Intelligent Flight Control System Research Articles

BASIC PRINCIPLES OF BUILDING INTELLIGENT SYSTEMS FLIGHT CONTROL

The issues of creating intelligent control systems in a dynamically changing flight environment are considered and the principle of constructing an onboard intelligent flight control system is proposed. The implementation of the proposed system is based on the pilot's situational awareness of the flight progress in real time, which allows avoiding errors and maintaining enhanced synergy between human and avionics systems. Constant adaptation of the cockpit, as well as onboard. automation successfully maintains the pilot's workload within an optimal range, mitigating the occurrence of dangerous levels of fatigue. Methods for assessing and predicting the threat of an aviation accident based on direct control of the variation in the values of characteristics that affect flight safety, using methods and tools of artificial intelligence, are proposed. Keywords: flight safety, aircraft, intelligent aviation system, accident threat, prevention, control system, artificial intelligence, aircraft control systems

PPO-Based Attitude Controller Design for a Tilt Rotor UAV in Transition Process

The complex aerodynamic changes of the tilt-rotor UAV (TRUAV) in the transition process show strong nonlinearity, which brings a great impact on the stability of the vehicle attitude. This study aims to design a PPO-based RL controller for attitude control in the transition process. A reinforcement-learning PPO approach is used to learn the control strategy by interacting directly with the environment. And the reward function is designed and improved for the transition process. The performance of the proposed controller is tested and compared by simulation. The results show that the PPO algorithm is more suitable for the tilt-rotor transition process control than the A2C algorithm. Our proposed reward function improves the attitude control performance and the designed RL controller has good adaptability to changes in the takeoff weight, the diagonal wheelbase and the tilt rate. This study highlights the effectiveness and potential of reinforcement learning for tilt-rotor UAV transition process attitude control. These findings contribute to the advancement of autonomous flight systems by providing insights into the application of reinforcement learning algorithms. These results have important implications for the development of intelligent flight control systems and could guide future research in this area.

Smart aerosonde UAV longitudinal flight control system based on genetic algorithm

Synthesis of a flight control system for such an aircraft that achieves stable and acceptable performance across a specified flying envelope in the presence of uncertainties represents an attractive and challenging design problem. This study uses the genetic self-tuning PID algorithm to develop an intelligent flight control system for the aerosonde UAV model. To improve the system's transient responses, the gains of the PID controller are improved using a genetic algorithm (GA). Simulink/MATLAB software is used to model and simulate the proposed system. The proposed PID controller integrated with the GA is compared with the classical one. Three simulation scenarios are carried out. In the first scenario, and at normal conditions, the proposed controller performance is better than the classical one. While in the second scenario, identical results are achieved from both controllers. Finally, in the third scenario, the PID controller with GA shows the robustness and durability of the system compared with the classical PID in presence of external wind disturbance. The simulation results prove the system parameters optimization.

Characteristics and design key points of small CIGS solar UAV

Based on the working mode of small CIGS solar UAV, the difference and basic characteristics between its energy power system and traditional UAV are analyzed. Some key problems to be solved in the miniaturization design of flight platform are studied from airfoil selection, aerodynamic layout design and basic parameter calculation. By increasing the power factor of the airfoil and reducing the wing load, the UAV can get a smaller sinking rate. In addition, through the coupling of energy system and power system and the use of new composite materials, the overall efficiency can also be improved and the endurance of solar UAV can be effectively extended. Combined with the intelligent flight control system, it can realize full autonomous over the horizon long-time flight. The main conclusions can provide reference for the miniaturization design and optimization of solar UAV.

Reinforcement Learning for UAV Attitude Control

Autopilot systems are typically composed of an “inner loop” providing stability and control, whereas an “outer loop” is responsible for mission-level objectives, such as way-point navigation. Autopilot systems for unmanned aerial vehicles are predominately implemented using Proportional-Integral-Derivative (PID) control systems, which have demonstrated exceptional performance in stable environments. However, more sophisticated control is required to operate in unpredictable and harsh environments. Intelligent flight control systems is an active area of research addressing limitations of PID control most recently through the use of reinforcement learning (RL), which has had success in other applications, such as robotics. Yet previous work has focused primarily on using RL at the mission-level controller. In this work, we investigate the performance and accuracy of the inner control loop providing attitude control when using intelligent flight control systems trained with state-of-the-art RL algorithms—Deep Deterministic Policy Gradient, Trust Region Policy Optimization, and Proximal Policy Optimization. To investigate these unknowns, we first developed an open source high-fidelity simulation environment to train a flight controller attitude control of a quadrotor through RL. We then used our environment to compare their performance to that of a PID controller to identify if using RL is appropriate in high-precision, time-critical flight control.

