A Prototype Target Tracking Network System for Robot Feedback Control

Real-Time Feedback Control of Carbon Content of Zirconium Dioxide Thin Films Using Optical Emission Spectroscopy

The J-TEXT newly deployed three-wave polarimeter-interferometer (POLARIS) system provides a better time and spatial resolution of the plasma electronic density than the old HCN interferometer system. The plasma electron density feedback control system is implemented on the already existing POLARIS DAQ system which is based on FlexRIO FPGA. Another FlexRIO board with an output module is added to implement the feedback control algorithm and feed the output to the piezoelectric crystal valve. The density feedback control system is able to extract the phase difference information from the intermedia frequency signal using FFT, calculate density of multiple channels and output control signal to the piezoelectric crystal valve in real-time. NI P2P technology is used to transfer processed data from a FlexRIO board to another in real-time without using the CPU. This system is also able to calculate density profile. With the density profile it can use compensated central chord line integrated or average density as control target. Also the real-time density profile data is useful for future plasma control system and disruption detection system.

The power generation system with renewable energy supply is susceptible to the influence of external environment. Lithium battery and other energy storage devices need to be added in the new energy field to smooth the output of renewable energy generation system and improve the stability of the integrated system. Accurately estimating the state of charge (SOC) of energy storage batteries can effectively improve the use and reasonable scheduling of batteries. This paper takes ternary lithium battery pack as the research object and builds a second-order RC equivalent circuit model to estimate the SOC. According to the time-varying characteristics of battery model parameters, recursive least square method with forgetting factor was used to identify the parameters, the model parameters are modified using the current data. To solve the problem of accumulated error of extended Kalman filter (EKF) algorithm, an adaptive fading factor was introduced to correct prediction error covariance matrix and suppress the influence of historical data on the current state. Matlab simulation and dynamic stress testing experiments show that, compared with EKF algorithm, the adaptive fading EKF (AFEKF) algorithm has higher accuracy.

Purpose of reseach. The article deals with the study of the operation of the extended Kalman filter (EKF), supplemented with an adaptive digital filter in order to compensate for the error in the operation of the EKF when performing data integration of the mobile robot control system.Methods. The adaptive digital filter (ADF) is a self-tuning filter that iteratively changes its variable parameters to achieve the optimal desired values of the output data. The EKF supplemented with the ADF with the NLMS adaptation algorithm will be called the EKF–ADF system or the EKF+NLMS digital filter. An important task is the selection of the number of frames and ADF weighting coefficients at which the optimum quality of noise suppression and the convergence rate of the adaptation algorithm are achieved.Results. With various options of organizing the operation of the ADF buffer memory, the adjusted values for assessing the state of the ‘mobile robot–environment’ system may differ. When the number of input data frames and ADF weighting coefficients are small, low quality noise suppression will be observed. With an increase in the number of frames and weighting coefficients, the quality of noise suppression is improved, and the convergence rate of the adaptation algorithm decreases.Conclusion. The EKF+NLMS digital filter algorithm takes an intermediate place between the EKF algorithm and the serial filtering of the EKF and ADF signals with the NLMS algorithm according to the criteria for estimating the mean square error, mean absolute error, signal-to-noise ratio, and convergence rate.

The present article is a position paper reviewing the main requirements and existing achievements of co-design approaches for real-time control and computing. This problem arises with the increasing complexity of modern computers which require more integrated methodologies, specifically suited to critical embedded systems. The general problem to be solved is the achievement of multi-objective goals (i.e. mixing stability, performance and dependability requirements) under constraints of limited execution resources (combining hardware and software components in CPUs and networks). It is expected that co-design approaches, handling the constraints arising from the control and real-time computing domains at early design time, can improve the overall effectiveness of distributed real-time controllers.

This paper compares algorithms for velocity estimation for feedback control using position encoder measurements. Simulations and experiments for position-velocity control of motors using both fixed-time and fixed-displacement algorithms are compared for various operating conditions. While no single approach is optimal for all situations, least-squares fit algorithms are promising in general. Mathematical models are developed to predict the performances of the algorithms. Specifically, upper bounds on absolute and relative errors for each of the algorithms under different constant velocity and constant acceleration conditions are derived. The algorithms are extensively tested in simulation and also on data obtained from an experimental setup consisting of a motor, two encoders with different resolutions, and a real-time feedback control system. The theory and simulations are confirmed by experimental results.

