Speed Tracking Research Articles

A composite adaptive robust control for nonlinear systems with model uncertainties

AbstractThis article is concerned with a composite adaptive robust control (CARC) for non‐strict feedback nonlinear systems with model uncertainties including parametric uncertainties and uncertain nonlinearities. It is assumed that the model uncertainties of the nonlinear system are bounded but unknown. Firstly, prescribed tracking performance is achieved by the CARC control law even the unknown parameters are time‐varying. This is accomplished firstly by designing a discontinuous projection that confines the unknown parameters in an estimated region. Then the robustness of the closed‐loop system is guaranteed by leveraging the deterministic robust control philosophy. Also, a command filter has been designed to avoid computing complex derivatives of virtual control law in the backstepping procedure. Secondly, a composite adaptation law that intelligently integrates information of both tracking error and prediction error is constructed for improving the performance of controllers. The high adaptation gain of CARC leads to higher parameter convergence speed and smaller tracking errors. Moreover, if the actual parameters are within the design range, asymptotic stability can be achieved in presence of parametric uncertainties only. In addition, exponential convergence of tracking errors and parameters can be shown under certain conditions. Finally, numerical examples are made to validate the superiority and effectiveness of the proposed control scheme.

Design, analysis, and adaptive maximum power point tracking control of small‐scale wind turbine system

SummaryDue to the stochastic and unpredictable nature of wind speed, extraction of maximum power from the wind turbine system becomes an attractive control objective. Therefore, to capture more wind power, various conventional maximum power point tracking (MPPT) schemes are developed for standalone small‐scale variable speed wind power generation system (VSWPGS). This manuscript proposes novel adaptive step size (ASS) and drift‐free ASS (DF‐ASS) MPPT methods for a permanent magnet synchronous generator (PMSG)‐based VSWPGS. The control schemes achieve maximum power point (MPP) without using mechanical sensors like speed encoder and anemometer. The proposed MPPT controllers are implemented on a laboratory‐scale DC motor‐PMSG‐based wind power generation system (WPGS) using OPAL‐RT real‐time digital platform. The ASS and DF‐ASS methods capture more power, reduce the steady‐state power oscillation around MPP, and improve the tracking speed. Also, the proposed DF‐ASS control scheme avoids drift phenomenon during wind speed increase condition. The comparative experimental results of proposed ASS and DF‐ASS methods with respect to the fixed step size MPPT schemes show better dynamic and steady‐state performance, making it pertinent for small‐scale WPGS.

A Cascade BP Neural Network Tuned PID Controller for a High-Voltage Cable-Stripping Robot

A 10 kV distribution network is a crucial piece of infrastructure to guarantee enterprises' and households' access to electricity. Stripping cables is one of many power grid maintenance procedures that are now quickly expanding. However, typical cable-stripping procedures are manual and harmful to workers. Although numerous automated solutions for grid maintenance have been created, none of them focus on cable stripping, and most of them have large dimensions to guarantee multi-functions. In this paper, a new cable-stripping robot for the 10 kV power system is introduced. The design of a live working cable-stripping robot that is appropriate for installing insulating rods is introduced, taking into account the working environment of 10 kV overhead lines and the structural characteristics of overhead cables. The robot is managed by an auxiliary remote control device. A cascade PID control technology based on the back propagation neural network (BPNN) method was developed, as the stripper robot's whole system is nonlinear and the traditional PID controller lacked robustness and adaptability in complex circumstances. To validate the structural feasibility of the cable-stripping robot, as well as the working stability and adaptability of the BPNN-PID controller, a 95 mm2 cable-stripping experiment are carried out. A comparison of the BPNN-PID controller with the traditional PID method revealed that the BPNN-PID controller has a greater capacity for speed tracking and system stability. This robot demonstrated its ability to replace manual stripping procedures and will be used for practical routine power maintenance.

