. Finally, based on identification parameters, the online fault diagnosis is processed by remains decision technology, at the same time the flight envelop is assessed accurately. The simulation results show the fault diagnosis and envelop assessment is achieved.

Comparison of Inverse Optimal and Tuning Functions Designs for Adaptive Missile Control

An inverse optimal adaptive backstepping technique is applied to the design of a pitch control law for a generic surface-to-air nonlinear missile model, which is aerodynamically controlled. The control objective is to track a commanded reference signal with the angle of attack in the presence of control surface damage and aerodynamic uncertainties. The nonlinear adaptive control law is designed using a control Lyapunov function, after which inverse optimality with respect to a meaningful cost functional is demonstrated. The dynamic parameter update laws of the controller have been augmented with e-modification terms and dead-zones to prevent parameter drift. A second pitch control law is designed by means of the well known tuning functions adaptive backstepping technique. Both control laws are evaluated using numerical simulations where several cases of uncertainty are considered, after which a comparison of their nominal and robust performance is made.

Novel Control Scheme for Helicopter Flight: Fuzzy Immune Adaptive Model Inversion Control

A novel control scheme, fuzzy immune adaptive model inversion control, was put forward, aiming at the large flight envelope curve control problem of helicopter. The proposed scheme was designed based on model inversion theory, biology immune response mechanism and fuzzy control method. It could achieve effective control throughout large flight envelope curve via the design of fuzzy immune online-compensation element, only needing a static inverse model on the basis of a single flight condition. Simulation results comparing with the neural network adaptive control method show the system based on the proposed scheme has better real-time performance. It can offer adaptive compensation in time for model inverse errors caused by the changes of flight conditions and achieve accurate commands tracking. Stronger robustness has been demonstrated in the case of control surface damage and sensor output noise. Good three-axis uncoupled performance has also been shown.

Design of Reconfigurable Flight Controller Using Discrete Model Reference Adaptive Scheme

In this paper, an adaptive control algorithm using system identification is proposed for an aircraft fault tolerant control system. A discrete state-space system is reformulated to be the ARX model which has the advantage in handing variable structure systems. Discrete model reference adaptive control is used to make the output of fault system follow the output of reference model. To validate the performance of the proposed control scheme, numerical simulations are performed for the high performance aircraft with control surface damage.

Nonlinear discrete-time reconfigurable flight control law using neural networks

A neural-network-based adaptive reconfigurable flight controller is presented for a class of discrete-time nonlinear systems. The objective of the controller is to make the angle of attack, sideslip angle, and bank angle follow a given desired trajectory in the presence of control surface damage and aerodynamic uncertainties. The adaptive discrete-time nonlinear controller is developed using the backstepping technique and feedback linearization. Feedforward multilayer neural networks (NNs) are augmented to guarantee consistent performance when the effectiveness of the control decreases due to control surface damage. NNs learn through the recursive weight update rules that are derived from the discrete-time version of Lyapunov control theory. The boundness property of the error states and NN weight estimation errors is also investigated by the discrete-time Lyapunov analysis. The effectiveness of the proposed control law is demonstrated by applying it to a nonlinear dynamic model of the high-performance aircraft.

Direct Adaptive Neural Flight Controller for F-8 Fighter Aircraft

A novel neural network approach based on model-following direct adaptive control system design is proposed to improve damping and also to follow pilot commands accurately. The control law is derived using system theory and an implicit function theorem. A neural network with a linear filter is used to approximate the control law. The neural controller is trained using offline (finite interval of time) and online learning strategies. The neural controller was trained offline using the error signal between the aircraft response and pilot command (reference model). The offline-trained neural controller provides the necessary damping and tracking performances. The neural controller is adapted online for variations in aerodynamic coefficients or control surface deficiencies caused by control surface damage. A discrete time linear dynamic longitudinal model of a high-performance aircraft (F-8) is considered to demonstrate the effectiveness of the proposed control scheme. The performance results of the proposed control scheme are compared with the recently developed dynamic inversion technique and fully tuned radial basis function network. The neural controller performance is also evaluated for steady climb-and-hold maneuver in a nonlinear six-degree-of-freedom aircraft model.

Reconfigurable Flight Control System Design Using Input-Output Information

An adaptive control algorithm using input-output information is proposed for designing an aircraft fault tolerant control system. An input-output model is derived on the basis of a discrete state-space system. The formulated input-output model has the same structure as the autoregressive moving average (ARMA) model does, and therefore, the conventional system identification method using recursive least square can be used to identify the system. To design a reconfigurable control system, an LQ tracker with output feedback scheme is adopted. During the recursive adaptive control process, the system model is updated periodically. The proposed algorithm is applicable to time-varying systems in real time. To validate the performance of the proposed adaptive fault tolerant control technique, numerical simulation of the high performance aircraft with control surface damage was performed.

신경회로망을 이용한 이산 비선형 재형상 비행제어시스템

A neural network based adaptive reconfigurable flight controller is presented for a class of discrete-time nonlinear flight systems in the presence of variations of aerodynamic coefficients and control effectiveness decrease caused by control surface damage. The proposed adaptive nonlinear controller is developed making use of the backstepping technique for the angle of attack, sideslip angle, and bank angle command following without two time separation assumption. Feedforward multilayer neural networks are implemented to guarantee reconfigurability for control surface damage as well as robustness to the aerodynamic uncertainties. The main feature of the proposed controller is that the adaptive controller is developed under the assumption that all of the nonlinear functions of the discrete-time flight system are not known accurately, whereas most previous works on flight system applications even in continuous time assume that only the nonlinear functions of fast dynamics are unknown. Neural networks learn through the recursive weight update rules that are derived from the discrete-time version of Lyapunov control theory. The boundness of the error states and neural networks weight estimation errors is also investigated by the discrete-time Lyapunov derivatives analysis. To show the effectiveness of the proposed control law, the approach is i]lustrated by applying to the nonlinear dynamic model of the high performance aircraft.

Reconfigurable Flight Control System Design Using Adaptive Neural Networks

An adaptive controller design method based on neural network is proposed for reconfigurable flight control systems in the presence of variations in aerodynamic coefficients or control effectiveness deficiencies caused by control surface damage. The neural network based adaptive nonlinear controller is developed by using the backstepping technique for command following of the angle of attack, sideslip angle, and bank angle. On-line learning neural networks are implemented to compensate the control effectiveness decrease and guarantee the robustness to the uncertainties due to aerodynamic coefficients variations. The main feature of the proposed controller is that the adaptive controller is designed by assuming that all of the nonlinear functions of the system have uncertainties, whereas most of the previous works assume that only some of the nonlinear functions are unknown. Neural networks learn through the weight update rules that are derived from the Lyapunov control theory. The closed-loop stability of the error states is also investigated. A nonlinear dynamic model of a high performance aircraft is used to demonstrate the effectiveness of the proposed control law.