Adaptive Attitude Research Articles

HOLISTIC LEADERSHIP WITH SYSTEM THINKING AS A MODEL FOR SCHOOL PRINCIPALS IN THE ERA OF TECHNOLOGY

This paper aims to see how holistic leadership can solve school leadership problems in Indonesia after the Covid-19 Pandemic. Schools today face complex problems related to rapid changes that impact the needs of students and parents, learning models, the facilities needed, and the dominant role of information technology. Using a qualitative case study, this study seeks to explore the experiences of highly successful school leaders through the interview method. They consist of three Foundation leaders and four school principals from successful foundations and private schools affiliated with certain religions in Java and Sumatra. The results of this study mention schooling problems related to competence, use of data, adaptive attitude including technology, and building a focus of excellence with business logic. To overcome this problem, principals are needed to be adaptive, move flexibly, use data, and empower. Steps to improve or prepare leaders are carried out by understanding the concept of a holistic, organic leader that is flexible and networked.

Adaptive Backstepping Axial Position Tracking Control of Autonomous Undersea Vehicles with Deferred Output Constraint

In this paper, an adaptive backstepping control scheme is proposed to solve the the surge motion tracking control problem of an autonomous undersea vehicle (AUV) with system constraint. First, an initial rectification reference signal is constructed for the subsequent implementation of deferred output constraint and making the control input smaller and smoother in the early stage of system operation. Second, a barrier Lyapunov function is adopted for developing an output-constrained state feedback adaptive controller. Then, on the basis of coordinate transformation and estimating the derivative of surge displacement by a linear and nonlinear combined differentiator, we develop an output feedback adaptive backstepping control scheme for AUVs whose velocity signals are unmeasurable. We also carried out a comparative numerical simulation with traditional adaptive control to verify the feasibility of the proposed control strategy.

Anti‐unwinding adaptive attitude tracking control for spacecraft with quantized torques

SummaryThe adaptive attitude tracking control with limited communication to actuators is addressed in this paper. To avoid unwinding, a rotation matrix rather than a quaternion is used to describe the attitude, for which the stability is proved by Morse‐Lyapunov function. To meet the need of restricted communication, two different quantizers, logarithmic quantizer and hysteres quantizer, are used to quantize the control torque signal. Two robust adaptive control techniques, indirect and direct, are proposed to deal with the impacts of quantization error and external disturbances. The proposed control schemes, in conjunction with quantizers, guarantee the global boundedness of all signals in the closed‐loop system, allowing the attitude tracking error to converge toward an ultimately bounded region. Finally, numerical simulations are conducted to illustrate the performance of the proposed controllers.

On adaptive attitude tracking control of spacecraft: A reinforcement learning based gain tuning way with guaranteed performance

This paper investigates the attitude tracking control problem of a rigid spacecraft subject to inertial uncertainties, uncertain space perturbations and actuator saturation. Firstly, a static inertial-matrix free attitude controller is designed to guarantee the appointed-time convergence and tracking accuracy indicators of the spacecraft. Then, two-layer critic-action NNs are used to tune and optimize the control gains adaptively to improve the robustness and tracking performance of the devised static attitude controller via exploring the reinforcement learning (RL) technique. Compared with the existing attitude control methods, the prominent advantage of our work is that the devised controller is inertial-matrix free with a simple RL-based adaptive parameter tuning scheme, which reduces the conservativeness of the traditional attitude controllers with fixed control gains and is also easy to be achieved. Finally, two groups of illustrative examples are organized to validate the effectiveness of the proposed attitude control method.

Reinforcement Learning-Based Adaptive Position Control Scheme for Uncertain Robotic Manipulators with Constrained Angular Position and Angular Velocity

Aiming at robotic manipulators subject to system uncertainty and external disturbance, this paper presents a novel adaptive control scheme that uses the time delay estimation (TED) technique and reinforcement learning (RL) technique to achieve a good tracking performance for each joint of a manipulator. Compared to conventional controllers, the proposed control scheme can not only handle the system parametric uncertainty and external disturbance but also guarantee both the angular positions and angular velocities of each joint without exceeding their preset constraints. Moreover, it has been proved by using Lyapunov theory that the tracking errors are uniformly ultimately bounded (UUB) with a small bound related to the parameters of the controller. Additionally, an innovative RL-based auxiliary term in the proposed controller further minimizes the steady state tracking errors, and thereby the tracking accuracy is not compromised by the lack of asymptotic convergence of tracking errors. Finally, the simulation results validate the effectiveness of the proposed control scheme.

