Autopilot Research Articles

Autonomous flight performance maximization for slung load carrying rotary wing mini unmanned aerial vehicle

PurposeThis research study aims to minimize autonomous flight cost and maximize autonomous flight performance of a slung load carrying rotary wing mini unmanned aerial vehicle (i.e. UAV) by stochastically optimizing autonomous flight control system (AFCS) parameters. For minimizing autonomous flight cost and maximizing autonomous flight performance, a stochastic design approach is benefitted over certain parameters (i.e. gains of longitudinal PID controller of a hierarchical autopilot system) meanwhile lower and upper constraints exist on these design parameters.Design/methodology/approachA rotary wing mini UAV is produced in drone Laboratory of Iskenderun Technical University. This rotary wing UAV has three blades main rotor, fuselage, landing gear and tail rotor. It is also able to carry slung loads. AFCS variables (i.e. gains of longitudinal PID controller of hierarchical autopilot system) are stochastically optimized to minimize autonomous flight cost capturing rise time, settling time and overshoot during longitudinal flight and to maximize autonomous flight performance. Found outcomes are applied during composing rotary wing mini UAV autonomous flight simulations.FindingsBy using stochastic optimization of AFCS for rotary wing mini UAVs carrying slung loads over previously mentioned gains longitudinal PID controller when there are lower and upper constraints on these variables, a high autonomous performance having rotary wing mini UAV is obtained.Research limitations/implicationsApproval of Directorate General of Civil Aviation in Republic of Türkiye is essential for real-time rotary wing mini UAV autonomous flights.Practical implicationsStochastic optimization of AFCS for rotary wing mini UAVs carrying slung loads is properly valuable for recovering autonomous flight performance cost of any rotary wing mini UAV.Originality/valueEstablishing a novel procedure for improving autonomous flight performance cost of a rotary wing mini UAV carrying slung loads and introducing a new process performing stochastic optimization of AFCS for rotary wing mini UAVs carrying slung loads meanwhile there exists upper and lower bounds on design variables.

An Attitude Adaptive Integral Sliding Mode Control Algorithm with Disturbance Observer for Microsatellites to Track High-Speed Moving Targets

Gaze tracking of high-speed moving targets is a novel application mode for low Earth orbit microsatellites. In this mode, small satellites are equipped with high-resolution, narrow-field-of-view video cameras for stable gaze-tracking imaging of high-speed moving targets. This paper proposes a high-precision attitude adaptive integral sliding mode control method with a feedforward compensation disturbance observer to enhance the capability of a microsatellite attitude control system for gaze tracking of high-speed moving targets. Specifically, first, we present the attitude control system model for microsatellites and the calculation method for the desired attitude of target tracking based on image feedback. Then, an adaptive integral sliding mode attitude control algorithm with a feedforward compensation disturbance observer, which meets the requirements of high-precision tracking control, is designed. The developed algorithm utilizes the disturbance observer to observe the friction torque of the flywheel and compensates for it through feedforward control. It also employs the adaptive integral sliding mode control algorithm to reduce the impact of uncertain disturbances, decrease the steady-state error of the system, and enhance attitude control precision. Simulation experiments demonstrated that the designed disturbance observer can successfully observe the frictional disturbance torque of the flywheel. The attitude Euler angle control precision for high-speed moving target tracking reached 0.03°, and the angular velocity control precision reached 0.005°/s, validating the effectiveness of the proposed approach.

Driving Towards Safety: The Role of ECUs and IMUs in Advanced Driver-Assistance Systems (ADAS)

This research paper explores the pivotal role of Electronic Control Units (ECUs) and Inertial Measurement Units (IMUs) in Advanced Driver-Assistance Systems (ADAS), focusing on their functionalities, applications, regulatory frameworks, emerging technologies, and real-world case studies. Through a comprehensive review of literature, this paper investigates the integration of ECUs and IMUs in ADAS and its implications for vehicle safety, mobility, and sustainability. Key topics addressed include the adoption challenges, societal impact, international regulatory landscape, standardization efforts, emerging technologies such as artificial intelligence and sensor fusion, and real case studies exemplified by Tesla's Autopilot system, Volvo's City Safety system, and Waymo's autonomous taxi service. The paper concludes by highlighting the transformative potential of ECUs and IMUs in shaping the future of automotive technology and advancing towards safer, more efficient, and more sustainable transportation systems.

