Adaptive Flight Control System Research Articles

Correction to: selection and evaluation of FOPID criteria for the X-15 adaptive flight control system (AFCS) via Lyapunov candidates: Optimizing trade-offs and critical values using optimization algorithms

Recently, there has been a growing interest among academics worldwide in studying flight control systems. The advancement of tracking technologies, such as the X-15 adaptive flight control system developed at NASA (National Aeronautics and Space Administration), has sparked significant exploration efforts by scientists. The vast availability of aerial resources further contributes to the importance of studying adaptive flight control systems (AFCS). The successful operation of AFCS relies on effectively managing the three fundamental motions: pitch, roll, and yaw. Therefore, scientists have been diligently working on developing optimization algorithms and models to assist AFCS in achieving optimal gains during motion. However, in real-world scenarios, each motion requires its own set of criteria, which presents several challenges. Firstly, there are multiple criteria available for selecting appropriate optimization values for each motion. Secondly, the relative importance of these criteria influences the selection process. Thirdly, there is a trade-off between the performance of the criteria within a single optimization case and across different cases. Lastly, determining the critical value of the criteria poses another obstacle. Consequently, evaluating and selecting optimum methods for AFCS trajectory controls becomes a complex operation. To address the need for optimizing AFCS for various maneuvers, this study proposes a new selection process. The suggested approach involves utilizing black hole optimization (BHO), Jaya optimization algorithm (JOA), and sunflower optimization (SFO) as methods for detecting and correcting trajectories in adaptive flight control systems. These methods aim to determine the best launch of missiles from the AFCS based on the coordinate location for both long and short distances. Additionally, the methods determine the optimal gains for the FOPID (fractional order proportional integral derivative) controller and enhance protection against enemy attacks. The research framework consists of two parts. The first part focuses on improving the FOPID motion gains by employing optimization algorithms (BHO, JOA, and SFO) that are evaluated based on the FOPID criteria. Lower significant weighting values of the optimization algorithms demonstrate the best missile launching in a cosine wave trajectory within AFCS, while higher significant values indicate the best missile launching in a sine wave trajectory within AFCS. The FOPID controller criteria, including Kp_pitch, Ki_roll, Kd_yaw, λ_pitch, and µ_yaw, are considered in all situations. Furthermore, the study reports the best weights obtained for the "Kp_pitch" criterion across the motions as follows: (0.8147, 66.7190, and 54.4716). For the "Ki_roll" criterion, the best weights are (0.0975, 64.4938, and 64.7311), and for "Kd_yaw" the weights are (0.1576, 35.2811, and 54.3886). The results of the selection process by the BHO, JOA, and SFO algorithms also include λ_pitch (0.1419, 40.0791, and 72.1047) and µ_yaw (0.6557, 13.5752, and 52.2495). To ensure the validity of the proposed research framework, a systematic evaluation and precise analysis were conducted.

Selection and evaluation of FOPID criteria for the X-15 adaptive flight control system (AFCS) via Lyapunov candidates: Optimizing trade-offs and critical values using optimization algorithms

