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Traditional input–output feedback linearization requires full knowledge of system dynamics and assumes no disturbance at the input channel and no system’s uncertainties. In this paper, a model-free active input–output feedback linearization technique based on an improved active disturbance rejection control paradigm is proposed to design feedback linearization control law for a generalized nonlinear system with a known relative degree. The linearization control law is composed of a scaled generalized disturbance estimated by an improved nonlinear extended state observer with saturation-like behavior and the nominal control signal produced by an improved nonlinear state error feedback. The proposed active input–output feedback linearization cancels in real-time fashion the generalized disturbances which represent all the unwanted dynamics, exogenous disturbances, and system uncertainties and transforms the system into a chain of integrators up to the relative degree of the system, which is the only information required about the nonlinear system. Stability analysis has been conducted based on the Lyapunov functions and revealed the convergence of the improved nonlinear extended state observer and the asymptotic stability of the closed-loop system. Verification of the outcomes has been achieved by applying the proposed active input–output feedback linearization technique on the single-link flexible joint manipulator. The simulations results validated the effectiveness of the proposed active input–output feedback linearization tool based on improved active disturbance rejection control as compared to the conventional active disturbance rejection control–based active input–output feedback linearization and the traditional input–output feedback linearization techniques.

In this paper, an adaptive active disturbance rejection control is newly designed for precise angular steering position tracking of the uncertain and nonlinear SBW system with time delay communications. The proposed adaptive active disturbance rejection control comprises the following two elements: (1) An adaptive extended state observer and (2) an adaptive state error feedback controller. The adaptive extended state observer with adaptive gains is employed for estimating the unmeasured velocity, acceleration, and compound disturbance which consists of system parameter uncertainties, nonlinearities, exterior disturbances, and time delay in which the observer gains are dynamically adjusted based on the estimation error to enhance estimation performances. Based on the accurate estimations of the adaptive extended state observer, the proposed adaptive full state error feedback controller is equipped with variable gains driven by the tracking error to develop control precision. The integration of the advantages of the adaptive extended state observer and the adaptive full state error feedback controller can improve the dynamic transient and static steady-state effectiveness, respectively. To assess the superior performance of the proposed adaptive active disturbance rejection control, a comparative analysis is conducted between the proposed control scheme and the classical active disturbance rejection control in two different cases. It is worth noting that the active disturbance rejection control serves as a benchmark for evaluating the performance of the proposed control approach. The results from the comparison studies executing two simulated cases validate the superiority of the suggested control, in which estimation, tracking response rate, and steering angle precision are greatly improved by the scheme proposed in this article.

The paper is devoted to the theoretical problem of designing a robust asymptotic tracking control system for a rotational motion of a 2DOF underactuated linear mechanical system with parametric uncertainties. The mathematical formulation of the problem is motivated by the attitude control problem of an earth observation satellite with a solar panel. It is assumed that all the parameters of the plant model are uncertain and the plant single input is additively disturbed by an unknown constant torque. By employing the general regulator theory in the state space setup combined with the concept of the structured singular value, we develop a robustly stabilizing and robustly asymptotically tracking error feedback controller. The rotation of the main rigid body of the mechanical system is to asymptotically track a harmonically changing reference signal. The obtained theoretical results are successfully tested on two numerical examples and computations are performed in Matlab.

Shipborne high-frequency surface wave radar (HFSWR) has a wide range of applications and plays an important role in moving target detection and tracking. However, the complexity of the sea detection environment causes the target signals received by shipborne HFSWR to be seriously disturbed by sea clutter. Sea clutter increases the difficulty of azimuth estimation, resulting in a challenging problem for shipborne HFSWR. To solve this problem, a novel azimuth correction method based on adaptive boosting error feedback dynamic weighted particle swarm optimization extreme learning machine (APO-ELM) is proposed to improve the azimuth estimation accuracy of shipborne HFSWR. First, the sea clutter is modeled and simulated. Then, we study its characteristics and analyze the influence of its characteristics on the first-order clutter spectrum and target detection accuracy, respectively. In addition, the proposed improved particle swarm optimization (PSO) and adaptive neuron clipping algorithm are used to optimize the input parameters of the ELM network. Then, the network performs error feedback based on the optimized parameter performance and updates the feature matrix, which can give a minimum clutter-error estimation. After that, it iteratively trains multiple weak learners using the adaptive boosting (AdaBoost) algorithm to form a strong learner and make strong predictions. Finally, after error compensation, the best azimuth estimation results are obtained. The sample sets used for the APO-ELM network are obtained from field shipborne HFSWR data. The network training and testing features include the wind direction, sea current, wind speed, platform speed, and signal-to-clutter ratio (SCR). The experimental results show that this method has a lower root-mean-square error than the back-propagation neural network and support vector regression (SVR) azimuth correction methods, which verifies the effectiveness of the proposed method.

The channel reliability function is a crucial tool for characterizing the dependable transmission of messages across communication channels. In many cases, the only upper and lower bounds of this function are known. We investigate the computability of the reliability function and its associated functions, demonstrating that the reliability function is not Turing computable. This also holds true for functions related to the sphere packing bound and the expurgation bound. Additionally, we examine the R∞ function and zero-error feedback capacity, as they are vital in the context of the reliability function. Both the R∞ function and the zero-error feedback capacity are not Banach–Mazur computable.

