Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel Synchronization Criteria of Chaotic Lur'e Systems With Time-varying Delayed Feedback PD Control

This paper is concerned with the problem of asymptotical synchronization of chaotic Lur’e systems controlled via PD controller with time-varying delay. Firstly, a new Lyapunov-Krasovskii functional(LKF) with more information of time-varying delay is constructed. Then, by applying the Wirtinger-based integral inequality and the extended reciprocally convex combination lemma(RCCL), a new synchronization criterion for time-varying delay is obtained, and a less conservatism corollary for the constant delay is established by weakening some terms of LKF. Finally, a numerical example is given to show the better performance of the proposed criteria.
Design of the steering structure for a single wheeled vehicle

Nowadays, there is a need to reduce emissions in the world and use electricity to drive vehicles. There is an increasing interest in small passenger vehicles suitable for crowded cities. In this paper we try to design a universal control structure for a single-wheeled vehicle with a tilt sensor. This structure can also be applied to a vehicle with parallel wheel arrangement. The paper describes a current and position loop suitable for a single wheeled vehicle. In the paper is designed a simple complementary filter for data processing from the accelerometer and gyroscope and a block diagram of the program for use in any microprocessor.
An Optimization Method for Encrypting CAN Messages

The electric vehicles nowadays are managed by networked controllers. Because of the advantages of fast, reliable, simple wiring, and light weight, CAN bus has become one of the most widely used field buses in the world. Most of the networks were designed with little concern about security which has recently motivated researchers to demonstrate various kinds of attacks against the system. This paper proposes an optimization method for encrypting CAN Messages. In addition, a single frame CAN message can only transmit 64-bit valid data, it is often necessary to complete the communication task by sending a multi-frame message with the same ID. The AES algorithm has become the first choice for encrypting multi-frame CAN messages with its extremely high reliability and fast encryption and decryption speed. In this paper, a method of encrypting multi-frame CAN messages based on the AES algorithm is proposed, and on the basis of which a hash value is optimized to ensure the security of data transmitted on the CAN bus.
Control Design for Current Loop of PMSM Using A Modified Model-Compensation ADRC Controller

Active disturbance rejection control (ADRC) is now widely applied in motor drives due to its advantages like independence of system models and better disturbance rejection ability. For the current loop of PMSM, the uncertainties like model coupling and parameter variation can be estimated and compensated by ADRC. Considering that the total disturbance of current loop can be partially compensated for by available plant characteristics, the idea of model compensation can be applied to ADRC controller. However, the filter link in current feedback loop is usually ignored in conventional model compensation approach. Therefore, a modified model-compensation ADRC current control strategy considering the filter link effect is proposed in this paper, with simulation results show that better current tracking performance is achieved by the proposed control strategy.
Closed-form integration of IMU error state covariance for optimization-based Visual-Inertial State Estimator

In this paper, we propose a closed-form IMU error state covariance integration method for optimization-based visual- inertial state estimator. We derive a closed-form solutions of the IMU error state covariance propagation in pre-integration process, yielding improved accuracy of IMU residual information matrix. Our visual-inertial state estimator is based on a tightly-coupled, sliding-window optimization framework, which jointly estimate the IMU states and landmarks and performing marginalization to limit the computational cost. Finally, the system are validated in park environment dataset, the result shows our proposed method is effective.
Reduction of Multiplications in Convolutional Neural Networks

Convolution neural network (CNN) widely used for the application of computer vision, such applications would be beneficial for CNN if their workload could be reduced. In this paper, we evaluate CNN's arithmetic properties (Matrix Multiplications and Additions) and proposed a Strassen algorithm with the help of Pan’s result to minimize their workload of computation. That algorithm needs the least workspace and it has great computational accuracy. Matrix multiplication (MM) is the most fundamental computational operation, and the performance of Matrix multiplication (MM) depends on different factors. Moreover, that is a very flexible and robust structure. Here we are focusing on the number of element-wise multiplications and addition, the effects of this research on modern computations.
Robust I&I Adaptive DSC with Disturbance Observer for Maglev System with Output Constraint*