Study of the psychomotor abilities of athletes in cyclic sports, martial arts and esports

Purpose: to conduct a comparative analysis of psychomotor abilities in athletes of cyclic sports (short track), martial arts (karate, taekwondo) and esports (CS:GO). Materials, participants and methods: The study involved athletes of percussion martial arts (n=10, age 18,9±0,12, candidates for master of sports), elite players of semi-professional CS:GO teams (n=10, age 19,2±0,50), short track speed skaters (n=10, age 19±0,09, masters of sports). The choice reaction time, the frequency of local movements (60-second tapping test), and the reaction time to a change in the size of the object (Size-test) were determined. Research results. Esports athletes have the best reaction time of choice and tapping test compared to other athletes. The wrestlers showed the best reaction to distinguishing the size of an object in the Size-Test. Skaters have high rates of complex reaction time and frequency of movements. The duration of the tapping test has a likely inverse relationship with the frequency of tapping. Conclusions. It was found that esports athletes have a significantly better choice reaction time (p<0,05) than martial arts athletes. The results of short track skaters do not have significant differences with martial arts and esports athletes (p>0.05). The results of the tapping test in terms of the total number of movements and the average number for 5 s in esports athletes tend to be higher compared to short track speed skaters and martial arts athletes. The fastest response time to a change in the size of an object in the Size-test was shown by martial arts athletes, the slowest by short-track skaters. This reflects the trend towards better results of this type of reaction in martial arts athletes compared to short track speed skaters and esports athletes.

Reset-free adaptive polarization controller on a silicon-photonic platform for a self-coherent communication system.

To give full play to the advantages of the self-coherent systems in the data center scene, the problem of the random walk of the polarization state of the delivered local oscillator must be solved. An adaptive polarization controller (APC) is an effective solution, with the characteristics of easy integration, low complexity, being reset-free, and so on. In this work, we experimentally demonstrated an endlessly APC based on a Mach-Zehnder interferometer on a silicon-photonic integrated circuit. The APC is thermally tuned with only two control electrodes. It endlessly stabilizes the arbitrary state of polarization (SOP) of the light to a state of equal power of the two orthogonal polarizations (X and Y). A polarization tracking speed of up to 800 rad/s is achieved.

Fractional Order Sliding Mode Controller Based on Supervised Machine Learning Techniques for Speed Control of PMSM

Tracking the speed and current in permanent magnet synchronous motors (PMSMs) for industrial applications is challenging due to various external and internal disturbances such as parameter variations, unmodelled dynamics, and external load disturbances. Inaccurate tracking of speed and current results in severe system deterioration and overheating. Therefore, the design of the controller for a PMSM is essential to ensure the system can operate efficiently under conditions of parametric uncertainties and significant variations. The present work proposes a PMSM speed controller using machine learning (ML) techniques for quick response and insensitivity to parameter changes and disturbances. The proposed ML controller is designed by learning fractional-order sliding mode control (FOSMC) controller behavior. The primary purpose of using ML in FOSMC is to avoid the self-tuning of the parameters and ensure the speed reaches the reference value in finite time with faster convergence and better tracking precision. Furthermore, the ML model does not require the mathematical model of the speed controller. In this work, several ML models are empirically evaluated on their estimation accuracy for speed tracking, namely ordinary least squares, passive-aggressive regression, random forest, and support vector machine. Finally, the proposed controller is implemented on a real-time hardware-in-the-loop (HIL) simulation platform from PLECS Inc. Comparative simulation and experimental results are presented and discussed. It is shown from the comparative study that the proposed FOSMC based on ML outperformed the traditional sliding mode control (SMC), which is more commonly used in industry in terms of tracking speed and accuracy.

Object tracking in surveillance video based on Siamese network considering geographical spatiotemporal constraints

