Speed Tracking Research Articles

Effects of Fractional Order on Performance of FOSMC for Speed Control of PMSM

Fractional order sliding mode control has been applied for speed control of PMSM. However, in many previous works, the effects of the controller's parameters have not been studied. This paper investigates the effects of fractional order on performance of FOSMC speed control of PMSM. In this work, fractional order, α and β of FOSMS-PID were varied, and their performances were compared. The simulation and experimental results show that variation of order of fractional order integration, α and order of fractional order differentiation, β can affect the performance of the FOSMC-PID controller. Selection of α and β values determines balancing strategies between integral and differentiation portion of the controller. Proper value selection and combination of these variables can further contribute to obtain optimum speed tracking, disturbance rejection and chattering reduction abilities.

TelecomTM

We introduce the TelecomTM system that uses pre-existing telecommunication fiber-optic cables as virtual strain sensors to sense vehicle-induced ground vibrations for fine-grained and ubiquitous traffic monitoring and characterization. Here we call it a virtual sensor because it is a software-based representation of a physical sensor. Due to the extensively installed telecommunication fiber-optic cables at the roadside, our system using redundant dark fibers enables to monitor traffic at low cost with low maintenance. Many existing traffic monitoring approaches use cameras, piezoelectric sensors, and smartphones, but they are limited due to privacy concerns and/or deployment requirements. Previous studies attempted to use telecommunication cables for traffic monitoring, but they were only exploratory and limited to simple tasks at a coarse granularity, e.g., vehicle detection, due to their hardware constraints and real-world challenges. In particular, those challenges are 1) unknown and heterogeneous properties of virtual sensors and 2) large and complex noise conditions. To this end, our TelecomTM system first characterizes the geographic location and analyzes the signal pattern of each virtual sensor through driving tests. We then develop a spatial-domain Bayesian filtering and smoothing algorithm to detect, track, and characterize each vehicle. Our approach uses the spatial dependency of multiple virtual sensors and Newton's laws of motion to combine the distributed sensor data to reduce uncertainties in vehicle detection and tracking. In our real-world evaluation on a two-way traffic road with 1120 virtual sensors, TelecomTM achieved 90.18% vehicle detection accuracy, 27x and 5x error reduction for vehicle position and speed tracking compared to a baseline method, and ±3.92% and ±11.98% percent error for vehicle wheelbase and weight estimation, respectively.

Driving training‐based optimization (DTBO) for global maximum power point tracking for a photovoltaic system under partial shading condition

AbstractThe presence of bypass diodes in photovoltaic (PV) arrays can mitigate the negative effects of partial shading conditions (PSCs), which can cause multiple peak characteristics at the output. However, conventional maximum power point tracking (MPPT) methods can develop errors and detect the local maximum power point (LMPP) instead of the global maximum power point (GMPP) under certain circumstances. To address this issue, several artificial intelligence (AI)‐based methods have been proposed, but they result in complicated and unreliable methodologies. This study introduces the driving training‐based optimization (DTBO) method, which aims to address the partial shading (PS) problem quickly and reliably in maximum power point (MPP) detection for PV systems. DTBO improves tracking speed and reduces fluctuations in the power output during the tracking period. The proposed method is extensively verified using the Typhoon hardware‐in‐the‐loop (HIL) 402 emulator and compared to conventional methods such as particle swarm optimization (PSO), and JAYA, as well as the recently proposed adaptive JAYA (AJAYA) method for MPPT in a PV system under similar conditions.

Longitudinal and Lateral Control Strategies for Automatic Lane Change to Avoid Collision in Vehicle High-Speed Driving.

The vehicle particle model was built to compare and analyze the effectiveness of three different collision avoidance methods. The results show that during vehicle high-speed emergency collision avoidance, lane change collision avoidance requires a smaller longitudinal distance than braking collision avoidance and is closer to that with a combination of lane change and braking collision avoidance. Based on the above, a double-layer control strategy is proposed to avoid collision when vehicles change lanes at high speed. The quintic polynomial is chosen as the reference path after comparing and analyzing three polynomial reference trajectories. The multiobjective optimized model predictive control is used to track the lateral displacement, and the optimization objective is to minimize the lateral position deviation, yaw rate tracking deviation, and control increment. The lower longitudinal speed tracking control strategy is to control the vehicle drive system and brake system to track the expected speed. Finally, the lane changing conditions and other speed conditions of the vehicle at 120 km/h are verified. The results show that the control strategy can track the longitudinal and lateral trajectories well and achieve effective lane change and collision avoidance.