This paper proposes a robust immersion and invariance (I&I) adaptive dynamic surface controller (DSC) with disturbance observer (DO) for the tracking control of uncertain output-constrained maglev system subjected to mismatched disturbance. The uncertain maglev system with output constraint is firstly transformed into an equivalent unconstrained one via a constructive nonlinear mapping. Dynamic surface control (DSC) combined with I&I adaption law and extended disturbance observer is designed systematically for the transformed system. The proposed control law overcomes the problem of "explosion of complexity" due to the DSC baseline controller. The robust I&I adaptation law in the control law is developed to recover the unknown parameter of the system, which shapes the dynamics of parameter estimation error and renders the estimation error exponentially converging to a bounded residual set. And the extended disturbance observer is used to handle the time-varying mismatched disturbances in the maglev system. The stability analysis of the overall system shows that all signals of the closed-loop system are bounded and output constraint of the system is not violated as well. The numerical simulation results verify the effectiveness of the proposed controller at last.
A Day and Night Operational Quasi-Z Source Multilevel Grid-Tied PV Power System to Achieve Active and Reactive Power Control

A day and night operational single-phase energy stored quasi-Z-source-cascaded H-bridge (ES-qZS-CHB) inverter photovoltaic (PV) power system to achieve the active and reactive power control is proposed in this article. The ES-qZS-CHB inverter PV power system usually employs the unity power factor control method to ensure the output current tracking the desired reference in phase with the grid voltage, combining with distributed maximum power point tracking (MPPT) to determine the values of shoot-through duty ratios Dn. These cannot operate at night because there is no PV power input. Meanwhile, as multiple combinations of Dn and modulation ratio Mn could achieve the same voltage gain at night operation, but which is optimal has not been addressed. This article proposes a solution to address these issues. First, a comprehensive control scheme, which not only has the attractiveness of day and night operation but also shows the advantage of active and reactive power control simultaneously, is proposed; then, an optimization control method is proposed for night operational ES-qZS-CHB inverter to achieve optimal combination of Dn and Mn. Simulation and experimental results validate the day and night operational ES-qZS-CHB inverter and the proposed optimal control technique.
Research on Vibration Isolation Control of Six wheel-legged Robot Based on Impedance Control

The electric parallel six wheel-legged robot (EPSWLR) is characterized by heavy load and fast moving speed. But the body will shake and vibrate while passing through rough roads, which affects its stability. In order to solve this problem, the single-leg structure based on Stewart structure was equivalent to the impedance system composed of mass block, spring and damping in this paper. Due to changes in environmental forces and environmental impedance parameters caused by environmental changes, the traditional impedance control (IC) still causes force error at the foot end of robot and thus results in poor vibration isolation effect. Therefore, a new adaptive algorithm was proposed in this paper and added to IC algorithm to reduce the force error caused by environmental changes and thus improve the vibration isolation effect. In this paper, the characteristic equation of adaptive impedance control (AIC) algorithm was first obtained and the condition for system stability was calculated based on Routh-Hurwitz stability criterion. Finally, the stability of this system was verified. The simulation experiment combining MATLAB and Adams was designed and performed with EPSWLR. The effectiveness of AIC was proved by monitoring the changes in the force of single leg and the attitude of robot body.
Improved Adaptive Critic for Neural Optimal Control of Constrained Nonlinear Discrete-Time Systems

There always exist approximation errors during neural network control processes, which may cause the estimation value to exceed the control constraint when the optimal control input reaches to a neighborhood of the constraint. In this paper, through a new neural network training approach, the near-optimal control problem for a class of nonlinear discrete-time systems with control constraints is solved. Based on the nonquadratic performance index and the dual heuristic dynamic programming scheme, the iterative algorithm is developed with convergence guarantee and is also implemented by using three neural networks. At last, two examples are given to demonstrate the effectiveness of the proposed optimal control scheme.
Simulation and control co-design methodology for soft robotics

Soft robotics is an emergent research field which has variant promising applications. However, the design and control of soft robots nowadays still suffer from the inefficient trial-and-error process. To eliminate the drawback, this paper proposes an innovative simulation/control co-design methodology for designing soft robotics. The main idea is to use Finite Element Method to model any type of soft robots by utilizing SOFA as a FEM simulator and designing controllers in Maltab/Scilab. The interaction between SOFA and Maltab/Scilab has been presented in order to achieve the co-design task. The proposed methodology has been applied to simulate/design a concrete trunk-like soft manipulator, with the associated controller realized in Maltab/Scilab.
Hardware-in-the-loop Simulation Platform for Unmanned Aerial Vehicle Swarm System: Architecture and Application

A hardware-in-the-loop (HITL) simulation platform is developed for fixed-wing unmanned aerial vehicle (UAV) swarm system. Flight dynamics simulation of multiple UAVs is enabled to run on a single machine simultaneously with full physics and 3D visualized scenes. Flight control system is identical to that on actual UAV, and coordinate control algorithms are implanted on micro computers, separating from underlying attitude control loop on autopilots. Classical UAV swarm control application scenarios including formation and formation alternation are tested. Besides, formation flight experiments combining a physical UAV with simulated UAVs are conducted to evaluate communication network.
A Comparison Study of Three Active Disturbance Rejection Methods