AbstractIdentifying and tracking objects in surveillance videos is an important means of mining information during surveillance. Currently, most object‐tracking methods rely only on image features, which cannot accurately express the motion of the tracked object in real geographical scenes and are easily influenced by occlusion and surrounding objects having similar features. However, tracked objects, such as pedestrians and vehicles, usually move in geographical space with fixed patterns of motion, and the motion in a short time is constrained by geographical space and time, the motion trajectory is predictable, and the range of motion is limited. Therefore, based on the SiamFC object tracking framework, this study introduces geographical spatiotemporal constraints into the tracking framework and proposes the GeoSiamFC method. The objective of this is to: (1) construct the mapping relationship between geographical space and image space to solve the problem that the pixel movement within the image after perspective imaging cannot accurately express the motion of the tracked object in a real geographical scene; (2) add candidate search areas according to the predicted trajectory location to correct the tracking errors caused by the occlusion of the object; and (3) focus on the search for the range of motion of the mapped pixel within the image space according to the limited geographical range of motion of the tracked objects in a certain time to reduce the interference of similar objects within the search area. In this study, separate experiments were conducted on a common test dataset using multiple methods to deal with challenges such as occlusion and illumination changes. In addition, a robust test dataset with noise addition and luminance adjustment based on the common test dataset was used. The results show that GeoSiamFC outperforms other object‐tracking methods in the common test dataset with a precision score of 0.995 and a success score of 0.756 compared with most other object‐tracking algorithms under the base condition of using only shallow networks. Moreover, GeoSiamFC maintained the highest precision score (0.970) and high success score (0.734) in the more challenging robust test dataset as well. The tracking speed of 59 frames per second far exceeds the real‐time requirement of 25 FPS. Geographical spatiotemporal constraints were considered to improve tracker performance while providing real‐time feedback on the motion trajectory of the target in geographical space. Thus, the proposed method is suitable for real‐time tracking of the motion trajectory of a target in real geographical scenes in various surveillance videos.

Temporal contexts for motion tracking in ultrasound sequences with information bottleneck.

Recently, deep convolutional neural networks (CNNs) have been widely adopted for ultrasound sequence tracking and shown to perform satisfactorily. However, existing trackers ignore the rich temporal contexts that exists between consecutive frames, making it difficult for these trackers to perceive information about the motion of thetarget. In this paper, we propose a sophisticated method to fully utilize temporal contexts for ultrasound sequences tracking with information bottleneck. This method determines the temporal contexts between consecutive frames to perform both feature extraction and similarity graph refinement, and information bottleneck is integrated into the feature refinementprocess. The proposed tracker combined three models. First, online temporal adaptive convolutional neural network (TAdaCNN) is proposed to focus on feature extraction and enhance spatial features using temporal information. Second, information bottleneck (IB) is incorporated to achieve more accurate target tracking by maximally limiting the amount of information in the network and discarding irrelevant information. Finally, we propose temporal adaptive transformer (TA-Trans) that efficiently encodes temporal knowledge by decoding it for similarity graph refinement. The tracker was trained on 2015 MICCAI Challenge on Liver Ultrasound Tracking (CLUST) dataset to evaluate the performance of the proposed method by calculating the tracking error (TE) between the predicted landmarks and the ground truth landmarks for each frame. The experimental results are compared with 13 state-of-the-art methods, and ablation studies areconducted. On CLUST 2015 dataset, our proposed model achieves a mean TE of 0.81 ± 0.74 mm and a maximum TE of 1.93 mm for 85 point-landmarks across 39 ultrasound sequences in the 2D sequences. Tracking speed ranged from 41 to 63 frames per second (fps). This study demonstrates a new integrated workflow for ultrasound sequences motion tracking. The results show that the model has excellent accuracy and robustness. Reliable and accurate motion estimation is provided for applications requiring real-time motion estimation in the context of ultrasound-guided radiationtherapy.

An Integral-Backstepping Controller for Interleaved Boost Converter based on Photovoltaic Systems

Photovoltaic systems are one of the most widely used technologies for sustainable energy production due to their various advantages, such as being clean, free, and renewable energy sources. However, the power generated by the PV systems is strongly dependent on the atmospheric conditions. In addition, a suitable DC/DC converter must be used with a robust controller to facilitate system operation point changes associated with the changed climate conditions. In this paper, to deal with this challenge, a photovoltaic system based on four leg interleaved boost converter (FLIBC) has been proposed and studied. For robustness, the presented system is controlled using nonlinear integral backstepping control (IBSC) to track the maximum power point (MPP) and ensure an equal sharing current among the FLIBC legs. Moreover, the particle swarm optimization algorithm is used to find the optimum gains of the proposed IBSC. The entire system is simulated and validated using MATLAB/ Simulink environment. The simulation results demonstrate the efficiency of the proposed system based on the optimized IBSC (OIBSC) controller in terms of MPP tracking speed, overshoot and undershoot reduction, power ripple, and optimization of the performance term integral time square error (ITSE).