State-of-the-Art Intelligent Flight Control Systems in Unmanned Aerial Vehicles

We discuss state-of-the-art intelligent robotic aircraft with the special focus on evolutionary autopilots for small unmanned aerial vehicles (UAVs). Under the umbrella of adaptive autopilots, we highlight the pros and cons of the most widely implemented intelligent algorithms against the navigational and maneuvering capabilities of small UAVs. We present several cutting-edge applications of bioinspired flight control systems that have the capability of self-learning. We also highlight several research opportunities and challenges associated with each technique.

Intelligent Flight Control System Design for the Small UAV Based on the Adaptive Neuro-Fuzzy Inference System

Intelligent Flight Control System Design for the Small UAV Based on the Adaptive Neuro-Fuzzy Inference System

Modified shuffled frog leaping algorithm optimized control for air-breathing hypersonic flight vehicle

This article addresses the flight control problem of air-breathing hypersonic vehicles and proposes a novel intelligent algorithm optimized control method. To achieve the climbing, cruising and descending flight control of the air-breathing hypersonic vehicle, an engineering-oriented flight control system based on a Proportional Integral Derivative (PID) method is designed for the hypersonic vehicle, which including the height loop, the pitch angle loop and the velocity loop. Moreover, as a variant of nature-inspired algorithm, modified shuffled frog leaping algorithm is presented to optimize the flight control parameters and is characterized by better exploration and exploitation than the standard shuffled frog leaping algorithm. A nonlinear model of air-breathing hypersonic vehicle is used to verify the dynamic characteristics achieved by the intelligent flight control system. Simulation results demonstrate that the proposed swarm intelligence optimized PID controllers are effective in achieving better flight trajectory and velocity control performance than the traditional controllers.

Intelligent Flight Control Systems Evaluation for Loss-of-Control Recovery and Prevention

Recent developments in the field of loss-of-control recovery and prevention included improved pilot training, cockpit automation, and fault-tolerant control. The Flight Mechanics Action Group on Fault-Tolerant Control of the Group for Aeronautical Research and Technology in Europe demonstrated the advantages of fault-tolerant “intelligent,” flight control systems. The research enabled the improvement of the technology readiness level of these systems by evaluating one of them in realistic operational scenarios. The handling qualities results of a piloted flight simulator assessment with a damaged aircraft model showed that an online physical model identification approach contributed to improved pilot performance following potentially catastrophic structural and flight critical system failures. After the failures and subsequent control reconfiguration by the intelligent flight control system, airline and engineering test pilots experienced no difficulties in conducting a safe approach and landing.

0418 Performance of a High-order LES Model Related to the Effect of Jet on Airfoil profile change(OS4-4 流体機械の研究開発とそれに関連した複雑流動現象,OS4 流体機械の研究開発とそれに関連した複雑流動現象)

Recent aircraft morphing program envisions an aircraft without conventional control surfaces. Instead of moving control surfaces, the vehicle control systems may be implemented with a combination of propulsive forces, micro-surface-effectors, and fluidic devices dynamically operated by an intelligent flight control system. In the present work, first of all, the accuracy of a high-order LES turbulent model, developed by the author is verified by applying it to predict the experimental drag and lift coefficient for (NACA4412) airfoil. Finally, the work described in this paper focused on the aerodynamic virtual shaping of an (NACA0012) symmetric airfoil at zero angle of attack, using only a thin jet without additional fixtures. The effects of virtual shaping are investigated systematically with jet actuation at different chord-wise locations, and different ratio of jet and free-stream velocity. The jet was used to alter the aerodynamic shape of a symmetric airfoil to enhance lift and/or to reduce drag.

UAV Flight Control System Based on an Intelligent BEL Algorithm

A novel intelligent control strategy based on a brain emotional learning (BEL) algorithm is investigated in the application of the attitude control of a small unmanned aerial vehicle (UAV) in this study. The BEL model imitates the emotional learning process in the amygdala- orbitofrontal (A-O) system of mammalian brains. Here it is used to develop the flight control system of the UAV. The control laws of elevator, aileron and rudder manipulators adopt the forms of traditional flight control laws, and three BEL models are used in above three control loops, to on- line regulate the control gains of each controller. Obviously, a BEL intelligent control system is self-learning and self-adaptive, which is important for UAVs when flight conditions change, while traditional flight control systems remain unchanged after design. In simulation, the UAV is on a flat flight and suddenly a wind disturbs it making it depart from the equilibrium state. In order to make the UAV recover to the original equilibrium state, the BEL intelligent control system is adopted. The simulation results illustrate that the BEL-based intelligent flight control system has characteristics of better adaptability and stronger robustness, when compared with the traditional flight control system.