Recently, there has been a growing interest among academics worldwide in studying flight control systems. The advancement of tracking technologies, such as the X-15 adaptive flight control system developed at NASA (National Aeronautics and Space Administration), has sparked significant exploration efforts by scientists. The vast availability of aerial resources further contributes to the importance of studying adaptive flight control systems (AFCS). The successful operation of AFCS relies on effectively managing the three fundamental motions: pitch, roll, and yaw. Therefore, scientists have been diligently working on developing optimization algorithms and models to assist AFCS in achieving optimal gains during motion. However, in real-world scenarios, each motion requires its own set of criteria, which presents several challenges. Firstly, there are multiple criteria available for selecting appropriate optimization values for each motion. Secondly, the relative importance of these criteria influences the selection process. Thirdly, there is a trade-off between the performance of the criteria within a single optimization case and across different cases. Lastly, determining the critical value of the criteria poses another obstacle. Consequently, evaluating and selecting optimum methods for AFCS trajectory controls becomes a complex operation. To address the need for optimizing AFCS for various maneuvers, this study proposes a new selection process. The suggested approach involves utilizing black hole optimization (BHO), Jaya optimization algorithm (JOA), and sunflower optimization (SFO) as methods for detecting and correcting trajectories in adaptive flight control systems. These methods aim to determine the best launch of missiles from the AFCS based on the coordinate location for both long and short distances. Additionally, the methods determine the optimal gains for the FOPID (fractional order proportional integral derivative) controller and enhance protection against enemy attacks. The research framework consists of two parts. The first part focuses on improving the FOPID motion gains by employing optimization algorithms (BHO, JOA, and SFO) that are evaluated based on the FOPID criteria. Lower significant weighting values of the optimization algorithms demonstrate the best missile launching in a cosine wave trajectory within AFCS, while higher significant values indicate the best missile launching in a sine wave trajectory within AFCS. The FOPID controller criteria, including Kp_pitch, Ki_roll, Kd_yaw, λ_pitch, and µ_yaw, are considered in all situations. Furthermore, the study reports the best weights obtained for the "Kp_pitch" criterion across the motions as follows: (0.8147, 66.7190, and 54.4716). For the "Ki_roll" criterion, the best weights are (0.0975, 64.4938, and 64.7311), and for "Kd_yaw" the weights are (0.1576, 35.2811, and 54.3886). The results of the selection process by the BHO, JOA, and SFO algorithms also include λ_pitch (0.1419, 40.0791, and 72.1047) and µ_yaw (0.6557, 13.5752, and 52.2495). To ensure the validity of the proposed research framework, a systematic evaluation and precise analysis were conducted.

Influence of the Order of Vehicle Motion Equations in the Implementation of an Adaptive Flight Control System with the Real Time Identification of Controlled Element Dynamics Parameters

Influence of the Order of Vehicle Motion Equations in the Implementation of an Adaptive Flight Control System with the Real Time Identification of Controlled Element Dynamics Parameters

Nonlinear adaptive flight control system: Performance enhancement and validation

The problem of decreasing stability margins in L1 adaptive control systems is discussed and an out-of-loop L1 adaptive control scheme based on Lyapunov’s stability theorem is proposed. This scheme enhances the effectiveness of the adaptation, which ensures that the system has sufficient stability margins to achieve the desired performance under parametric uncertainty, additional delays, and actuator faults. The stability of the developed control system is demonstrated through a series of simulations. Compared with an existing control scheme, the constant adjustment of the stability margins by the proposed adaptive scheme allows their range to be extended by a factor of 4–5, bringing the stability margin close to that of variable gain PD control with adaptively scheduled gains. The engineered practicability of adaptive technology is verified. A series of flight tests verify the practicability of the designed adaptive technology. The results of these tests demonstrate the enhanced performance of the proposed control scheme with nonlinear parameter estimations under insufficient stability margins and validate its robustness in the event of actuator failures.

Adaptive flight control system for flight safety improvement in reentry and other high-velocity vehicles

An adaptive controller and a nonlinear rate limiter are presented in order to improve the flight safety of manually controlled reentry vehicles and supersonic transport (SST) aircraft. The adaptive controller is based on the principle of dynamic inversion. The calculation of the controller parameters corresponding to the aerodynamic coefficients of the vehicle was carried out by a proposed online identification procedure. The effectiveness of the adaptive controller was tested through a set of experiments performed using a ground-based simulator. The obtained results were used to estimate the probability of an accident due to pilot error for the basic Space Shuttle flight control system and for its improved version with the adaptive controller. It was found that using the adaptive controller causes high required rates of elevon deflection. Because of the actuator's rate limiting, this is a source of pilot-induced oscillations. To counteract these oscillations in the pilot-vehicle closed loop system, an adaptive nonlinear rate limiter is introduced. The effectiveness of using the adaptive prefilter for improving the flight safety and flying qualities of the Space Shuttle reentry vehicle and an SST was demonstrated in experiments involving a ground-based simulator.