Dynamic Analysis of a Coupled System of High-Speed Maglev Train and a Flexible Long-Span Continuous Rigid Frame Bridge

The dynamic characteristics, deformation and installation accuracy of the bridge structure will have a greater impact on the response of maglev system. This study performs the dynamic analysis of a flexible long-span continuous rigid frame bridge induced by high-speed maglev train. To achieve this, the spatial coupling analysis model is first established. Then, the influence of track irregularities and speed on the maglev system is analyzed. In addition, the mechanism of vertical resonance of the bridge and vehicle under the action of harmonic loads at low speed is also studied by a semi-analytical approach. The results show that the influence of the long-span bridge deformation on car-body vertical acceleration is greater than that of track irregularity. The resonance exists between train and the long-span bridge at low speed. When the frequency of the harmonic loads is consistent with the basic frequency of the bridge, it will cause the bridge resonance. The resonant speed range of the rigid car-body is 20–40[Formula: see text]km/h. During bridge design, it shall be avoided that the basic frequency of the bridge is consistent with that of the vehicle.

A bi-level control framework with temperature rise inference and intelligent decision-making longitudinal control on downgrade hydrodynamic braking process

Temperature rise is a salient characteristic of the braking device of heavy-duty vehicles (HDVs) during long downhill braking. The temperature-independent effect on braking performance is an inevitable challenge for the longitudinal controller, which also causes the overheating of mechanical or hydrodynamic braking devices and may bring about brake failures for heavy-duty vehicles. To this end, a universal Bi-level control framework combining a temperature hierarchy system performance prediction method and a deep reinforcement learning (DRL)-based controller is proposed for long downhill braking of heavy-duty vehicles. Firstly, the temperature-independent characteristic is clustered to predict the braking performance under various rotating speeds. Secondly, a data-driven model for temperature rising is built for the long-time braking thermal prediction and estimates the safety remaining braking time using the auxiliary braking. Thirdly, a temperature-hierarchy environmental perceptive control framework with Double Deep Q Network (DDQN) algorithm is exploited to achieve the target speed tracking accuracy. Thermal safety is ensured with the application of fast calculating for the thermal rising, along with the effective estimation of remaining braking performance on endurance braking. The proposed Bi-level longitudinal controller is compared with the average-temperature strategy and the classic PID strategy to validate its superiority in terms of speed-tracking accuracy on robust conditions. The simulation results show that the proposed strategy improves the speed tracking accuracy by 37.31% on constant slope conditions and 68.11% on varying slope conditions compared with the classic PID strategy. Furthermore, a processor-in-the-loop test experiment verifies its real-time application.

A Fast Hyperspectral Tracking Method via Channel Selection

With the rapid development of hyperspectral imaging technology, object tracking in hyperspectral video has become a research hotspot. Real-time object tracking for hyperspectral video is a great challenge. We propose a fast hyperspectral object tracking method via a channel selection strategy to improve the tracking speed significantly. First, we design a strategy of channel selection to select few candidate channels from many hyperspectral video channels, and then send the candidates to the subsequent background-aware correlation filter (BACF) tracking framework. In addition, we consider the importance of local and global spectral information in feature extraction, and further improve the BACF tracker to ensure high tracking accuracy. In the experiments carried out in this study, the proposed method was verified and the best performance was achieved on the publicly available hyperspectral dataset of the WHISPERS Hyperspectral Objecting Tracking Challenge. Our method was superior to state-of-the-art RGB-based and hyperspectral trackers, in terms of both the area under the curve (AUC) and DP@20pixels. The tracking speed of our method reached 21.9 FPS, which is much faster than that of the current most advanced hyperspectral trackers.

Online Running-Gait Generation for Bipedal Robots with Smooth State Switching and Accurate Speed Tracking.