Research on Adaptive Prescribed Performance Control Method Based on Online Aerodynamics Identification

Wide-speed-range vehicles are characterized by high flight altitude and high speed, with significant changes in the flight environment. Due to the strong uncertainty of its aerodynamic characteristics, higher requirements are imposed on attitude control. In this paper, an adaptive prescribed performance control method based on online aerodynamic identification is proposed, which consists of two parts: an online aerodynamic parameter identification method and an adaptive attitude control method based on the pre-defined parameters of the control system. The aerodynamic parameter identification is divided into offline design and online design. In the offline design, neural networks are used to fit nonlinear aerodynamic characteristics. In the online design, a nonlinear recursive identification method is used to correct the errors of the offline fitted model. The adaptive attitude control is based on the conventional control method and updates the control gain in real time according to the desired system parameters to enhance the robustness of the controller. Finally, the effectiveness of the offline neural network and online discrimination correction is verified by mathematical simulations, and the effectiveness and robustness of the adaptive control proposed in this paper are verified by comparative simulation.

High-precision geomagnetic directional technology based on sensor error correction and adaptive hybrid filter

Low-cost magnetic and inertial sensors based on micro-electro-mechanical systems (MEMS) are extensively used for navigation systems. However, measurements of the magnetometer are very susceptible to different factors, resulting in low accuracy when used for attitude estimation. To solve these problems, a novel method is proposed for geomagnetic directional technology based on sensor error correction and adaptive hybrid filter. In the dynamic directional accuracy test with the turntable rotating speed of 40°/s, the maximum error and RMSE of the yaw angle is 1.00° and 0.63°, respectively. When magnetic interference exists, this algorithm dynamically adjusts the weighting of the magnetometer data by information entropy and RMSE. The experimental results show that the adaptive attitude updating algorithm can reduce the directional error by 90.61% in the environment of magnetic interference. It can meet the overall demand for high-precision attitude calculation of different carriers.

Constrained Nonsingular Terminal Sliding Mode Attitude Control for Spacecraft: A Funnel Control Approach

This paper presents an adaptive constrained attitude control for uncertain spacecraft. Inspired by the concept of nonsingular terminal sliding mode control and funnel control for nonlinear systems, a novel adaptive attitude control is introduced which contains a time-varying gain to handle the constraints imposed on the spacecraft attitude. Indeed, when the attitude trajectory approaches the boundary of the constraint set, the control effort as well as the time-varying gain will increase in order to preclude the trajectory from intersecting the boundary. Then, it is analytically proved that the system trajectories converge to an arbitrary small region around the origin within a fixed time where the smallest upper bound of the convergence time is determined as an independent parameter in the controller. Further, the proposed control scheme is nonsingular without having to use any piecewise continuous function which simplifies stability analysis. These properties distinguish the proposed control scheme from the existing finite/fixed-time attitude controls. Finally, several simulation results confirm the robustness and performance of the proposed control framework.

Adaptive backstepping position tracking control of quadrotor unmanned aerial vehicle system

<abstract><p>In this work, an adaptive backstepping position tracking control using neural network (NN) approximation mechanism is proposed with respect to the translational system of quadrotor unmanned aerial vehicle (QUAV). Concerning the translational system of QUAV, on the one hand, it does not satisfy the matching condition and is an under-actuation dynamic system; on the other hand, it is with strong nonlinearity containing some uncertainty. To achieve the control objective, an intermediary control is introduced to handle the under-actuation problem, then the backstepping technique is combined with NN approximation strategy, which is employed to compensate the uncertainty of the system. Compared with traditional adaptive methods, the proposed adaptive NN position control of QUAV can alleviate the computation burden effectively, because it only trains a scalar adaptive parameter instead of the adaptive parameter vector or matrix. Finally, according to Lyapunov stability proof and computer simulation, it is proved that the control tasks can be accomplished.</p></abstract>

Noise-Separated Adaptive Feature Distillation for Robust Speech Recognition

This letter makes an improvement on feature-based knowledge distillation for robust speech recognition. The use of distillation techniques in speech recognition has been demonstrated to improve the robustness of the system. In this letter, we propose a noise-separated adaptive feature distillation method, including an adaptive distillation position selection strategy and a noise separation mechanism, assuming that there is a common network structure between the student and teacher. The proposed method has two improvements. First, distillation positions can be adaptively selected in each iteration by comparing loss values computed on intermediate representations of the student and the teacher, increasing the flexibility of knowledge transfer during distillation. Second, a noise separation module is proposed to constrain noise information elimination by explicitly separating the speech information and the noise information in noisy speech, which reduces the interference of noise information during distillation. Therefore, a better recognition performance is demonstrated with the proposed method compared to the standardized feature-based knowledge distillation method.