Human Pilot Interaction with Fast Adapting Flight Control System

Interaction between the adaptive behavior of a human pilot and an adaptive flight control system (FCS) usually leads to undesirable adverse effects such as pilot-induced oscillations (PIO). This paper presents an analysis of this interaction in the case of a fast adapting FCS. The analysis shows that, although a pilot will have some nonlinear behavior at the moment when a failure occurs, the fast adapting FCS allows to keep the physical workload low and the pilot behavior remains the same as before the failure. This effectively mitigates the adverse effects seen in the literature.

Robust modification of nonlinear L1 adaptive flight control system via noise attenuation

This paper describes the design of a nonlinear L1 adaptive control system based on angle and angular velocity feedback for the longitudinal attitude control of an unmanned aerial vehicle. An integral-form adaptive law based on Lyapunov's stability theorem is applied to estimate both the linear and nonlinear uncertainties. A modified algorithm is proposed based on the transformation between energy and autocorrelation function to solve the parameter drift problem caused by nonlinear terms in the adaptive law. The absolute value of the function is minimized by selecting an appropriate step length to eliminate the influence of noise on the adaptive law. A parameter design method for several common forms of noise is proposed. Compared with existing robust modification techniques, autocorrelation modification is demonstrated using MATLAB simulations to solve the parameter drift problem and ensure the robustness of the system. Flight tests examining the stability of the controller and the performance robustness of the nonlinear L1 adaptive control system to sensor noise and actuator failure are described. The variable gain control system is verified through flight tests. Comparative experiments show that the proposed modification suppresses the system oscillations caused by noise and solves the parameter drift problem within 5 s.

Aircraft Mass Properties Estimation During Airdrop Maneuver: A Nonlinear Filtering Approach

Unlike a single-body approach, modeling based on a two-body approach has been employed to prepare the required system dynamic model as a time update equation in the applied filtering technology and measurement data for the estimation process. This more precise mathematical model enabled better understanding about the dynamics of the change in the aircraft mass properties during the airdropping operation. The problem is defined as estimation of the optimal mass properties parameters for the best possible fit of the model output to the real data. The parameter estimation problem is investigated by a nonlinear filtering methodology in two sequential steps. In the first step, the single extended Kalman filter is used to estimate the aircraft total mass and moment of inertia before entering an airdrop maneuver. In the second step, both the aircraft dynamic states and mass parameters are estimated during an airdrop maneuver using a joint extended Kalman filter. Monte Carlo simulations have then been performed to assess the performance of the algorithm in the presence of measurement errors and model uncertainties. The results indicate that time-varying aircraft mass properties estimation, as a technological requirement for the aircraft adaptive flight control system to reduce the risks involved with the airdrop operation, is satisfactorily possible.

Reinforcement learning based closed‐loop reference model adaptive flight control system design

SummaryIn this study, we present a reinforcement learning (RL)‐based flight control system design method to improve the transient response performance of a closed‐loop reference model (CRM) adaptive control system. The methodology, known as RL‐CRM, relies on the generation of a dynamic adaption strategy by implementing RL on the variable factor in the feedback path gain matrix of the reference model. An actor‐critic RL agent is designed using the performance‐driven reward functions and tracking error observations from the environment. In the training phase, a deep deterministic policy gradient algorithm is utilized to learn the time‐varying adaptation strategy of the design parameter in the reference model feedback gain matrix. The proposed control structure provides the possibility to learn numerous adaptation strategies across a wide range of flight and vehicle conditions instead of being driven by high‐fidelity simulators or flight testing and real flight operations. The performance of the proposed system was evaluated on an identified and verified mathematical model of an agile quadrotor platform. Monte‐Carlo simulations and worst case analysis were also performed over a benchmark helicopter example model. In comparison to the classical model reference adaptive control and CRM‐adaptive control system designs, the proposed RL‐CRM adaptive flight control system design improves the transient response performance on all associated metrics and provides the capability to operate over a wide range of parametric uncertainties.