Feature Engineering Versus Deep Learning for Scene Recognition: A Brief Survey

Scene recognition is an important computer vision task that has evolved from the study of the biological visual system. Its applications range from video surveillance, autopilot systems, to robotics. The early works were based on feature engineering that involved the computation and aggregation of global and local image descriptors. Several popular image features such as SIFT, SURF, HOG, ORB, LBP, KAZE, etc. have been proposed and applied to the task with successful results. Features can be either computed from the entire image on a global scale, or extracted from local sub-regions and aggregated across the image. Suitable classifier models are deployed that learn to classify these features. This review paper analyzes several of these handcrafted features that have been applied to the scene recognition task over the past decades, and tracks the transition from the traditional feature engineering to deep learning which forms the current state of the art in computer vision. Deep learning is now deemed to have overtaken feature engineering in several computer vision applications. Deep convolutional neural networks and vision transformers are the current state of the art for object recognition. However, scenes from urban landscapes are bound to contain similar objects posing a challenge to deep learning solutions for scene recognition. In our study, a critical analysis of feature engineering and deep learning methodologies for scene recognition is provided, and results on benchmark scene datasets are presented, concluding with a discussion on challenges and possible solutions that may facilitate more accurate scene recognition.

Neural network-based robust adaptive super-twisting sliding mode fault-tolerant control for a class of tilt tri-rotor UAVs with unmodeled dynamics

Abstract Aiming at alleviating the adverse influence of coupling unmodeled dynamics, actuator faults and external disturbances in the attitude tracking control system of tilt tri-rotor unmanned aerial vehicle (UAVs), a neural network (NN)-based robust adaptive super-twisting sliding mode fault-tolerant control scheme is designed in this paper. Firstly, in order to suppress the unmodeled dynamics coupled with the system states, a dynamic auxiliary signal, exponentially input-to-state practically stability and some special mathematical tools are used. Secondly, benefiting from adaptive control and super-twisting sliding mode control (STSMC), the influence of the unexpected chattering phenomenon of sliding mode control (SMC) and the unknown system parameters can be handled well. Moreover, NNs are employed to estimate and compensate some unknown nonlinear terms decomposed from the system model. Based on a decomposed quadratic Lyapunov function, both the bounded convergence of all signals of the closed-loop system and the stability of the system are proved. Numerical simulations are conducted to demonstrate the effectiveness of the proposed control method for the tilt tri-rotor UAVs.

Rotary inverted pendulum control using neuro-adaptive robust generalized dynamic inversion

The objective of this research is to design a robust control system for trajectory tracking of the under-actuated Rotary Inverted Pendulum (RIP). The focus is on achieving semi-global asymptotic stability in the absence of knowledge about the RIP geometric and inertia parameters. The control design commences by formulating differential servo constraint dynamics (VCD) that encapsulates the control objectives for the RIP. The baseline generalized dynamic inversion (GDI) control law is derived by inverting the VCD for the control voltage input using the Moore–Penrose generalized inverse. To enhance robustness against modeling uncertainties and exogenous disturbances, a sliding mode control (SMC) element is integrated within the GDI control system, resulting in the robust GDI (RGDI) control system. Furthermore, an adaptive estimator that is based on Radial Basis Function Neural Networks (RBF-NN) is adopted to eliminate the dependency of the RGDI control system on the RIP mathematical model. The weighting matrices of the RBF-NN are updated with the aid of a Lyapunov control function. The proposed control system offers robust solution and guarantees semi-global asymptotic stability to the unstable equilibria of the RIP dynamics in the absence of a mathematical model. The proposed control system’s performance is evaluated through computer simulations and experimental tests on a real RIP system. Comparative analyses with traditional SMC and linear quadratic control methodologies are conducted to show the effectiveness of the proposed approach.

Research on Intelligent Vehicle Trajectory Tracking Control Based on Improved Adaptive MPC.