Stabilator Failure Adaptation from Flight Tests of NF-15B Intelligent Flight Control System

Adaptive flight control systems have the potential to be more resilient to extreme changes in airplane behavior. Extreme changes could be a result of a system failure or of damage to the airplane. A direct adaptive neural-network-based flight control system was developed for the National Aeronautics and Space Administration NF-15B Intelligent Flight Control System airplane and subjected to an inflight simulation of a failed (frozen) (unmovable) stabilator. Formation flight handling qualities evaluations were performed with and without neural network adaptation. The results of these flight tests are presented. Comparison with simulation predictions and analysis of the performance of the adaptation system are discussed. The performance of the adaptation system is assessed in terms of its ability to decouple the roll and pitch response and reestablish good onboard model tracking. Flight evaluation with the simulated stabilator failure and adaptation engaged showed that there was generally improvement in the pitch response; however, a tendency for roll pilot-induced oscillation was experienced. A detailed discussion of the cause of the mixed results is presented.

INTEGRATION OF SENSOR AND ACTUATOR FAILURE DETECTION, IDENTIFICATION, AND ACCOMMODATION SCHEMES WITHIN FAULT TOLERANT CONTROL LAWS

This paper describes the design of a fault tolerant scheme for coping with both sensor and actuator failures within a flight control system. The failure detection and identification scheme is based on the use of neural estimators interfaced with correlation functions of the aircraft angular rates. Particular emphasis is placed on the differentiation between sensor and actuator failures. The failure types considered are actuator blockage along with partial/total loss of aerodynamic efficiency of the control surface and angular rate sensor step-type failure. The design of the accommodating control laws for actuator failures is based upon a non-linear dynamic inversion approach with neural network augmentation. The accommodation of sensor failures is performed by replacing the failed sensor output with neural estimates computed as part of the detection and identification process. The performance of the scheme is evaluated using the non-linear simulator for the NASA Intelligent Flight Control System F-15 aircraft developed at West Virginia University. The simulation results confirm the capabilities of the scheme to handle both sensor and actuator failures of different types over a large range of magnitudes.

A validation approach for neural network‐based online adaptive systems

AbstractTraditional software validation methods cannot guarantee the safe behavior of online self‐adaptive systems. These systems are characterized by continual adaptation to changing environmental conditions. We present a novel methodology for validating adaptive software systems based on online operational monitoring. The methodology inherits its theoretical underpinnings from the generic stability and convergence analysis of Lyapunov's theory. Lyapunov theory is well established in mathematics and control theory, but has not been used before for software validation. The presented validation technique is applied to a neural network‐based online self‐adaptive system, the intelligent flight control system. In this application, environmental changes include system failure modes, such as a stuck stabilator, broken aileron and/or rudder, sensor failure, etc. Our case study for validation is a specific online self‐adaptive system, the intelligent flight control that utilizes dynamic cell structures' neural networks to perform online adaptation. The theoretical foundation and practicability of the presented validation technique make the presented validation approach generally applicable to other types of online adaptive systems. Copyright © 2006 John Wiley & Sons, Ltd.

Monitoring techniques for an online neuro-adaptive controller

The appeal of biologically inspired soft computing systems such as neural networks in complex systems comes from their ability to cope with a changing environment. Unfortunately, adaptability induces uncertainty that limits the applicability of static analysis to such systems. This is particularly true for systems with multiple adaptive components or systems with multiple types of learning operation. This work builds a paradigm of dynamic analysis for a neuro-adaptive controller where different types of learning are to be employed for its online neural networks. We use support vector data description as the novelty detector to detect unforeseen patterns that may cause abrupt system functionality changes. It differentiates transients from failures based on the duration and degree of novelties. Further, for incremental learning, we utilize Lyapunov functions to assess real-time performance of the online neural networks. For quasi-online learning, we define a confidence measure, the validity index, to be associated with each network output. Our study on the NASA F-15 Intelligent Flight Control System demonstrates that our novelty detection tool effectively filters out transients and detects failures; and our light-weight monitoring techniques supply sufficient evidence for an insightful validation.