An Alternative Tuning Scheme for Simple Adaptive Flight Control System

The occurrence of system failure or of external disturbances are problem that must be considered in the design of safe flight control systems. Adaptive control schemes show the ability to handle such issues without any a priori knowledge about the fault or disturbance. The main condition for obtaining a stable adaptive controller is the passivity of the plant but most real system do not satisfy this condition. The use of properly defined Parallel Feedforward Compensator (PFC) can allow the augmented system to meet the passivity requirements. An alternative design approach for tuning the PFC and the invariant gains of the Simple Adaptive Controller (SAC) is presented in this work. The tuning procedures is a modification of the PSO and is called Population Decline Swarm Optimization (PDSO) since it takes into account a decline demographic model to speed up the tuning steps. The method is applied to adaptively control the pitch response of an aeroplane during level flight. Tuning results are presented along with flight mechanics simulation taking into account different command laws.

Aircraft neural modeling and parameter estimation using neural partial differentiation

PurposeThe purpose of this paper is to build a neural model of an aircraft from flight data and online estimation of the aerodynamic derivatives from established neural model.Design/methodology/approachA neural model capable of predicting generalized force and moment coefficients of an aircraft using measured motion and control variable is used to extract aerodynamic derivatives. The use of neural partial differentiation (NPD) method to the multi-input-multi-output (MIMO) aircraft system for the online estimation of aerodynamic parameters from flight data is extended.FindingsThe estimation of aerodynamic derivatives of rigid and flexible aircrafts is treated separately. In the case of rigid aircraft, longitudinal and lateral-directional derivatives are estimated from flight data. Whereas simulated data are used for a flexible aircraft in the absence of its flight data. The unknown frequencies of structural modes of flexible aircraft are also identified as part of estimation problem in addition to the stability and control derivatives. The estimated results are compared with the parameter estimates obtained from output error method. The validity of estimates has been checked by the model validation method, wherein the estimated model response is matched with the flight data that are not used for estimating the derivatives.Research limitations/implicationsCompared to the Delta and Zero methods of neural networks for parameter estimation, the NPD method has an additional advantage of providing the direct theoretical insight into the statistical information (standard deviation and relative standard deviation) of estimates from noisy data. The NPD method does not require the initial value of estimates, but it requires a priori information about the model structure of aircraft dynamics to extract the flight stability and control parameters. In the case of aircraft with a high degree of flexibility, aircraft dynamics may contain many parameters that are required to be estimated. Thus, NPD seems to be a more appropriate method for the flexible aircraft parameter estimation, as it has potential to estimate most of the parameters without having the issue of convergence.Originality/valueThis paper highlights the application of NPD for MIMO aircraft system; previously it was used only for multi-input and single-output system for extraction of parameters. The neural modeling and application of NPD approach to the MIMO aircraft system facilitate to the design of neural network-based adaptive flight control system. Some interesting results of parameter estimation of flexible aircraft are also presented from established neural model using simulated data as a novelty. This gives more value addition to analyzing the flight data of flexible aircraft as it is a challenging problem in parameter estimation of flexible aircraft.

Modeling and Adaptive Flight Control for Quadrotor Trajectory Tracking

In the near future, unmanned aerial vehicles are probably going to be included in the airspace. Therefore, air traffic management needs to be enhanced in terms of reliability, safety, and accuracy. The main objective of this paper deals with a quadrotor flight control system that generates the necessary control laws to perform accurately, as well as possible, the reference flight trajectories. In this paper, a new quadrotor flight dynamics is developed, including an adopted wind model component where a study of the minimum phase and observability conditions is performed. Based on an adaptive backstepping control algorithm, a developed flight control system generates nonlinear control laws in an iterative and systematic procedure design. In addition to some simulations in a no-wind situation, the proposed adaptive flight control system is simulated in a windy flight condition. The design of adaptation laws according to some quadrotor parameters is developed, and the global stability of the proposed flight control system is guaranteed using the Lyapunov theory. Numerical simulations are performed for the quadrotor engaged in complex trajectory tracking, and obtained results show the feasibility and effectiveness of the proposed control approach.