Intelligent vehicle trajectory tracking exhibits problems such as low adaptability, low tracking accuracy, and poor robustness in complex driving environments with uncertain road conditions. Therefore, an improved method of adaptive model predictive control (AMPC) for trajectory tracking was designed in this study to increase the corresponding tracking accuracy and driving stability of intelligent vehicles under uncertain and complex working conditions. First, based on the unscented Kalman filter, longitudinal speed, yaw speed, and lateral acceleration were considered as the observed variables of the measurement equation to estimate the lateral force of the front and rear tires accurately in real time. Subsequently, an adaptive correction estimation strategy for tire cornering stiffness was designed, an AMPC method was established, and a dynamic prediction time-domain adaptive model was constructed for optimization according to vehicle speed and road adhesion conditions. The improved AMPC method for trajectory tracking was then realized. Finally, the control effectiveness and trajectory tracking accuracy of the proposed AMPC technique were verified via co-simulation using CarSim and MATLAB/Simulink. From the results, a low lateral position error and heading angle error in trajectory tracking were obtained under different vehicle driving conditions and road adhesion conditions, producing high trajectory-tracking control accuracy. Thus, this work provides an important reference for improving the adaptability, robustness, and optimization of intelligent vehicle tracking control systems.

Collaborative control of path tracking and roll stability in intelligent commercial vehicle: A minimax robust regulator based on Nash game

—Currently, most vehicles are generally equipped with roll stability control system or electronic stability control system to improve stability. However, when encountering emergency obstacle avoidance and similar conditions, roll stability control system may compete with path tracking control system for vehicle control, resulting in the inability to balance path tracking and roll stability. Therefore, a closed-loop feedback Nash game collaborative control method is proposed to solve the collaborative problem between the two control systems. Firstly, a fifth-order vehicle model with coupled yaw-roll dynamics is presented, which was extended to a vehicle-road closed-loop model based on lateral position, yaw angle and road preview information. Secondly, the path tracking control system and roll stability control system are regarded as two agents in the dynamic game control process. On this basis, a Nash game collaborative control strategy is designed by fully considering the control interaction between two game agents. Finally, considering the influence of uncertain disturbances, a finite-time minimax robust control strategy is proposed by using minimax optimization theory. Simulation and hardware in loop experiments show that the minimax robust regulator based on Nash game controller can effectively improve the path tracking and roll stability performance of vehicles. Moreover, the regulator improves the robustness of the system when it is subjected to bounded uncertain lateral disturbances.

Optimal Tracking of Networked Control Systems Over the Fading Channel With Packet Loss

Optimal Tracking of Networked Control Systems Over the Fading Channel With Packet Loss

Quantized Proportional-Integral Tracking Control for Two-Stage Chemical Reactor Systems

Abstract This paper is concerned with the problem of proportional-integral tracking control of a two-stage chemical reactor system subject to time delays, disturbances, uncertainties, and input quantization. In this work, an improved equivalent-input-disturbance estimator is incorporated into the proportional-integral tracking control system to compensate for the disturbances in the addressed model. Moreover, to minimize the communication congestion in the control networks, the quantized control input signals are considered while designing the controller. Further, a robust stability condition for the addressed system is established in the form of linear matrix inequalities by employing asymmetric Lyapunov–Krasovskii functional and Jensen's integral inequalities. Moreover, in accordance with the derived conditions, the control and observer gain matrices are determined. Finally, a numerical example is provided to demonstrate the validity of the proposed control scheme.

Uncrewed aerial vehicle with onboard winch system for rapid, cost-effective, and safe oceanographic profiling in hazardous and inaccessible areas

Interactions between coastal waters and marine-terminating glaciers in the Polar Regions play a significant role in global sea level rise fueled by a rapidly warming Arctic.The risk of glacier calving, and the abundance of ice, can make it impossible for surface vessels to access the waters near glacier termini. Alternative methods using manned aircraft are expensive. As a result, oceanographic measurements are limited near glacier termini.We present an uncrewed aerial vehicle (UAV) with an on-board winch system that allows oceanographic profiling in remote, hazardous areas using a commercial conductivity, temperature, and depth (CTD) sensor payload. The UAV is optimized for easy handling and deployment and is capable of high-speed and efficient cruise flight. An autopilot system provides pilot assistance and autonomous flight capabilities. The total weight of the UAV including payload is 6.5 kg with an endurance of 24 min.Testing of the system was conducted in South Greenland during winter conditions in March 2023 with successful profiles collected near a glacier terminus (<5 m) and in small openings in ice mélange (2.2 m). The system proved capable, reliable, and efficient. Further development of the system will allow other sensors for an even more flexible measurement suite.