Smooth state switching and accurate speed tracking are important for the stability and reactivity of bipedal robots when running. However, previous studies have rarely been able to synthesize these two capabilities online. In this paper, we present an online running-gait generator for bipedal robots that allows for smooth state switching and accurate speed tracking. Considering a fluctuating height nature and computational expediency, the robot is represented by a simplified variable-height inverted-pendulum (VHIP) model. In order to achieve smooth state switching at the beginning and end of running, a segmented zero moment point (ZMP) trajectory optimization is proposed to automatically provide a feasible and smooth center-of-mass (CoM) trajectory that enables the robot to stably start or stop running at the given speed. To accurately track online the desired speed during running, we propose an iterative algorithm to compute target footholds, which allows for the robot to follow the interactive desired speed after the next two steps. Lastly, a numerical experiment and the simulation of online variable speed running were performed with position-controlled bipedal robot BHR7P, and the results verified the effectiveness of the proposed methods.

Maximum Power Point Tracking for Solar Photovoltaic System Based on Interval Type-3 Fuzzy Logic: Practical Validation

Maximum power point tracking (MPPT) is a technique that allows photovoltaic (PV) arrays to operate at or near their maximum power point (MPP) under all weather conditions. Fuzzy logic controller (FLC) is the most popular method to monitor the MPPT, such as Type-1 fuzzy logic (T1FL), Type-2 fuzzy logic (T2FL), and Type-3 fuzzy logic (T3FL) controllers. This article proposes a new T3FL algorithm that improves tracking efficiency and tracking speed because it is better equipped to deal with uncertainty arising from disturbances. The proposed system consists of a PV module, a DC-DC buck converter controlled by the MPPT algorithm, a resistive load, and a battery. The PV solar panel is directly connected to the DC-DC buck converter, which operates based on the proposed controller’s output pulse width to ensure that the PV system works at MPP. The T3FL algorithm has the advantage of extracting the maximum power from the PV panel while preventing battery damage caused by variable MPPT voltage and thus extending battery lifetime. The maximum power and voltage of T3FL are compared with the existing T1FL and T2FL of battery and ohmic load. The MPPT performance of the proposed different algorithms was applied to a PV module and a DC-DC buck converter to be evaluated via simulation studies and experimental studies. MATLAB/Simulink is used to implement different algorithms. Several weather conditions are simulated: (i) uniform irradiation, (ii) sudden changing, and (iii) partial shading. A real-time implementation of the three MPPT algorithms by using Arduino Uno and spatial Simulink package known as the “support package for Arduino hardware”. Finally, the simulation and experimental results demonstrate that the T3FL controller of an ohmic load provides accurate maximum power, high levels of stability, and robust performance against uncertainties arising from disturbances to the PV system’s inputs

Vibration suppression method for flexible link underwater manipulator considering torsional flexibility based on adaptive PI controller with nonlinear disturbance observer

Achieving stable and high-precision control of underwater manipulators in complex ocean environments is one of the biggest challenges faced by the research of marine robotics at present. Flexible link manipulators can be installed on unmanned underwater vehicles to realize underwater operations such as underwater cable inspection, deep sea rescue, and marine organizational collections. The flexible link underwater manipulator will vibrate due to the influence of torsional flexibility and water flow disturbance during rotation, which will seriously affect the motion accuracy of the end actuator. In this paper, a control strategy combining the adaptive proportional-integral (PI) controller and the nonlinear disturbance observer is proposed, which indirectly suppresses vibration by improving the speed control accuracy. Firstly, according to the assumed mode method, a dynamic model of the flexible link underwater manipulator considering torsional flexibility and external disturbance is established. Next, the overload compensation control law is proposed to meet the closed-loop stability of the flexible link underwater manipulator. In addition, the PI controller's adaptive law is designed according to the Lyapunov theorem. Finally, the effectiveness of the adaptive PI controller with a nonlinear disturbance observer is proved by the simulation and control experiments of different control strategies. The results show that the proposed controller can achieve stable speed tracking and has a better vibration suppression effect.

Adaptive Fuzzy-PI for Induction Motor Speed Control

An induction motor (IM) is one type of AC motor which widely used. IM is chosen due to its simplicity, reliability, efficiency, and low cost. There are many methods proposed to control the speed of IM which is known as variable speed drive (VFD). In this study, the DTC method is used since it is more robust to the parameter’s changes. The combination of the Fuzzy and PI method is used in speed control. PID performances decrease when the system condition changes. Therefore, fuzzy is used as an adaptive algorithm to vary the PID gain. It was superior in terms of settling time, overshoot/ undershoot, and IAE compared to the PI method. It has lower IAE in both speed tracking and loaded conditions by 44.98% and 4.47%, respectively.