This paper investigates the performance of three active disturbance-rejection methods: disturbance-observer-based (DOB) control, active disturbance rejection control (ADRC), and equivalent-input-disturbance-based (EID) control. Compared to the classic feedback control, these methods provide an additional degree of freedom to reject disturbances effectively. First, the disturbance-rejection mechanism of each of the methods is analyzed. For a proper design, the transfer function from disturbances to the output of a system approximates zero using these methods. This implies the effect of disturbances is blocked to the output. Then, the three methods are applied to the position control of a ball-and-beam system for comparison. The simulation results show that all these methods are insensitive to disturbances and robust to system uncertainties. And the error of the system output is smaller for the EID method than for the DOB and ADRC methods.
Cooperative Network Model for Joint Mobile Sink Scheduling and Dynamic Buffer Management Using Q-Learning

Development of energy-efficient wireless sensor networks is crucial in the deployment of IoT and IIoT for modern day applications like smart home, smart vehicles, and smart industries. Several methods like network clustering, mobile sink deployment and dynamic sensing rate have been used in improving the energy-efficiency of wireless sensor networks in IoT framework. However, these methods have been developed independently which can lead to certain network issues like reduced lifetime, network breakdown among others. In this work, an energy-efficient method that optimizes mobile sink scheduling while concurrently providing dynamic buffer management is proposed. A cooperative network model that incorporates node clustering and mobile sink deployment in variable node sensing rate scenario is first developed. However, in such cooperative network models, mobile sink scheduling and buffer overflow management which causes information loss become challenging. This is primarily due to limited buffer size, variable sensing rate of the nodes, and the unavailability of mobile sink at all times near a cluster. Therefore, a reinforcement Q-learning framework is developed for scheduling the mobile sink while minimizing the information loss caused by buffer overflow in each cluster of a clustered WSN. More specifically, the network behaviour is learnt in the context of buffer overflow using Q-learning approach. The proposed method computes the adaptive halt-times for the mobile sink based on information loss and buffer overflow in each cluster. Performance of the proposed joint mobile sink scheduling and dynamic buffer management method is evaluated on a medium scale WSN. A clustered wireless sensor network with a total of 600 sensor nodes is considered for performance evaluation. The proposed method is shown to learn the variable node sensing rate in a reasonable amount of time using convergence analysis. Numeric evaluations indicate that the proposed method minimizes the information loss in a medium scale wireless sensor network while improving the network lifetime simultaneously. The proposed cooperative network model also outperforms in terms of energy-efficiency when compared to conventional WSN. The results are motivating enough for the use of cooperative network model in practical WSNs for IoT applications.
Comparison of Field Oriented and Six-step Control for High-speed PMSM Drive

This paper deals with comparison of Field Oriented Control and Six-step control for high-speed PMSM drive. Common situation for these high-speed drives, but also large traction drives are high ratios of fundamental-to-sampling frequencies. In such case, the torque produced by these methods is greatly deteriorated and overall control quality is affected. Comparison of mentioned control algorithms in high-speed region is done based on ripple in electromagnetic torque.
Kinematics Analysis of 3UPU_UP Coupling Parallel Platform in the Marine Environment

A study of the kinematic characteristics of a 3UPU_UP coupling parallel platform that has three-degree-of-freedom(3-DOF). In this article, a novel 3UPU_UP parallel platform is presented, which is based on the 5-DOF 3UPU parallel platform by adding a UP followed limb. The DOFs and motion constraints of the platform are analyzed by the screw theory, and the coupling constraint equation of the platform is obtained through coordinate transformation. The inverse kinematics equation of the platform is derived by the closed-loop method, and the forward kinematics equation of the platform is solved by Netwon-Rapson iteration. The trajectory of the platform in space is obtained and the correctness of the coupling constraint equation of the platform in other DOFs is verified. Considering that the motion of a ship on the sea is multi-DOF and complicated, the motion of the cargo on the ship can be compensated by using a coupling characteristic of the platform to improve the safety of marine operations.
A CRNN System for Sound Event Detection Based on Gastrointestinal Sound Dataset Collected by Wearable Auscultation Devices