Model Predictive Current Control of Permanent Magnet Synchronous Motor Based on Sliding Mode Observer With Enhanced Current and Speed Tracking Ability Under Disturbance

For model predictive current control (MPCC) strategy, its poor dynamic response caused by speed loop PI control and poor parameter robustness are the main factors restricting its development. So this paper proposes a model predictive current control based on sliding mode observer with better tracking ability of current and speed under disturbance. First, this paper analyzes the effect of motor parameter mismatches on current predicted values from multiple perspectives. And a sliding mode disturbance observer is designed to compensate lumped disturbances including parameter mismatches and unmodeled dynamic disturbances into prediction model. Then, the speed loop PI controller in model predictive current control is replaced by a deadbeat predictive controller to improve response speed of the speed loop. Meanwhile, the sliding mode load torque observer is constructed to observe the load torque and sends it into the deadbeat predictive controller to improve the anti-load disturbance performance of the control system. Furthermore, a sliding mode reaching law based on sinusoidal saturation function is proposed to suppress chattering of sliding mode observer. The experiments demonstrate that the proposed control strategy not only has faster dynamic response but also has good speed and current tracking performance under lumped disturbance and load disturbance.

Comparative analysis of recent metaheuristic algorithms for maximum power point tracking of solar photovoltaic systems under partial shading conditions

<span lang="EN-US">The photovoltaic (PV) system comprises one or more solar panels, a converter/inverter, controllers, and other mechanical and electrical elements that utilize the generated electrical energy by the PV modules. The PV systems are ranged from small roofs or transportable units to massive electric utility plants. The maximum power point tracking (MPPT) controller has been used in PV systems to get the maximum power available. In addition, the MPPT controller is much essential for PV systems to protect the battery devices or direct loads from the power fluctuations received from solar PV panels. There are several MPPT control mechanisms available right now. The most common and commonly applied approaches under constant irradiance are perturb and observe (P&O) and incremental conductance (INC). But such methods show variations in the maximum power point. In this sense, this paper analyses and utilizes two recent metaheuristic algorithms called artificial rabbit optimization (ARO) and the most valuable player (MVP) algorithm for MPPT applications. The performance comparisons are made with the most preferred traditional algorithms, such as P&O and INC. Based on the result obtained, this study recommends that ARO perform better in standard testing conditions than all the other algorithms, but in partially shaded conditions, the MVP algorithm performs better in terms of efficiency and tracking speed.</span>

Model Predictive Current Control for IPMSM Drives With Extended-State-Observer-Based Sliding Mode Speed Controller

Aiming at the characteristics that the classical proportional-integral (PI) controllers and the model predictive current control (MPCC) is extremely dependent on the motor parameters, and PI controllers are sensitive to the changes of the load torque, a novel extended-state-observer-based sliding mode speed control (ESO-SMSC) scheme and an extended-state-observer-based MPCC (ESO-MPCC) scheme are proposed to improve the speed and current tracking performance of the interior permanent magnet synchronous motor (IPMSM) under the disturbances of the internal parameter mismatch and the external load change. Firstly, the reaching law of the SMSC will be redesigned, and an ESO is designed to estimate the total disturbance to update the control law of the SMSC in real-time. Secondly, the stability of the closed-loop IPMSM drive system with the ESO-SMSC is proved by the Lyapunov theory. Thirdly, in the prediction part of the MPCC, another ESO real-time estimation of total disturbance is designed to compensate for the disturbance, which improves the strong robustness of the MPCC to the disturbance caused by parameter mismatch. Finally, experimental comparisons of the ESO-SMSC scheme, the ESO-MPCC scheme, and the ESO-SMSC+ESO-MPCC scheme are compared experimentally. The analysis and results show that the ESO-SMSC+ESO-MPCC scheme has good speed response and current characteristics, which proves the superiority of the proposed scheme for disturbance rejection.

Suppression of Gyroscopic Torque Disturbance in High Speed Magnetically Levitated Rigid Rotor Systems Based on Extended State Observer

The gyroscopic torque disturbances generated by base motion directly affects the stability of the high speed magnetically levitated centrifugal compressor (MLCC). This article explores a disturbance suppression method for the high-speed magnetically levitated rotor (MLR) system based on improved linear extended state observer (LESO). Firstly, the model of the high-speed MLR system with base motion is established, and the characteristics of the gyroscopic torque disturbance are analyzed. Then, a LESO with adaptive notch filter is designed for disturbance estimation and compensation of the magnetic bearing, and its convergence performance and disturbance tracking speed are deduced. Furthermore, due to the gain attenuation introduced by LESO, an innovative gain compensator is utilized to improve controller dynamic performance. Finally, simulation and experimental results demonstrate that the proposed method can effectively reject the gyroscopic torque disturbances in high speed MLCC.