Comparison of Flight Parameters in SIL Simulation Using Commercial Autopilots and X-Plane Simulator for Multi-Rotor Models

Modern aviation technology development heavily relies on computer simulations. SIL (Software-In-The-Loop) simulations are essential for evaluating autopilots and control algorithms for multi-rotors, including drones and other UAVs (Unmanned Aerial Vehicle). In such simulations, it is possible to compare the flight parameters achieved by flying platforms using various commercial autopilots widely used in the UAV sector. This research aims to provide objective and comprehensive insights into the effectiveness of different autopilot systems This article examines the simulated flight test results of a drone performing the same mission using different autopilot systems. The X-Plane software was used as an environment to simulate the dynamics of the drone and its surroundings. Matlab/Simulink r2023a provided the interface between autopilot software and X-Plane models. Those methods allowed us to obtain an appropriate comparison of the autopilot systems and indicate the main differences between them. This research focused on analyzing UAV flight characteristics such as stability, trajectory tracking, response time to control changes, and the overall effectiveness of autopilots. Various flight scenarios including take-off, landing, flight at a constant altitude, dynamic manoeuvrers and, flight along a planned trajectory were also examined. In order to obtain the most accurate and realistic results, the tests were carried out in various weather conditions. The aim of this research is to provide objective data and analysis to compare the performance of commercial autopilots. This method offers several advantages, including cost-effective testing, the ability to test in diverse environmental conditions, and the evaluation of autopilot algorithms without the need for real hardware. The findings of this study may have a considerable impact on how autopilot designers and developers choose the best platforms and technologies for their projects. Future research on this topic will compare the obtained data with flight test data.

Model predictive control‐based tracking controller for hybrid‐driven underwater legged robot

AbstractTo address the tracking problem of the hybrid‐driven underwater legged robot, a control strategy is proposed that decomposes the whole tracking control system into two subsystems: body‐level and actuator‐level. The body‐level subsystem uses a central pattern generator (CPG)‐based controller to plan suitable gaits to meet the required heading and the forward velocity, crucial for accurate tracking in underwater environments. The actuators‐level subsystem employs a cooperative approach between the C‐shaped legs and thrusters of the robot. To execute the intended gait while adhering to actuation constraints and the no‐slip requirement, the torques of the legs are calculated by a model predictive control and feedback compensation (MPCF)‐based controller. Simultaneously, the calculation of the thrusters concerns four aspects to keep the legs attached to the ground and maintain the stable locomotion of the robot. Simulations on the ROS‐Gazebo platform verify the mobility of the robot and demonstrate the effectiveness of the proposed CPG‐MPCF strategy.

Development of a coding SNP panel for tracking the origin of whole-exome sequencing samples.

Whole-exome sequencing (WES) is widely used to diagnose complex genetic diseases and rare conditions. The implementation of a robust and effective quality control system for sample identification and tracking throughout the WES process is essential. We established a multiplex panel that included 22 coding single-nucleotide polymorphism (cSNP) loci. The personal identification and paternity identification abilities of the panel were evaluated, and a preliminary validation of the practical feasibility of the panel was conducted in a clinical WES case. These results indicate that the cSNP panel could be a useful tool for sample tracking in WES.

High-speed target tracking control system based on short-time rotational reflection imaging.

High-speed tracking technology has wide applications in the military and aerospace industry. However, existing approaches, such as camera arrays or Doppler radar systems, suffer from high cost and inconvenience. This paper reports a high-speed target tracking control system based on short-time rotational reflection imaging, specifically aimed at overcoming certain limitations. In the system we designed, a high-speed camera coupled with a rotating reflector is used to achieve reliable high-speed target tracking. This paper first introduces the working principle and mathematical model of the system, then analyzes the key technologies, including motor response delay time and rotational speed curve fitting, and, finally, verifies the feasibility of the system and the correctness of the theory based on a series of experiments. Experimental results demonstrated that our work is efficient and accurate in target tracking and image clarity. The developed system demonstrates significant potential for widespread use across military and aerospace sectors. Furthermore, the insights gained from our investigation into key technologies could act as a reference point for fellow researchers in related scientific areas.

