Adaptive Autopilot Research Articles

Advanced autopilot design with extremum-seeking control for aircraft control

Abstract The aim of this research is to enhance adaptive autopilots for the effective management of aircraft systems, control maintenance, and the rejection of external disturbances. To achieve this objective, we propose the design of an autopilot integrated with the extremum-seeking control (ESC) algorithm. Although autopilots proficiently manage the lateral and longitudinal modes of aircraft control, they lack filtering or adaptive capabilities, thereby exposing the system to significant external threats. To mitigate these risks, the ESC method is employed. This adaptive approach can operate in a disturbance rejection manner by adjusting parameters for unknown inputs and restoring the system to its original controlled response. ESC represents a versatile control method suitable for effective application in simulations or experimental models. Through the incorporation of this method, the pitch attitude hold autopilot, altitude hold autopilot, and yaw autopilot acquire advanced disturbance rejection capabilities with adaptive ESC features. The novelty of the proposed method lies in providing advanced disturbance rejection properties to conventional autopilots, thereby rendering them innovative and superior disturbance rejection controllers. The newly developed autopilots are capable of eliminating severe disturbances from the system response, including ramp, sinusoidal, and step disturbances. The integration of autopilots with ESC offers significant advantages, such as superior disturbance rejection properties for the aircraft unmanned aerial vehicle (UAV) system. The proposed method successfully eliminates severe disturbances, as demonstrated in simulation results, surpassing previous methods in effectiveness. Furthermore, the Autopilot-ESC method enhances aircraft operation even under disturbances, minimizing energy consumption and ensuring stability and control. This novel method reduces operator workload and ensures reliable and efficient autonomous flight capabilities. Additionally, the adaptability of the Autopilot-ESC to changing environmental conditions make it well-suited in aircraft UAVs. This upgraded version of autopilot surpasses other robust controllers, such as Linear Quadratic Gaussian (LQG) regulator and Model Predictive Control (MPC), as it can effectively address ramp, sinusoidal, and step disturbances, which LQG and MPC cannot handle.

An Underactuated Control System Design for Adaptive Autopilot of Fixed-Wing Drones

Effective design of autopilots for fixed-wing unmanned aerial vehicles (UAVs) is still a great challenge, due to unmodeled effects and uncertainties that these vehicles exhibit during flight. Unmodeled effects and uncertainties comprise longitudinal/lateral cross-couplings, as well as poor knowledge of equilibrium points (trimming points) of the UAV dynamics. The main contribution of this article is a new adaptive autopilot design, based on uncertain Euler–Lagrange dynamics of the UAV and where the control can explicitly take into account under-actuation in the dynamics, reduced structural knowledge of cross-couplings and trimming points. This system uncertainty is handled via appropriately designed adaptive laws: stability of the controlled UAV is analyzed. Hardware-in-the-loop tests, comparisons with an Ardupilot autopilot and with a robustified autopilot validate the effectiveness of the control design, even in the presence of strong saturation of the UAV actuators.

Online closed loop system identification for the actuator of a flying vehicle

The frequent minor wars have brought to the fore guided missiles as the main weapon against all types of military targets. Restoring the single shot kill probability can be achieved by using two methods as follows: first, using large warhead with the large lethal area; second, design a robust guidance system to reduce the miss-distance with high single shot kill probability. The performance of an anti-tank guided missile can be enhanced by select the nominal model to design the guidance computer system. Furthermore, an online closed-loop system identification is required to adaptive autopilot design. This paper is devoted to using system identification in position control robotic benchmark via open and closed loops system identification. Hence, applying the closed-loop methods to identify the actuator and airframe of the intended plant in order to design a self-tuning controller. The online system identification is based on the recursive least square method.

Fuzzy Self-Tuning of Strictly Negative-Imaginary Controllers for Trajectory Tracking of a Quadcopter Unmanned Aerial Vehicle

Robustness in the face of uncertainties is an integral part of designing a real-time control system. Based on negative imaginary (NI) systems theory, we design robust and adaptive control systems for accurate trajectory tracking of a quadcopter aerial vehicle. Considering the challenging dynamics of unmanned aerial vehicles, we employ knowledge-based fuzzy inference systems (FIS) to facilitate automatic tuning in our SNI controllers, leading to the development of adaptive SNI control systems. Unlike fixed-gain controllers that have no ability to adapt to the variations in environmental conditions or changes in the dynamics of the plant, our adaptive SNI controllers are able to perform self-tuning to constantly update their parameters. The concept of adaptive autopilots will enhance the ability of the closed-loop control systems to accommodate large uncertainties. To demonstrate their efficacy, we design and implement our adaptive SNI controllers in the three-position control loops of the AR.Drone quadcopter after conducting extensive computer simulations. We also perform a rigorous comparative study with respect to the performance of fixed-gain SNI controllers, fixed-gain NI systems, in addition to model-predictive-control systems, and proportional integral derivative (PID) control systems as our benchmarks. To complete the study, we conduct a stability analysis based on Kharitonov's Theorem.