In this article, we set up a novel audio dataset named Gastrointestinal (GI) Sound Set which includes 6 kinds of body sounds Bowel sound, Speech, Snore, Cough, Groan, and Rub. We do sound event detection (SED) based on it, and can accurately detect 6 types of sound events. First, the GI Sound Set is collected by wearable auscultation devices. To ensure generalization, patients from five different hospital departments are recruited for data collection, along with a group of healthy subjects. GI Sound Set refers to Google AudioSet in data format but varies in audio length and sampling rate. Second, we extract Mel-filter features from the recordings and investigate the performance of different activation functions and neural network architectures for detecting sound events. We use data augmentation, class balance to deal with the problem of quantitative imbalance between classes on the dataset. We apply multiple instances learning(MIL) to give out not only bag-level results but also frame-level results. In this work, GI Sound Set is the largest body sound dataset to date, and our approach shows state-of-the-art performance with an average score of F1=81.06% evaluated on the test set. Due to its simple network and conventional processing method, our CRNN system has high universality, which can be used in other audio datasets, such as respiratory sound and heart sound.
Pose Estimation for Non-cooperative Spacecraft based on Deep Learning

The pose estimation of non-cooperative space-borne object is of vital importance for on-orbit service and spacecraft approaching missions. Based on images taken by a monocular camera, an estimate algorithm is proposed to estimate the relative position and the relative attitude of non-cooperative spacecraft. The approach utilizes the off-the-shelf target detection network and key point regression network to predict 2D key points coordinates and combines the multiple view triangulation to reconstruct 3D model. Nonlinear least squares method is used to minimize 2D-3D corresponding coordinates to predict position and attitude. The proposed method effectively combines deep learning and geometric optimization algorithms, which is an innovative application of deep learning in the aerospace field. Finally, simulations are performed to prove the effectiveness of the theoretical results.
Cross-Subject emotion recognition from EEG using Convolutional Neural Networks

Using electroencephalogram (EEG) signals for emotion detection has aroused widespread research concern. However, across subjects emotional recognition has become an insurmountable gap which researchers cannot step across for a long time due to the poor generalizability of features across subjects. In response to this difficulty, in this study, the moving average(MA) technology is introduced to smooth out short-term fluctuations and highlight longer-term trends or cycles. Based on the MA technology, an effective method for cross-subject emotion recognition was then developed, which designed a method of salient region extraction based on attention mechanism, with the purpose of enhancing the capability of representations generated by a network by modelling the interdependecices between the channels of its informative features. The effectiveness of our method was validated on a dataset for emotion analysis using physiological signals (DEAP) and the MAHNOB-HCI multimodal tagging database. Compared with recent similar works, the method developed in this study for emotion recognition across all subjects was found to be effective, and its accuracy was 66.23% for valence and 68.50% for arousal (DEAP) and 70.25% for valence and 73.27% for arousal (MAHNOB) on the Gamma frequency band. And benefiting from the strong representational learning capacity in the two-dimensional space, our method is efficient in emotion recognition especially on Beta and Gamma waves.
Fixed-time Distributed Optimization for Multi-Agent Systems Using an Event-Triggered Communication Scheme

In this paper, a problem of distributed optimization is investigated for multi-agent systems. The event-triggered communication scheme and fixed-time control method are used. First, for the aim of reducing the consumption of hardware resources and improving the communication efficiency of multi-agent systems, an event-triggered communication scheme is proposed. The scheme only depends on the states of each agent and its neighbors. Second, the convergence of fixed-time distributed optimization algorithm using event-triggered communication scheme is proved by Lyapunov theory. At last, the proposed approach is verified by simulation. The results are given and clearly show the effectiveness of the fixed-time distributed optimization algorithm using the event-triggered communication scheme for multi-agent systems.
Performance Analysis of NOMA in 5G Systems With HPA Nonlinearities

In this paper, we provide an analytical performance assessment of downlink non-orthogonal multiple access (NOMA) systems over Nakagami- $m$ fading channels in the presence of nonlinear high-power amplifiers (HPAs). By modeling the distortion of the HPA by a nonlinear polynomial model, we evaluate the performance the NOMA scheme in terms of outage probability (OP) and ergodic sum rate. Hence, we derive a new closed-form expression for the exact OP, taking into account the undesirable effects of HPA. Furthermore, to characterize the diversity order of the considered system, the asymptotic OP in the high signal-to-noise (SNR) regime is derived. Moreover, the ergodic sum rate is investigated, resulting in new upper and lower bounds. Our numerical results demonstrate that the performance loss in presence of nonlinear distortions is very substantial at high data rates. In particular, it is proved that in presence of HPA distortion, the ergodic sum rate cannot exceed a determined threshold which limits its performance compared to the ideal hardware case. Monte-Carlo simulations are conducted and their results agree well with the analytical results.