Low-cost orientation system for a concentrating solar collector with static reflector and tracking receiver

A solar linear concentrator with a fixed reflector and a moving focus is under development. The main objective of the research is to develop a low – cost, roof integrated, solar collector suited for mid – range thermal applications, such as absorption cooling and industrial heat (90 – 200 ºC). This range of temperatures could be achieved with low concentration factors (9 - 15 suns). This range of radiation concentration is also well suited for the development of Combined Solar Heat and Power (CHP) systems integrating PV and thermal converters in the same unit [1]. Recently, this kind of application has gained much attention from the solar energy industry with several interesting development projects from both the industry and the academy [2]. The proposed solution is based on a static reflector with a moving linear focus (Figure 1). The basic geometric concepts were developed in the ‘70s [3] but the product development never passed beyond the experimental prototype stage. Nevertheless, a detailed ray-tracing modelling of the reflector behaviour shows clearly that it is possible to achieve a high optical efficiency into the target concentration range. Although the reflector is static, the sun path should be tracked by the receiver to be kept on the reflector focus. Therefore a precise and inexpensive tracking control system is of fundamental importance for a successful development of the collector

A generalized super-twisting algorithm-based adaptive fixed-time controller for spacecraft pose tracking

Abstract A generalized super-twisting second-order sliding mode adaptive fixed-time control law, which is used for spacecraft pose tracking in the presence of internal and external uncertainties, is proposed. Lie group SE(3) (for special Euclidean group), which is the configuration space for rigid body motion, is used for modeling the six-degrees-of-freedom dynamics of spacecraft. A fixed-time sliding mode surface is proposed and applied to design an generalized super-twisting sliding mode control law. A novel dual-layer adaption law for the controller is proposed to the ensure the gains varying rapidly with the disturbance. The adaptive second-order sliding mode controller guarantees a uniform exact convergence for the closed-loop tracking control system with less energy consumption. Numerical simulations are performed to demonstrate the excellent performances of the control law.

Cooperative Control of Path Tracking and Driving Stability for Intelligent Vehicles on Potholed Road

When a vehicle hits a pothole, the reaction force of the pothole on the tire causes an imbalance in the driving forces on both sides of the vehicle and may cause the wheels to jump, potentially resulting in path deviation and roll instability. To address these issues, a cooperative control system for path tracking and driving stability is proposed. Roll stability is achieved by the roll moment generated by reasonably distributing the driving torque among the four IWMs. The necessary roll moment is calculated by the designed robust sliding mode control (RSMC), which enhances the transient performance of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H_∞</i> by 48.9%. The path tracking is ensured by active front steering (AFS) based on model predictive control (MPC), and the front wheel angle is used as the disturbance of the stability control. Simulation and bench test results demonstrate that the developed controller can maintain vehicle stability even when the greatest path deviation is 0.03 m, whereas conventional direct yaw-moment control (DYC) and AFS controllers fail to achieve the same level of control. These findings provide a theoretical foundation and serve as a reference for cooperative control on potholed roads.

Robotic trajectory tracking control system based on fuzzy neural network

The manipulator system is a multi-input and multi-output system with highly coupled, nonlinear and other dynamic characteristics, and the system structure and parameters are unpredictable in practice. A fuzzy neural network controller is designed for this system, and the parameters of FNNC are optimized by combining particle swarm algorithm and BP algorithm. The experimental results show that the scheme has strong adaptability, stability and anti-interference performance for the control system, and effectively solves the trajectory tracking problem of the manipulator.

Autopilot System for Depth and Pitch Control in Underwater Vehicles: Navigating Near-Surface Waves and Disturbances

Autopilot System for Depth and Pitch Control in Underwater Vehicles: Navigating Near-Surface Waves and Disturbances