Accuracy of Trajectory Tracking Based on Nonlinear Guidance Logic for Hydrographic Unmanned Surface Vessels.

A new trend in recent years for hydrographic measurement in water bodies is the use of unmanned surface vehicles (USVs). In the process of navigation by USVs, it is particularly important to control position precisely on the measuring profile. Precise navigation with respect to the measuring profile avoids registration of redundant data and thus saves time and survey costs. This article addresses the issue of precise navigation of the hydrographic unit on the measuring profile with the use of a nonlinear adaptive autopilot. The results of experiments concerning hydrographic measurements performed in real conditions using an USV are discussed.

An Optimal Algorithm for Midcourse Guidance Law Under Wind Disturbance

The guidance laws are commonly designed to yield as small a miss distance as possible, harmonious with the missile’s acceleration capability. In recent decades, the concept of optimized guidance law is well understood in applications where information concerning the target range and line-of-sight angle is available. Researchers' efforts have been continually made to apply modern control theory to conventional and adaptive autopilot designs, even though the classical theory is still applicable to autopilots. It can be noted that it is desirable to perform a detailed computer-aided feasibility study within the context of a realistic missile-target engagement model. Development and evaluation of guidance and control laws for simplified missile-target engagement scenarios are extended and adapted to the air-to-air missile situation and implemented in a complete three-dimensional engagement model. Thus, this study proposed a computational method for constructing an optimal midcourse guidance law, which is based on the optimal control theory and initial boundary conditions. This proposed guidance law is derived from an optimal control theory with the boundary conditions such as allowed relative distance between missile and target at the final time, low line-of-sight rate. A numerical simulation verifies the performance of this guidance law with the impact of harmonic wind. The simulation results demonstrate that the quality of effectiveness as well as the applicability of this proposed algorithm.

A Robust Hybrid of a Feedback Linearization Technique and an Interval Type-2 Fuzzy Control System for the Flapping Angle Dynamics of a Biomimetic Aircraft

We introduce a new configuration of a robust and adaptive autopilot system for a model-scale flapping-wing aircraft. The system is specifically designed to achieve high performance flapping angle tracking in the face of large uncertainties. To describe the dynamics of the system, we leverage the benefits of both first principle modeling and data-driven approach (system identification technique). We introduce a high-performance robust and adaptive nonlinear control system by means of a feedback linearization (FL) technique, supported with an interval Type-2 fuzzy system due to its ability to accommodate the footprint-of-uncertainties (FoUs). While the first stage of our nonlinear control system is to cancel some predictable nonlinearities using an FL technique, the second phase of control is to accommodate the existing uncertainties in the system by way of an interval Type-2 fuzzy control technique, e.g., due to imperfect cancelation and modeling errors. This way, the stability and the robustness of the closed-loop control system can be guaranteed. We quantify the relative merit of our hybrid control system with respect to an FL technique, supported with a fixed gain state feedback controller and a Type-1 fuzzy system. Lastly, we also conduct stability analysis of the overall closed-loop control system.

Shared Control Between Adaptive Autopilots and Human Operators for Anomaly Mitigation

Abstract As aerial vehicles become more autonomous, and guidance and navigation systems become increasingly network-centric, there is a need to consider a swift response to the growing forms of anomalies that may occur in these systems. We introduce a shared decision-making and control framework between a human operator and an adaptive autopilot, where the human operator plays a supervisory role and the adaptive autopilot retains the responsibility for low-level regulation and command tracking tasks. The human operator provides key inputs based on a higher-level perception of the anomaly, such as an increased lag in response to command inputs, which are then used by the adaptive autopilot in a suitable manner. The resulting shared control architecture is demonstrated on an unmanned aerial vehicle, whose actuators abruptly change from first-order to second-order due to an anomaly.

INTEGRAL ADAPTIVE AUTOPILOT FOR AN UNMANNED AERIAL VEHICLE

The aim of research is to study the modern algorithms used in autopilots of unmanned aerial vehicles and formulation of the problem of development and usage of new intellectual methods for automatic control systems. The approach considered in the article is based on the theory of high-precision remote control of dynamic objects and on the complex interaction of methods of theory of invariance, adaptive control and intellectualization of processes of UAV control. One of the features of the proposed method of intellectual control for unmanned aerial vehicle autopilot is the procedure of transforming a multi-dimensional system into an aggregate of virtual autonomous processes, for each of which the control algorithm is easily generated by an autonomous subsystem. Coming up next is the procedure of coordination of actions of all the autonomous systems into single functioning complex. This provides an opportunity to improved precision and sustainability of control. Using the method described in the article allows creating integral and adaptive autopilots to perform complicated spatial maneuvering an unmanned aerial vehicle being based on usage of full non-linear models without simplifications and linearization.