Center-of-Rotation-Augmented Kalman Filter for Adaptive Attitude Reference System

Center-of-Rotation-Augmented Kalman Filter for Adaptive Attitude Reference System

Adaptive attitude determination of bionic polarization integrated navigation system based on reinforcement learning strategy

<p style='text-indent:20px;'>The bionic polarization integrated navigation system includes three-axis gyroscopes, three-axis accelerometers, three-axis magnetometers, and polarization sensors, which provide pitch, roll, and yaw. When the magnetometers are interfered or the polarization sensors are obscured, the accuracy of attitude will be decreased due to abnormal measurement. To improve the accuracy of attitude of the integrated navigation system under these complex environments, an adaptive complementary filter based on DQN (Deep Q-learning Network) is proposed. The complementary filter is first designed to fuse the measurements from the gyroscopes, accelerometers, magnetometers, and polarization sensors. Then, a reward function of the bionic polarization integrated navigation system is defined as the function of the absolute value of the attitude angle error. The action-value function is introduced by a fully-connected network obtained by historical sensor data training. The strategy can be calculated by the deep Q-learning network and the action that optimal action-value function is obtained. Based on the optimized action, three types of integration are switched automatically to adapt to the different environments. Three cases of simulations are conducted to validate the effectiveness of the proposed algorithm. The results show that the adaptive attitude determination of bionic polarization integrated navigation system based on DQN can improve the accuracy of the attitude estimation.</p>

How Mediterranean winegrowers perceive climate change

<abstract> <p>Farmers are the most affected by the negative impacts of climate change and, at the same time, are called upon to adapt to climate change. Despite this, the degree of perception and adaptive attitude of farmers to climate change is still quite limited, especially in smallholder family farms in the Mediterranean areas. This study explores the level of perception of climate change by PDO (Protected Designation of Origin) winegrowers in a region of southern Italy (Sicily) and the adaptation actions able to cope with climate change, using a nonparametric approach. The analysis is based on data collected through self-administered questionnaires submitted to 380 PDO winegrowers. For variables comparison the Mann Whitney and the Kruskall Wallis test were applied according to the number of compared samples (two or more independent samples, respectively). Results show how winegrowers' perceptions of climate change tends to vary according to age and education of the respondents and to altitude and size of vineyards. This study highlights how information and dissemination of knowledge among winegrowers play a strategic role in the perception of climate change, especially in rural and remote Mediterranean areas.</p> </abstract>

Nussbaum Gain-Based Cone Angle Constrained Fault-Tolerant Attitude Control with Time-Varying Inertia

This paper proposes a robust adaptive attitude control law for handling a class of actuator faults in the presence of various challenges, such as time-varying inertia, attitude constraints, and input saturation. The proposed approach uses cone angles to represent orientation errors, which are constrained within a performance function to ensure desired transient and steady-state behaviour during reference tracking. Input saturation is approximated using a smooth hyperbolic tangent function. The Nussbaum gain technique is used to handle the unknown control coefficients, which guarantees uniformly ultimately bounded stability in the presence of uncertainties and disturbances. The paper also proposes a norm-based disturbance approximation to estimate the total uncertainty during the Lyapunov analysis. Numerical simulations demonstrate the effectiveness of the proposed control law.

Data-Driven Model-Free Adaptive Positioning and Anti-Swing Control for Bridge Cranes

In the positioning and anti-swing control of bridge crane, a model-free adaptive control (MFAC) based on data-driven is proposed in order to eliminate the dependence of controller design on the model and the influence of unmodeled dynamics and uncertain disturbances on the controller performance. Only using the input and output data of the bridge crane system, the virtual full format dynamic linearized data model of the bridge crane nonlinear system is obtained through the data-driven modeling method. On the basis of this virtual data model, a model-free adaptive control law and a pseudo-Jacobian matrix estimation algorithm are designed according to the optimization theory under the constraint conditions. The stability of the closed loop system and the convergence of the system error are analyzed and proved by Lipschitz condition and inequality theory. The effectiveness of the control strategy for positioning and anti-swing control of bridge cranes is verified on simulated simulation and experimental platform of bridge crane. The results show that the proposed method is feasible and has good anti-disturbance performance and robustness.

Conv-Former: A Novel Network Combining Convolution and Self-Attention for Image Quality Assessment.