Adaptive Self-Tuning Flight Control System for ATLANTE UAS

AbstractThis paper presents an adaptive self-tuning flight control system (AST-FCS) for a fixed-wing aircraft, applied to a high-fidelity dynamic model of the ATLANTE unmanned aircraft system (UAS). This new adaptive control system is based on adaptive predictive (AP) control methodology and a new guidance system. AP control has been implemented in many complex industrial applications with excellent results, but it has not been applied in the aerospace sector yet. Experimental results obtained in simulation by flying a high-fidelity model of the ATLANTE UAS illustrate excellent control system performance and show the robustness of the AST-FCS, which has been designed without using accurate knowledge of the aircraft dynamics embedded within the model. It is expected that the application of AP control in this field could introduce significant advantages in aircraft control, improving performance in a wide variety of flight conditions and reducing design costs.

Flight-Test Evaluation of a Loss-of-Control Mitigation System

For more than a decade, researchers have been developing novel adaptive flight control systems that provide a means to safely operate an air vehicle in the presence of damage or failures. Although these systems show great promise, integration of exemplar systems in flight-test aircraft at NASA has revealed the potential for unfavorable pilot–vehicle coupling, including pilot-induced oscillations. To address this issue directly, the Smart Adaptive Flight Effective Cue system was developed that features an adaptive command path gain to mitigate oscillation tendencies and an inceptor force-feedback cue to alert the pilot that the system is active. To evaluate the system, a representative adaptive controller that had been successfully evaluated in previous flight-test programs and the Smart Adaptive Flight Effective Cue system were implemented in a Calspan Learjet in-flight simulator. Flight-test evaluations consisted of three test pilots conducting two sorties each to evaluate the effectiveness of the combined system in the presence of failures. Pilot opinion ratings and quantitative assessments of performance indicated that the Smart Adaptive Flight Effective Cue system successfully eliminated the oscillation tendencies that can lead to loss of control. Furthermore, the system restored a more linear vehicle response that allowed the pilots to successfully complete the evaluation tasks with near baseline aircraft performance.

A Singularly Perturbed System Approach to Adaptive Neural Back-stepping Control Design of Hypersonic Vehicles

This paper presents the design of neural adaptive flight control systems for the longitudinal dynamics of hypersonic vehicle. By considering the coupling between thrust and pitch moment, the proposed control strategy is derived from the solutions of a series of fast dynamical equations, which are designed based on the back-stepping control and singularly perturbed system approach. The RBF neural networks are employed to approximate the unknown hypersonic dynamics. Simulation results are included to show the effectiveness of the neural adaptive control method.

Online Aerodynamic Model Identification Using a Recursive Sequential Method for Multivariate Splines

Avoiding high computational loads is essential to online aerodynamic model identification algorithms, which are at the heart of any model-based adaptive flight-control system. Multivariate simplex B-spline methods are excellent function approximation tools for modeling the nonlinear aerodynamics of high-performance aircraft. However, the computational efficiency of the multivariate simplex B-spline method must be improved in order to enable real-time onboard applications, for example, in adaptive nonlinear flight-control systems. In this paper, a new recursive sequential identification strategy is proposed for the multivariate simplex B-spline method aimed at increasing its computational efficiency, thereby allowing its use in onboard system identification applications. The main contribution of this new method is a significant reduction of the computational load for large-scale online identification problems as compared to the existing multivariate simplex B-spline methods. The proposed method consists of two sequential steps for each time interval and makes use of a decomposition of the global problem domain into a number of subdomains, called modules. In the first step, the B-coefficients for each module are estimated using a least-squares estimator. In the second step, the local B-coefficients for each module are then smoothened into a single global B-coefficient vector using a linear minimum mean-square errors estimation. The new method is compared to existing batch and recursive multivariate simplex B-spline methods in a numerical experiment in which an aerodynamic model is recursively identified based on data from a NASA F-16 wind-tunnel model.