Acceleration autopilot for a guided spinning rocket via adaptive output feedback

Uncertainties in control effectiveness, and moment coefficients are among the practical challenges in control of flight vehicles. Adaptive control is known as a proper method to handle uncertain systems, and has been used in numerous applications to improve system performance in the presence of system uncertainties. This paper presents a new method of synthesizing an acceleration autopilot for a guided spinning rocket, which is a class of uncertain, non-square multi-input/multi-output system. Firstly, a nonlinear and coupled six-degree-of-freedom (6-DoF) dynamic model is established, which is used to evaluate the performance of the proposed adaptive autopilot during the whole operating cycle. Secondly, a simple design procedure based on square-up method and linear matrix inequality (LMI) is proposed to design the autopilot, allowing a globally stable adaptive output feedback law to be generated. Finally, the adaptive output feedback autopilot is applied to the nonlinear 6-DoF dynamic model and it is shown to result in stable tracking in the presence of uncertainties.

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.

Robust Adaptive Output Feedback Control for a Guided Spinning Rocket

An adaptive autopilot is presented for the pitch and yaw channels of a guided spinning rocket. Firstly, the uncertain dynamic model of a guided spinning rocket is established, which is used to evaluate the performance of the proposed adaptive autopilot. Secondly, a robust adaptive output feedback autopilot containing a baseline component and an adaptive component is designed. The main challenge that needs to be addressed is the determination of a corresponding square and strictly positive real transfer function. A simple design procedure based on linear matrix inequality is proposed that allows the realization of such a transfer function, thereby allowing a globally stable adaptive output feedback law to be generated. Finally, numerical simulations are performed to evaluate the robustness and tracking performance of the proposed robust adaptive autopilot. The simulation results showed that the robust adaptive output autopilot can achieve asymptotic command tracking with significant uncertainty in control effectiveness, moment coefficient, and measurement noise.

A robust adaptive control approach to missile autopilot design

The huge developments in computational capabilities facilitate the design and implementation of adaptive and robust control. Furthermore, the great developments in nanotechnology and its availability in civilian level with less cost, weight, and size attract the researchers all over the world towards embedded systems especially the embedded flight control. One of the real applications are the guided missiles especially the anti-tank guided missile systems which are launched against the ground and short-range targets and is called command line of sight. The present work is concerned with improving the performance of an anti-tank guided missile system belonging to the first generation via adaptive synthesis of guidance systems. The online system identification is required to complete the cycle of adaptive autopilot design. This paper is devoted to designing an adaptive autopilot for the intended system using model reference and self-tuning regulator with justification against previous work and reference flight data concerning the performance requirements of time responses and flight path characteristics. The new design is implemented within the 6-DOF simulation from which the obtained results clarify its capability to stabilize the system in presence of unmodeled dynamics and satisfy the performance requirements with disturbance rejection and measurement noise attenuation. Also, the flight path is evaluated considering the HIL environment.

Bumpless Reengagement Using Shared Control between Human Pilot and Adaptive Autopilot

In this paper, we consider a flight control problem subjected to actuator anomalies. We propose a shared control architecture that includes actions of both a human pilot and an adaptive autopilot that achieves bumpless performance while retaining a Capacity for Maneuver (CfM) following the anomaly. The shared controller distributes the critical tasks of anomaly perception and adaptation between a human pilot and an adaptive autopilot to best utilize their individual strengths. The human pilot’s expertise in detection and perception of anomaly is utilized to determine an initial estimate of the plant parameters. An adaptive autopilot then carries out further tuning of the controller to minimize the tracking error while retaining a certain CfM. The shared controller is evaluated in simulation study of an F-8 aircraft under actuator anomalies and shown to be advantageous compared to a fixed-gain fault tolerant controller.

Longitudinal nonlinear adaptive autopilot design for missiles with control constraint

This paper develops a new nonlinear adaptive longitudinal autopilot for the control of missiles with control input constraint, in the presence of parametric uncertainties and external disturbance input. The objective here is to control the angle of attack of the missile. A saturating control law is derived for the trajectory control of the angle of attack. The control law includes an auxiliary dynamic system in the feedback loop, driven by control input error signal, caused by control saturation, to preserve stability in the closed-loop system. By the Lyapunov stability analysis, it is shown that in the closed-loop system, the system trajectories are uniformly ultimately bounded. Simulation results show that the designed autopilot with constrained input can accomplish accurate trajectory control if the control saturation period is short. It is also seen that although the tracking error increases with the saturation period, the angle of attack tends to zero, once the command input is set to zero. Furthermore this adaptive control system, including the control error signal feedback loop, performs better than the adaptive laws, designed earlier based on immersion and invariance principle, without control magnitude constraint.