As a fundamental prerequisite for a variety of location-based services, indoor location information has received increasing attention in recent years. Under the line-of-sight condition, the positioning accuracy of the indoor positioning technology based on ultrawideband (UWB) is acceptable for many applications, but under the non-line-of-sight condition, it degrades dramatically. The positioning accuracy can be significantly improved by the fusion of inertial measurement units and UWB sensors based on the extended Kalman filter (EKF) algorithm. However, when UWB measurements are affected by large non-Gaussian noise, the assumption of the EKF algorithm that observations are subject to Gaussian distribution for noise is invalid. Although the non-Gaussian noise can be handled by the robust EKF algorithm, this algorithm only uses the prior information to judge the reliability of the observations, and the positioning result is not stable when the number of beacons is small. To solve this problem, a method for successive updating of the covariance and posterior state of the observations in iterations based on an iterated extended Kalman filter (IEKF) is proposed. The marginal distribution of the posterior distribution is constructed and iteratively optimized, inhibiting the effect of non-Gaussian noise on UWB under a complex environment. The positioning results of the proposed method, the standard EKF algorithm, and the robust EKF algorithm, using different numbers of beacons, are compared. The results show that the positioning accuracy of the proposed algorithm is the highest under all scenarios. The proposed algorithm shows the smallest decrease in accuracy and presents the most stable positioning when the number of beacons is small, which is a common situation in practical applications.

To overcome productivity issues and revolutionize the stagnating construction industry, a large amount of research efforts has been devoted to robot-assisted construction technology. The advancements in robotics including mechanical system design, tool design, digital system design, and numerical control systems design enabled engineers to create complex geometries that are infeasible for conventional construction methods. In addition, innovative robotic systems that utilize mobile platforms, multiple robots, and unmanned aerial vehicles have demonstrated significant promise in fully automating the construction process. This work will provide a perspective on the state-of-the-art applications of robotics in the revolution of construction, where a comprehensive review of the current development of the relevant software and hardware, 3D concrete printing (3DCP), robot-assisted assembly of discrete prefabricated blocks, real-time quality monitoring and feedback control systems, and typical innovative robot-assisted structural designs are conducted. Finally, the limitations of existing robot-assisted construction technology are identified, which leads to several recommendations for future research toward fully automatic construction.

A real-time and effective double-loop feedback control system for underwater flexible manipulators is raised in this paper. The research object is a kind of underwater flexible manipulator driven by McKibben water hydraulic artificial muscle (WHAM) that can grasp, swallow, and disgorge target objects in its interior space. To make up for the lack of flexibility, an underwater flexible manipulator collaborative working strategy is proposed. A more flexible and smaller flexible manipulator is placed inside the flexible manipulator to assist it in performing difficult underwater works. The control system feeds back the position of internal objects through a built-in binocular camera and the working state of the manipulator through displacement sensors. The control system setups including underwater flexible manipulator subsystem, hydraulic drive subsystem, PLC control subsystem, displacement sensor subsystem, built-in binocular vision subsystem, and upper computer subsystem is built. PYTHON-based built-in binocular vision software and C++-based underwater flexible manipulator control software are also developed to facilitate observation and recording. The underwater flexible manipulator collaborative experiment is designed to verify the performance of the control system and the control algorithm.

Optimization of jet engine sprays has the potential to improve efficiency and reduce environmental impact. Sprays can be continually optimized in multivariate scenarios using real-time feedback control, but a method of controlling the sprays based on physical properties must first be established. In this study, a spray controller was developed to optimize the spray angle obtained from shadowgraphs, with the assumption that the largest angle is desired. The spray angle was used as an example, as it is a physically important parameter which is easily found through shadowgraph imaging. Varying ratios of swirled air to straight air, determined by the image-based feedback controller were introduced into the air portion of a coaxial airblast nozzle while keeping the total air flow rate constant. A golden section search converged on the swirled air ratio that provided the largest angle and was validated from the distribution of spray angle versus swirled air ratio. The ratio that produced a spray with the greatest angle of 25.8 ± 2 deg was found at a swirled air ratio of 0.66 ± 0.03 for a spray with a momentum ratio of 6. The successful design and implementation of this image-based feedback controller is intended to provide a foundation for developing real-time active feedback controllers for sprays.

The discrimination of inrush currents and internal fault currents in transformers is an important feature of a transformer protection scheme. The harmonic current restrained feature is used in conventional differential relay protection of transformers. A literature survey shows that the discrimination between the inrush currents and internal fault currents is still an area that is open to research. In this paper, the classification of internal fault currents and magnetic inrush currents in the transformer is performed by using an extended Kalman filter (EKF) algorithm. When a transformer is energized under normal conditions, the EKF estimates the primary side winding current and, hence, the absolute residual signal (ARS) value is zero. The ARS value will not be equal to zero for internal fault and inrush phenomena conditions; hence, the EKF algorithm will be used for discriminating the internal faults and inrush faults by keeping the threshold level to the ARS value. The simulation results are compared with the theoretical analysis under various conditions. It is also observed that the detection time of internal faults decreases with the severity of the fault. The results of various test cases using the EKF algorithm are presented. This scheme provides fast protection of the transformer for severe faults.