Composite adaptive posicast control for a class of LTI plants with known delay

Many potential applications of adaptive control, such as adaptive flight control systems, require that the controller have high performance, stability guarantees, and robustness to time delays. These requirements typically lead to engineering trade-offs, such as a trade-off between performance and robustness. In this paper, a new Composite Adaptive Posicast Control (CAPC) framework is proposed for linear time-invariant (LTI) plants with input-matched parametric uncertainties and known delay. The CAPC architecture uses a combination of several modifications to the typical direct model reference adaptive control (MRAC). The described approach is a nonlinear controller design that explicitly accounts for known time delay. The stability of the overall closed-loop system can be guaranteed using nonlinear analysis tools. The benefits of the CAPC approach are explored using a simulation of the longitudinal dynamics of a fixed-wing aircraft. Comparison studies are presented for 80 ms and 250 ms time delay cases.

Multicriteria analysis of an ℒ1 adaptive flight control system

This article presents an overview of the application of the Parameter Space Investigation method for the multicriteria design optimization of the ℒ1 adaptive flight control system implemented on the two turbine-powered dynamically-scaled generic transport model Airborne Subscale Transport Aircraft Research aircraft. In particular, this study addresses the improvement of a nominal prototype solution, obtained using basic design guidelines of ℒ1 adaptive control theory. The results validate the theoretical claims of ℒ1 adaptive control in terms of closed-loop performance and robustness and illustrate the systematic character of its design procedure. Furthermore, this article shows the suitability of the Parameter Space Investigation method for the multicriteria design optimization over a multidimensional design variable space of a flight control system subject to desired control specifications. The use of this particular method is of special interest, as it provides invaluable information about the behavior of the closed-loop system in an extended space of design parameters and performance criteria. The results and conclusions of this article have led to a deeper understanding of the characteristics of the closed-loop adaptive system and have contributed to the improvement of the flying qualities and the robustness margins of the adaptive ℒ1-augmented aircraft, which has been recently flight tested by National Aeronautics and Space Administration.

Design and Simulation of an Adaptive Flight Control System for Unmanned Rotorcraft

The principle of the model inverse controller is discussed in order to design an unmanned rotorcraft flight control system applicable to different mission tasks.A neural network compensation controller and online algorithms of its weight coefficients are proposed,and the stability of the synthesized controller is analyzed.The rotational dynamics inverse controller and translational dynamics inverse controller for the unmanned rotorcraft are deduced.The attitude inner loop controller and trajectory outer loop controller are designed.The control distribution strategy of the driving motor is determined to control the speed of coaxial rotor.A combined maneuver flight mission task is planned to imitate automatic flight motion.The adaptive flight control system is verified with simulation,and the flight control abilities of horizontal and vertical motion,hover and heading motion are demonstrated and validated.These results show that the designed adaptive flight control system has adaptability and robustness,and can realize attitude stability and trajectory following.

Adaptive and Resilient Flight Control System for a Small Unmanned Aerial System

The main purpose of this paper is to develop an onboard adaptive and robust flight control system that improves control, stability, and survivability of a small unmanned aerial system in off-nominal or out-of-envelope conditions. The aerodynamics of aircraft associated with hazardous and adverse onboard conditions is inherently nonlinear and unsteady. The presented flight control system improves functionalities required to adapt the flight control in the presence of aircraft model uncertainties. The fault tolerant inner loop is enhanced by an adaptive real-time artificial neural network parameter identification to monitor important changes in the aircraft’s dynamics due to nonlinear and unsteady aerodynamics. The real-time artificial neural network parameter identification is done using the sliding mode learning concept and a modified version of the self-adaptive Levenberg algorithm. Numerically estimated stability and control derivatives are obtained by delta-based methods. New nonlinear guidance logic, stable in Lyapunov sense, is developed to guide the aircraft. The designed flight control system has better performance compared to a commercial off-the-shelf autopilot system in guiding and controlling an unmanned air system during a trajectory following.