Robust lithium-ion state-of-charge and battery parameters joint estimation based on an enhanced adaptive unscented Kalman filter

Sensors are crucial for the control of unmanned aerial vehicles (UAVs). However, sensor faults will inevitably appear over time. Therefore, it is important to develop a sensor fault detection approach for the reliability of UAV. This article presents a novel model-based UAV fault detection approach based on quadratic Kalman filter (QKF). First, an accurate kinematic and dynamic model of UAVs is established, where the model is linearized and discretized for Kalman filter (KF). Second, the first KF is used for denoising, the secondKF is used to detrend, and residuals are calculated for detection. It is worth mentioning that the second KF is a modified Sage–Husa adaptive KF, which can automatically estimate the measurement noise variance. Compared with traditional approaches, this approach has the advantages of noise reduction, self-adaptation, divergence avoidance, and high detection rate. Simulation and experimental results show the effectiveness of the proposed approach, which can accurately detect the abrupt and incipient fault of an inertial measurement unit (IMU) sensor. At the same time, it can get the higher fault detection rates (FDRs) compared with conventional KF. Furthermore, this approach also provides the leading information and foundation for UAV fault-tolerant control. 

Quadratic-Kalman-Filter-Based Sensor Fault Detection Approach for Unmanned Aerial Vehicles

With the development of autonomous driving technology, some special vehicles should also be developed into autonomous driving vehicles. The robotic vehicles are widely used in engineering operations, and the robotic manipulator is a common tool mounted on robotic vehicles. The autonomous driving of robotic vehicles requires not only automatic movement, but also the automation of the robotic manipulator drive system. Therefore, this paper proposes an adaptive trajectory tracking control of manipulator based on sliding mode control (SMC), which can more accurately describe the joint space movement of the manipulator system under multi task coupling and external interference. Considering the multi-tasking situation, the influence of obstacle avoidance and base tilt on the trajectory of manipulator is investigated by using extreme learning machine (ELM), and the framework of machine learning is established. The output based on ELM-SMC is used as the bottom-layer control. Then an effective trajectory compensation method is proposed as the top-level control to avoid the error accumulation in the periodic repeated operation of the manipulator. Finally, the application effect of trajectory error tracking and compensation of manipulator in complex tasks of dynamic environment is verified by simulation and experiments, which lays a foundation for the stability control of manipulator in practical complex engineering applications. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners —The motivation of this paper is to solve the problem of poor trajectory tracking control accuracy of the manipulator caused by the change of the direction of the manipulator base. It is suitable for the high-precision position control of the manipulator assembled on the mobile robotic vehicle. Nowadays, the autonomous driving technology is developing rapidly, and the automation of traffic will be widely popularized in the future. Therefore, we should not only pay attention to the autonomous driving function of passenger vehicles, but also give consideration to the autonomous driving technology of robotic vehicles applied in engineering. It not only needs to complete the driving function, but also needs to ensure the reliable operation of the manipulator. In this paper, we mathematically deduce the control model of the manipulator. Then, we use SMC as the trajectory tracking control method of the manipulator, and use ELM to estimate the trajectory error caused by the end trajectory of the manipulator in the non-horizontal state of the base. Furthermore, we propose an optimization algorithm to optimize the compensation coefficient of cubic spline and give a reasonable compensation scheme. Through simulation and experiments, it is preliminarily verified that the schemes proposed in this paper are feasible, but they lack the application verification in the actual robotic vehicles, and the model fitting part of machine learning is completed with the help of the host computer, and the calculation ability of the controller of the actual manipulator may also be the limitation of completing this scheme.

A Two-Layer Trajectory Tracking Control Scheme of Manipulator Based on ELM-SMC for Autonomous Robotic Vehicle

Lung Cancer Classification using Optimized Attention-based Convolutional Neural Network with DenseNet-201 Transfer Learning Model on CT image

Permanent magnet eddy current couplers (PMECCs) have the characteristics of contactless torque transmission, removal of torque ripple, smooth dynamic process, and adjustable speed, and can be used as couplings, dampers, brakes, and speed governors. Their applications in industry, vehicles, and energy fields are gradually expanding. At the same time, the requirements for the torque density and dynamic performance of PMECCs are increasing. Therefore, a large amount of research work has focused on the fast and accurate modeling, design, and optimization of PMECCs. This paper provides a survey on the development of PMECCs technology. The main topics include the structure and classification of PMECCs, modeling methods, loss and heat transfer analysis modeling, and optimization design. In addition, this paper shows the future trends of PMECCs research. All the highlighted insights and suggestions of this review will hopefully lead to increasing efforts toward the model’s construction and the optimal design of PMECCs for future applications.