High-quality 3D model abstraction is needed in many graphics or 3D vision tasks to improve the rendering efficiency, increase transmission speed or reduce space occupation. Traditional simplification algorithms for 3D models rely heavily on the mesh topology and ignore objects’ overall structure during optimization. Learning-based methods are then proposed to form an end-to-end regression system for abstraction. However, existing learning-based methods have difficulty representing shapes with hollow or concave structures. We propose a self-supervised learning-based abstraction method for 3D meshes to solve this problem. Our system predicts the positive and negative primitives, where positive primitives are to match the inside part of the shape, and negative primitives represent the hollow region of the shape. More specifically, the bool difference between positive primitives and the object is fed to a network using Iteration error feedback mechanism to predict the negative primitives, which crop the positive primitives to create hollow or concave structures. In addition, we design a new separation loss to prevent a negative primitive from overlapping the object too much. We evaluate the proposed method on the ShapeNetCore dataset by Chamfer Distance and Intersection over Union. The results show that our positive–negative abstraction schema outperforms the baselines.

Low-light image enhancement is a challenging task because brightness, contrast, noise and other factors must be considered simultaneously. However, most of the existing studies focus on improving illumination, and it is difficult to obtain natural images when the images of complex scenes are enhanced. To address this issue, we propose a neural network—a multi-scale error feedback network (MSEFN)—to enhance low-light images. The proposed network consists of an error feedback encoder module (EFEM), an error feedback decoder module (EFDM) and a feature integration module (FIM). As the main component of EFEM and EFDM, the error feedback feature extraction module can effectively retain spatial information by using the shuffle attention fusion block (SAFB) to fuse the acquired multi-scale features and nonadjacent features. FIM has the ability to capture contextual information that can compensate for the lack of global features in the network. Furthermore, the local uneven illumination (LUI) dataset and polynomial loss function constructed in this paper make our network more stable. Extensive experiments demonstrate that the proposed network outperforms state-of-the-art methods both qualitatively and quantitatively. The LUI dataset is publicly available at: https://github.com/Qyizos/LUI-dataset .

This study investigated the application of WriteToLearn on Chinese undergraduate English majors' essays in terms of its scoring ability and the accuracy of its error feedback. Participants were 163 second-year English majors from a university located in Sichuan province who wrote 326 essays from two writing prompts. Each paper was marked by four human raters as well as WriteToLearn. Many-facet Rasch measurement (MFRM) was conducted to calibrate WriteToLearn's rating performance in scoring the whole set of essays against those of four trained human raters. In addition, the accuracy of WriteToLearn's feedback on 60 randomly selected essays was compared with the feedback provided by human raters. The two main findings related to scoring were that: (1) WriteToLearn was more consistent but highly stringent when compared to the four trained human raters in scoring essays; and (2) WriteToLearn failed to score seven essays. In terms of error feedback, WriteToLearn had an overall precision and recall of 49% and 18.7% respectively. These figures did not meet the minimum threshold of 90% precision (set by Burstein, Chodorow, and Leacock, 2003) for it to be considered a reliable error detecting tool. Furthermore, it had difficulty in identifying errors made by Chinese undergraduate English majors in the use of articles, prepositions, word choice and expression.

In order to meet the precision requirements and tracking performance of the continuous rotary motor electro-hydraulic servo system under unknown strong non-linear and uncertain strong disturbance factors, such as dynamic uncertainty and parameter perturbation, an improved active disturbance rejection control (ADRC) strategy was proposed. The state space model of the fifth order closed-loop system was established based on the principle of valve-controlled hydraulic motor. Then the three parts of ADRC were improved by parameter perturbation and external disturbance; the fast tracking differentiator was introduced into linear and non-linear combinations; the nonlinear state error feedback was proposed using synovial control; the extended state observer was determined by nonlinear compensation. In addition, the grey wolf algorithm was used to set the parameters of the three parts. The simulation and experimental results show that the improved ADRC can realize the system frequency 12 Hz when the tracking accuracy and response speed meet the requirements of double ten indexes, which lay foundation for the motor application.

The Enhanced Active Disturbance Rejection Controller (EADRC) is proposed to perform flexion and extension motions for the shoulder and elbow joints repeatedly and precisely in the sagittal plane using a combination of a nonlinear state error feedback (NLSEF), an extended state observer (ESO), and a finite time stable tracking differentiator (FTSTD). Making use of the Euler–Lagrangian method, the mathematical model of the exoskeleton is derived. A sinusoidal trajectory is supplied as input to a two-link multi-input and multi-output (MIMO) upper limb robotic rehabilitation exoskeleton to perform passive rehabilitation. EADRC tracks this sinusoidal trajectory by estimating the states of a system based on the input–output data of the system and actively removes disturbances. The model parameters are varied by  ± 20 % of actual value with constant external disturbance to demonstrate the proposed controller’s robustness against uncertainties and disturbances. Stability of the controller is discussed. The proposed strategy was compared with state-of-the-art ESO-based techniques in the simulation. Various performance indicators were used to assess the controller’s efficacy which acknowledge that the proposed strategy enhances tracking performance and reliability.