An adaptive threshold approach for the design of an actuator failure detection and identification scheme

Typical logic schemes associated with failure detection and identification algorithms rely on a set of constant thresholds. The selection of the values for these thresholds is generally a tradeoff between the goals of maximizing failure detectability while minimizing false alarm rates. The main purpose of this brief is to propose an alternative to this conventional approach for defining the thresholds of a specific aircraft actuator failure detection and identification scheme. A specific set of detection and identification criteria for failures of the decoupled stabilators, canards, ailerons, and rudders of the NASA Advanced Control Technology for Integrated Vehicle F-15 aircraft have been formulated in terms of neural network estimates and correlation functions of the angular rates. The proposed scheme is based on the use of adaptive thresholds through the floating limiter concept. This new approach eliminates the need for parameter scheduling and has shown to be able to reduce the delays associated with the constant threshold method. The functionality of the approach has been illustrated through numerical simulations on the West Virginia University NASA Intelligent Flight Control System F-15 simulator.

Design of safety monitor schemes for a fault tolerant flight control system

For a research aircraft, "conventional" control laws (CLs) are implemented on a "baseline" flight computer (FC) while research CLs are typically housed on a dedicated research computer. Therefore, for an experimental aircraft used to test specific fault tolerant flight control systems, a safety logic scheme is needed to ensure a safe transition from conventional to research CLs (while at nominal conditions) as well as from research CLs at nominal conditions to conditions with "simulated" failures on specific control surfaces. This paper describes the design of such a safety scheme for the NASA Intelligent Flight Control System (IFCS) F-15 Program. The goals of the IFCS F-15 program are to investigate the performance of a set of fault tolerant CLs based on the use of dynamic inversion with neural augmentation. The different transitions are monitored using information relative to flight conditions and controller-related performance criteria. The testing of the scheme is performed with a Simulink-based flight simulation code and interface developed at West Virginia University for the NASA IFCS F-15 aircraft.

Modelling and Simulation of a Fault-Tolerant Flight Control System

This paper describes the results of an attempt to develop a modelling and simulation environment for the closed-loop dynamics of the NASA Intelligent Flight Control System F-15 aircraft. Emphasis was placed on the modelling of fault tolerance capabilities for the aircraft flight control system. Failures of the primary control surfaces are simulated for the most common failure scenarios, which are actuator blockage with and without a missing portion of the control surface. The controller featured in the flight control system has an optimal “feedforward and feedback” architecture to provide desirable handling qualities at nominal flight conditions while retaining good performance at post-failure conditions. The parameters within the control laws are updated using an online real-time parameter identification scheme based on the Fourier Transform Regression method. The desirable performance and utility of the entire scheme are illustrated through numerical simulations in the event of stabilator failure.

Augmentation of an Intelligent Flight Control System for a Simulated C-17 Aircraft

This paper examines a neural-adaptive flight control system augmented with linear programming theory and adaptive critic techniques for a simulated C-17 aircraft. The baseline Intelligent Flight Control (IFC) system is composed of a neural network based direct adaptive control approach for applying alternate sources of control authority in the presence of damage or failures in order to achieve desired flight control performance. Neural networks are used to provide consistent handling qualities across flight conditions, adapt to changes in aircraft dynamics and make the controller easy to apply when implemented on different aircraft. In this study, IFC has been augmented with linear programming (LP) theory and adaptive critic technologies. LP is used to optimally allocate requested control deflections and the adaptive critic modifies the parameters of the aircraft reference model for consistent handling qualities. Full-motion piloted simulation studies were performed on a Boeing C-17. Subjects included NASA and Air Force pilots. Results, including subjective pilot ratings and time response characteristics of the system, demonstrate the potential for improving handing qualities and significantly increased survivability rates under various simulated failure conditions.

Neural network based tools for uncertainty management with applications to control system design

Almost all kinds of men’s activities associated with design, manufacturing and maintenance of complex systems consist of two main classes of problems: a generation of decision alternative set allowed for situation studied and decision making, i.e. choice of some decision alternative from the set of allowed ones. A role of various kind of uncertainties related to complex system design process is discussed founding on intelligent flight control system design example. A mixed information technology is used to solve this problem basing on mathematical modeling, multi-purpose system methodology and artificial neural networks. This is very promising approach for a solving not only FCS gain scheduling problem, but another kinds of FCS synthesis problems. The approach promises also to ensure effective technical decision generation and making tool for problem solving with various kind of uncertainties involved.