To address the challenge of no-reference image quality assessment (NR-IQA) for authentically and synthetically distorted images, we propose a novel network called the Combining Convolution and Self-Attention for Image Quality Assessment network (Conv-Former). Our model uses a multi-stage transformer architecture similar to that of ResNet-50 to represent appropriate perceptual mechanisms in image quality assessment (IQA) to build an accurate IQA model. We employ adaptive learnable position embedding to handle images with arbitrary resolution. We propose a new transformer block (TB) by taking advantage of transformers to capture long-range dependencies, and of local information perception (LIP) to model local features for enhanced representation learning. The module increases the model's understanding of the image content. Dual path pooling (DPP) is used to keep more contextual image quality information in feature downsampling. Experimental results verify that Conv-Former not only outperforms the state-of-the-art methods on authentic image databases, but also achieves competing performances on synthetic image databases which demonstrate the strong fitting performance and generalization capability of our proposed model.

Embodied airflow sensing for improved in-gust flight of flapping wing MAVs.

Flapping wing micro aerial vehicles (FWMAVs) are known for their flight agility and maneuverability. These bio-inspired and lightweight flying robots still present limitations in their ability to fly in direct wind and gusts, as their stability is severely compromised in contrast with their biological counterparts. To this end, this work aims at making in-gust flight of flapping wing drones possible using an embodied airflow sensing approach combined with an adaptive control framework at the velocity and position control loops. At first, an extensive experimental campaign is conducted on a real FWMAV to generate a reliable and accurate model of the in-gust flight dynamics, which informs the design of the adaptive position and velocity controllers. With an extended experimental validation, this embodied airflow-sensing approach integrated with the adaptive controller reduces the root-mean-square errors along the wind direction by 25.15% when the drone is subject to frontal wind gusts of alternating speeds up to 2.4m/s, compared to the case with a standard cascaded PID controller. The proposed sensing and control framework improve flight performance reliably and serve as the basis of future progress in the field of in-gust flight of lightweight FWMAVs.

RLS-based BeiDou Satellite High Precision Positioning for Power Device

The BeiDou-3 navigation satellite system (BDS-3) has been officially put into use, which will provide wider, more accurate, and smarter industrial applications. BDS-3 positioning has advantages of stability, economical reliability and most importantly, high positioning accuracy, which is widely used in power systems, covering power dispatching, energy management, data acquisition, and system monitoring. However, in the positioning process of BDS-3, positioning accuracy will be affected by spatial and temporal interference factors. This paper addresses the problem of poor positioning accuracy under the interference influence of BDS-3, and proposes a Recursive Least Squares (RLS)-based adaptive high-precision positioning scheme to effectively reduce the influence of interference factors.

Robust Adaptive General Formation Control of a Class of Networked Quadrotor Aircraft

This article is concerned with the consensus formation control problem of a class of networked quadrotor aircraft partially bounded and state-dependent perturbations. A general distributed consensus error model is first developed to formulate the formation behavior of the networked quadrotor aircraft. Then, by using adaptive techniques, virtual position control strategies are proposed to eliminate the impacts of perturbations, so that the following quadrotor aircraft can boundedly track the desired position trajectory with a satisfying pattern. Furthermore, based on the designed virtual position control strategies, the attitude reference angles are constructed and adaptive attitude control strategies are further designed to guarantee that the attitude angles track the reference angles asymptotically. In terms of the asymptotic tracking results of attitude control systems, the bounded consensus formation results are obtained based on the Lyapunov stability theorem. Numerical simulations are carried out to verify the efficiency of the designed position formation as well as attitude tracking control strategies of the networked quadrotor aircraft.

Rethinking Design and Evaluation of 3D Point Cloud Segmentation Models

Currently, the use of 3D point clouds is rapidly increasing in many engineering fields, such as geoscience and manufacturing. Various studies have developed intelligent segmentation models providing accurate results, while only a few of them provide additional insights into the efficiency and robustness of their proposed models. The process of segmentation in the image domain has been studied to a great extent and the research findings are tremendous. However, the segmentation analysis with point clouds is considered particularly challenging due to their unordered and irregular nature. Additionally, solving downstream tasks with 3D point clouds is computationally inefficient, as point clouds normally consist of thousands or millions of points sparsely distributed in 3D space. Thus, there is a significant need for rigorous evaluation of the design characteristics of segmentation models, to be effective and practical. Consequently, in this paper, an in-depth analysis of five fundamental and representative deep learning models for 3D point cloud segmentation is presented. Specifically, we investigate multiple experimental dimensions, such as accuracy, efficiency, and robustness in part segmentation (ShapeNet) and scene segmentation (S3DIS), to assess the effective utilization of the models. Moreover, we create a correspondence between their design properties and experimental properties. For example, we show that convolution-based models that incorporate adaptive weight or position pooling local aggregation operations achieve superior accuracy and robustness to point-wise MLPs, while the latter ones show higher efficiency in time and memory allocation. Our findings pave the way for an effective 3D point cloud segmentation model selection and enlighten the research on point clouds and deep learning.