/pdf/a-review-of-recent-developments-in-permanent-magnet-eddy-26wecuf5.pdf

A Review of Recent Developments in Permanent Magnet Eddy Current Couplers Technology

A modified Sage-Husa adaptive Kalman filter for state estimation of electric vehicle servo control system

The adaptive control of manipulator based on sliding mode control and dynamic equation was originally designed and successfully applied to joint space task tracking. However, in the case of multitasking, it has not been extended in practical engineering, so the controller tends to enforce the main engineering tasks, because the parameters tend to the actual values assumed by the model. In view of the influence of multi task control and uncertain disturbance in practical engineering application, the method to deal with this situation has an important impact on manipulator control. In this paper, the transmission delay and dynamic uncertainty are modeled, the multi task constraint is introduced into the dynamics and kinematics model of the manipulator, a two-layer control is proposed, and the trajectory compensation method is introduced. Finally, the effectiveness of the proposed method is verified by simulation and simple manipulator model.

Multi-task Trajectory Tracking Control of Manipulator Based on Two-Layer Sliding Mode Control

This note investigates the control design problem of a permanent-magnet eddy-current coupler (PEC) speed control system. To address this, the PEC speed control system is modeled as a fully-actuated system through electrical and kinematic equations. Considering the situation that the system state quantities are not directly accessible, an observer-based controller is developed using the separate design of the observer and state feedback. Meanwhile, a parametric method is adopted to calculate the gain matrix that allows the closed-loop control system to ensure stability while converging at a desired speed. Finally, the obtained results are effectively applied in a PEC speed control system example.

Design of permanent-magnet eddy-current coupler speed control system based on fully-actuated system model

The permanent magnet eddy current coupling is widely used in fan and pump equipment as an energy-saving speed control device. The traditional coupling speed adjustment method occupies too much axial space, limiting its application in the process of upgrading old equipment. This paper proposes a novel flux adjustable permanent magnet eddy current coupling with a double-sided conductor to reduce the coupling axial distance while increasing the coupling output torque. The relative angle between the permanent magnet rings is controlled to adjust the output torque, and the double-sided conductor structure is used to improve the output torque. The working principle of the proposed design is illustrated. An analytical model for estimating the regulation performance of the proposed structure is proposed, which introduces the regulated reluctance into the equivalent magnetic circuit method. Based on this model, the expressions for magnetic flux density, eddy current density, and output torque are established. Finally, the accuracy of the analytical model was verified by the 3D finite element method, and the parameter sensitivity analysis was performed. The analysis results show that this device can achieve a relatively large range of output torque regulation under the condition of a fixed air gap, and the output torque is greatly improved by adopting a double-sided conductor structure.

/pdf/analytical-analysis-of-a-novel-flux-adjustable-permanent-3ayg012v.pdf

Analytical Analysis of a Novel Flux Adjustable Permanent Magnet Eddy Current Coupling with Double-Sided Conductor

With the rapid development of power electronic circuits, power electronic devices are becoming more and more complex, with many fault types and low diagnostic accuracy. This paper proposes a solution based on variational mode decomposition and sparrow search algorithm to optimize support vector machines. Firstly, Simulink is used to establish the simulation model of three-phase bridge type fully controlled rectifier circuit, and the output voltage signal is decomposed by using variational mode decomposition to calculate and extract fault eigenvalue, and the fault data is normalized; Then the sparrow search algorithm is used to optimize the support vector machine, and the penalty factors and kernel parameters of the optimal estimation are searched out, and the obtained data are trained and diagnosed; Finally, the method is compared with the support vector machine without optimization. The final simulation results show that the method is superior to the unoptimized support vector machine in training accuracy and diagnosis effect.

Fault Diagnosis of Three phase Rectifier Circuit Based on VMD-SSA Algorithm

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