This paper proposes a new move toward to assess the performance of sensorless control of interior permanent magnet synchronous motor (IPMSM) drive along with electromagnetic noise effect by using EKF. Normally in rotary condition, rotor position and speed estimation of IPMSM drive are drawn through an Extended Kalman Filter (EKF) algorithm by measuring its voltages and currents of the stator. The main drawback in developing EKF is it may not proficient to consider the effect of electromagnetic noise which is mainly produced during the time of different speed ranges. Owing to this reason this may cause to vary the motor flux linkages which are significant to find the rotor position and speed by EKF method will give approximate results. To carry on this process, we present the simulation results for sensorless speed control of IPMSM drive by using EKF algorithm with the incorporation of a noise signal which is corresponding to the frequency of electromagnetic noise signal using MATLAB/Simulink software. The armature current, rotor position, and speed estimation are analyzed under this noise signal effect and the effectiveness of the EKF for sensorless control of IPMSM drive is observed.

BeiDou Navigation Satellite System (BDS) is composed of satellites in geostationary Earth orbit (GEO), medium Earth orbit (MEO) and inclined geosynchronous orbit (IGSO). However, the orbit determination of geostationary Earth orbits and of geosynchronous orbits (GSO) with small inclination angle and small eccentricity is a challenging task that is addressed in this paper using Extended Kalman Filter (EKF). The satellite positions were predicted in Earth-centred inertial (ECI) reference frame when propagated through Keplerian model and perturbation force model for different values of right ascension of ascending node (RAAN). Root mean square (RMS) errors of 9.61 cm, 6.73 cm and 11.46 cm were observed in ECI X, Y and Z satellite position coordinates of GSO respectively, whereas, the RMS errors for GEO satellite were 8.89 cm, 7.92 cm, and 0.93 cm respectively in ECI X, Y and Z coordinates; for perturbation force model with maximum value of RAAN when compared with dynamic orbit determination model. Kolmogorov-Smirnov test for EKF reported a p-value > 0.05, indicating a good fit of perturbation force model for orbit propagation. Orbit determination using EKF with perturbation force model were compared with that using EKF with Kepler's model. Wilcoxon Rank Sum test was used to compare the residuals from EKF algorithm through Kepler's model and perturbation force model. EKF with Perturbation force model showed improvement in predicting the satellite positions as compared to Kepler's model. EKF with Perturbation force model was further applied to International GNSS Service (IGS) station data and kilometre level accuracy was achieved. RMS errors of 0.75 km, 2.53 km and 1.91 km were observed in ECI X, Y and Z satellite position coordinates of GSO, respectively, whereas, the RMS errors for GEO satellite were 3.89 km, 4.20 km and 6.66 km respectively in ECI X, Y and Z coordinates for perturbation force model.

The development process pertaining to the design, fabrication, and testing of a 40-stage free-running arc synchronous distributed energy railgun is presented. Investigation of this type of system will determine the effectiveness of a distributed energy scheme to suppress the plasma restrike phenomenon and increase plasma armature railgun performance [1]. Determined by a computer simulation, the proposed system will have a 1.0 cm × 1.0 cm square bore cross section and a stage length of 15.24 cm producing a total rail length of 6 meters for 40 stages. A free-arc is utilized to relieve the financial burden of a large stored energy facility. A velocity of 8 km/s is desired to emulate conditions during a high altitude microsatellite launch. To achieve this velocity, pulsed power in conjunction with a low pressure (∼ 10 Torr) air environment is required. The pulsed power supplies 15 kJ of energy to provide an armature current (∼ 50 kA) for 1 millisecond. A real-time feedback control system will accurately release the stage energy upon arc arrival. Experimental data collected from a 7-stage prototype distributed system is discussed which will mimic the design and operation of the first 7 stages associated with the 40-stage railgun. The copper rail length is 1.2 m long with a 1 cm × 1 cm square bore cross section and a 15.24 cm stage length. Each distributed energy stage contains a 750 µF capacitor bank, a thyristor with an anti-parallel diode, and a driver board for triggering. The armature is formed using a plasma injector that is powered by a 40 kV Marx generator. Diagnostics for this examination include rail B-dot probes as well as independent Rogowski coils for each stage. Data collected from the rail B-dot probes will be used to measure the armature position and velocity as a function of time. There is no target velocity for this prototype; repeatable energy module operation, accurate stage triggering, and arc propagation toward the muzzle are the main areas focus. Outcomes of these initial experimental results will aid the development of the 40-stage system.

In this paper, the Extended Kalman Filtering (EKF) based learning algorithm which has much faster convergence speed than the backpropagation algorithm has been extended for training the neural network used for Adaptive Equalization of communication channel. Further, it has been shown that the bit error rate obtained using the EKF algorithm is much superior to that was obtained with the backpropagation algorithm. But due to its computational intensity, parallel processing of the EKF based learning algorithm is indispensable for real time implementation. Hence, parallel implementation of the EKF based learning algorithm on a network of three transputers has been developed.