Design of an adaptive missile autopilot considering the boost phase using the SDRE method and neural networks

An adaptive autopilot to control a skid-to-turn missile during its boost phase is designed using the state-dependent Riccati equation (SDRE) method and neural networks (NN). To address the rapid changes in parameters during the boost phase, the translational and rotational motions of the missile are modeled with time-varying velocity and inertial parameters. The autopilot with a two-loop structure is designed to perform integrated roll-pitch-yaw control of the missile with cross-coupled dynamics; each loop has a baseline controller and an adaptive controller. The baseline controller is designed using the SDRE method for reference command tracking in a nominal environment, and the adaptive controller is designed based on NN to manage uncertainty during the boost phase. Stability analysis of the closed-loop system is performed, and the performance of the proposed autopilot is demonstrated by numerical simulation.

Model reference adaptive autopilot with anti-windup compensator for an autonomous underwater vehicle: Design and hardware in the loop implementation results

In this paper, a novel model reference adaptive controller with anti-windup compensator (MRAC_AW) is proposed for an autonomous underwater vehicle (AUV). Input saturations and parametric uncertainties are among the practical problems in the control of autonomous vehicles. Hence, utilizing a proper adaptive controller with the ability to handle actuator saturations is of a particular value. The proposed technique of this paper incorporates the well-posed model reference adaptive control with integral state feedback and a modern anti-windup scheme to present an appropriate performance in practical conditions of an AUV. Stability of the proposed method is analyzed by Lyapunov theory. Then, the proposed controller is implemented in the hardware in the loop simulation of AUV. For this purpose, the introduced method is implemented in an onboard computer to be checked in a real-time dynamic simulation environment. Obtained results in the presence of real hardware of system, actuators, computational delays and real-time execution verify the effectiveness of proposed scheme.

Adaptive integral feedback controller for pitch and yaw channels of an AUV with actuator saturations

Input saturations and uncertain dynamics are among the practical challenges in control of autonomous vehicles. Adaptive control is known as a proper method to deal with the uncertain dynamics of these systems. Therefore, incorporating the ability to confront with input saturation in adaptive controllers can be valuable. In this paper, an adaptive autopilot is presented for the pitch and yaw channels of an autonomous underwater vehicle (AUV) in the presence of input saturations. This will be performed by combination of a model reference adaptive control (MRAC) with integral state feedback with a modern anti-windup (AW) compensator. MRAC with integral state feedback is commonly used in autonomous vehicles. However, some proper modifications need to be taken into account in order to cope with the saturation problem. To this end, a Riccati-based anti-windup (AW) compensator is employed. The presented technique is applied to the non-linear six degrees of freedom (DOF) model of an AUV and the obtained results are compared with that of its baseline method. Several simulation scenarios are executed in the pitch and yaw channels to evaluate the controller performance. Moreover, effectiveness of proposed adaptive controller is comprehensively investigated by implementing Monte Carlo simulations. The obtained results verify the performance of proposed method.

Robust Adaptive Control of a Weakly Minimum Phase General Aviation Aircraft

Loss of control is the leading cause of accidents in General Aviation aircraft. Installation of autopilots on every airplane would significantly reduce this type of accident. Adaptive controls could offer an affordable autopilot for a wide variety of aircraft, by allowing the gains to automatically adapt. This investigation presents the design and test of an adaptive autopilot. The autopilot was designed to control the altitude and bank angle of a small airplane at a specific trim condition. The airplane was then moved to extreme points in its flight envelope, and the autopilot successfully adapted to maintain controller performance, except at airspeeds near stall. Since the aircraft is weakly minimum phase, a zero filter was used in the adaptive autopilot, which required a nominal model of the linearized plant at the design condition. Turbulence was added to test robustness, and the autopilot was able to maintain its performance.

Adaptive Fuzzy Autopilot for a Scale Ship Model

This paper discusses the implementation of an adaptive fuzzy autopilot for a scale ship model using a Fuzzy Model Reference Learning Controller (FMRLC). Initially, a fuzzy singleton model of the scale ship model is obtained from experimental input and output data gathered from the turning circle manoeuvre. After that, the paper describes how to design a Fuzzy Model Reference Learning Controller (FMRLC) for controlling the directional heading of the scale ship model. At the end, results of the implementation of the adaptive ship autopilot are showed.