A Review of Partial Shading MPPT Algorithm on Speed, Accuracy, and Cost Embedded

This paper describes several Maximum Power Point Tracking algorithms for partial shading conditions that have detrimental effects on photovoltaic systems. The method used is a literature review of articles from reputable publishers. Fifty two articles were obtained after meeting the established criteria for selection. The literature review focused on the ability of the Maximum Power Point Tracking algorithm to overcome partial shading conditions in terms of tracking speed, tracking accuracy, efficiency, and implementation complexity. As the results, some algorithms are recommended to be applied for Maximum Power Point Tracking including the Single Swam Algorithm and the Perturb And Observer algorithm, the Enhanced Adaptive Step Size Perturb and Observe algorithm, the Novel Adaptable Step Incremental Conductance algorithm, the Improved Bat Algorithm and Fuzzy Logic Controller algorithm, and the Particle Swarm Optimization with One Cycle Control algorithm. In terms of implementation complexity, these five algorithms are categorized as medium-complexity, which can be characterized as low cost, high efficiency, and even 100% with high tracking speed and accuracy with a minimum number of sensors used.

Research on the angular velocity estimation method based on the state augmentation method

In the maneuvering target tracking research due to the unpredictability of the target’s maneuvering, it makes it difficult to establish an accurate target motion model to reflect the real motion trajectory of the target. Based on the determination of the parametric maneuver model and the radar measurement conditions of the target, the joint estimation method of the motion state and the maneuver parameters is designed to obtain the optimal estimation of the motion state and the maneuver parameters based on the optimal evaluation criteria. Currently, in the descriptive category of the state space model, the state augmentation method is obtained to solve the joint estimation of the motion state and maneuver parameters based on the estimation criterion [1]. Therefore, the angular velocity estimation method of the state augmentation method is used to consider the estimated value of the target angular velocity and the estimated variance comprehensively to improve the convergence speed of target tracking, minimize the error between the filtered angular velocity and the real angular velocity of the maneuvering target, and finally achieve the purpose of improving the target tracking accuracy.

Hybridized GWO-RUN optimized fractional order control for permanent magnet brush-less dc motor

The permanent magnet brush-less dc motor is a nonlinear, multivariable structure and magnetic coupled system wherein the load disturbances and the parameter uncertainties adversely affect the dynamic performance. From this, it required that the designed controller for the system must deal with the complexities. The objective of this paper is to introduce a novel fractional order proportional-integral controller for permanent magnet brush-less dc motor dealing with set parameter tracking problem. In this paper, in order to find the needed optimal gain parameters combination of metaheuristic and numerical optimization algorithm called Grey Wolf-Runge–Kutta algorithm is proposed. The efficacy of the proposed technique is demonstrated by comparing it with existing optimization technique viz. particle swarm, grey wolf, and Runge–Kutta algorithm. Furthermore, the stability and robustness analysis for the proposed controller is also studied for different operating conditions such as set speed variations, load disturbances and parameter variations. The simulation is performed using MATLAB toolbox. From the simulation results it is evident that FOPI controller optimized with novel hybrid algorithm guarantee the best set speed tracking and also capable to ameliorate the system robustness for parameter variations as well as external load disturbances.

Memoryless radial basis function neural network based proportional integral controller for PMSM drives

<p>This research paper presents the memoryless radial basis function neural network (RBFNN) dependent proportional integral (PI) controller for permanent magnet synchronous motor (PMSM) drives. The proposed RBFNN is memoryless adaptation scheme since the algorithm neither use the past samples nor retain the previous adaptation direction and hence the observations are used only once. Firstly, mathematical model for PMSM drive is explained and then structure of RBFNN controller is formulated. RBFNN consists of single input, a hidden layer with five neurons, a single output layer that are used to track the speed of the machine. The Jacobian matrix obtained from RBFNN is used to adjust the gain of the speed and applied to PI controller. The performance of the PI based vector controller with and without RBFNN are compared through the simulation by using MATLAB/Simulink environment. The simulations show that the proposed RBFNN outperform the existing PI based control method and enhances the speed tracking of PMSM drives.</p>