Partial Freezing for MIMO Adaptive Equalizers: Precise Demodulation in Dynamic Environments

We propose partially frozen (PF) multiple-input multiple-output (MIMO) adaptive equalizers (AEQs). By increasing the degrees of freedom (DOFs) of widely used complex 2×2 MIMO AEQs, complex 4×2 and 8×2 MIMO AEQs can cope with various signal impairments that occur within a transmitter and receiver, such as in-phase/quadrature (IQ) skew and IQ imbalance. Although precise signal demodulation with such MIMO AEQs are useful for enhancing the information rate of a transmission even to reach beyond 1 Tb/s, the increase in internal DOFs ultimately comes with a decrease in the speed of tracking polarization fluctuations and an increase in computational complexity, which are problematic for efficient demodulation in a dynamic environment. To solve this, as the speed of fluctuation in the state of polarization (SOP) of the signal is much faster than those of impairments within the transmitter and receiver, PF MIMO AEQs reduce effective DOFs by separating each DOF corresponding to the SOP, transmitter, and receiver, then freezing unnecessary updates of transmitter and receiver DOFs. In this paper, we describe the principle and show the results of numerical simulations and experiments with 128-Gbaud quadrature phase shift keying (QPSK) signals under IQ impairments. PF MIMO AEQs increased the tracking speed of the 8×2 MIMO AEQ, enabling the tracking of polarization fluctuations of over 11 Mrad/s (faster than fluctuation induced by lightning strikes) with less computational complexity than the conventional configuration. This method is a promising solution for high-speed data transmissions in dynamic environments.

Ocean Surface Gravity Wave Evolution during Three Along-Shelf Propagating Tropical Cyclones: Model’s Performance of Wind-Sea and Swell

Despite recent advancements in ocean–wave observations, how a tropical cyclone’s (TC’s) track, intensity, and translation speed affect the directional wave spectra evolution is poorly understood. Given the scarcity of available wave spectral observations during TCs, there are few studies about the performance of spectral wave models, such as Simulating Waves Nearshore (SWAN), under various TC scenarios. We combined the National Data Buoy Center observations and numerical model hindcasts to determine the linkages between wave spectrum evolution and TC characteristics during hurricanes Matthew 2016, Dorian 2019, and Isaias 2020. Five phases were identified in the wave spectrogram based on the normalized distance to the TC, the sea–swell separation frequency, and the peak wave frequency, indicating how the wave evolution relates to TC characteristics. The wave spectral structure and SWAN model’s performance for wave energy distribution within different phases were identified. The TC intensity and its normalized distance to a buoy were the dominant factors in the energy levels and peak wave frequencies. The TC heading direction and translation speed were more likely to impact the durations of the phases. TC translation speeds also influenced the model’s performance on swell energy. The knowledge gained in this work paves the way for improving model’s performance during severe weather events.

Hybrid gravitational search particle swarm optimization algorithm for GMPPT under partial shading conditions

Solar energy has become one of the popular choices among all renewable energy resources. In order to harvest solar energy, a photovoltaic (PV) system is required. Nowadays, researchers are increasingly paying more attention to PV system since it is affordable and easy to install and maintain. However, unpredictable weather and operating conditions of a PV system may reduce power generation. Therefore, a global maximum power point tracking (GMPPT) controller needs to be installed in the PV system to improve the power generation capability. However, the conventional GMPPT algorithm is less effective because of unsteady oscillations. In this paper, we propose a hybrid method of gravitational search particle swarm optimization (GSPSO) algorithm to track GMPP faster and more efficiently. The proposed algorithm utilizes the exploitation ability of the particle swarm optimization (PSO) algorithm and the exploration ability of the gravitational search algorithm (GSA). Simulation results show that the proposed algorithm has the fastest tracking speed and the highest generated power compared with the other competitive algorithms.

Optimal Tuning of Fractional Order Sliding Mode Controller for PMSM Speed Using Neural Network with Reinforcement Learning

An improved fractional-order sliding mode control (FOSMC) for PMSM is presented in this study to set the unavoidable parameters and to improve permanent magnet synchronous motors (PMSMs) drive performance, such as current and speed tracking accuracy. To determine the optimal parameters of the FOSMC for control speed in a PMSM drive, a neural network algorithm with reinforcement learning (RLNNA) is proposed. The FOSMC parameters are set by the ANN algorithm and then adapted through reinforcement learning to enhance the results. The proposed controller using RLNNA based on fractional-order sliding mode control (RLNNA-FOSMC) can drive the motor speed to achieve the referred value in a finite period of time, leading to faster convergence and improved tracking accuracy. For a fair comparison and evaluation, the proposed RLNNA-FOSMC is compared with conventional FOSMC by applying the integral of time multiplied absolute error as an objective function. The most commonly used objective functions in the literature were also compared, including the integral time multiplied square error, integral square error, and integral absolute error. To validate the performance of the RLNNA-FOSMC speed controller, different scenarios with different speeds steps were carried out. The computational results are promising and demonstrate the effectiveness of the proposed controller. Overall, the proposed RLNNA-FOSMC controller for the PMSM speed control system performed better than conventional FOSMC in numerical simulations.

Single Leg Lateral and Horizontal Loaded Jump Testing: Reliability and Correlation With Long Track Sprint Speed Skating Performance.

Zukowski, MH, Jordan, MJ, and Herzog, W. Single leg lateral and horizontal loaded jump testing: reliability and correlation with long track sprint speed skating performance. J Strength Cond Res 37(11): 2251-2259, 2023-This study examined the intraday reliability of 2 novel unilateral loaded jump protocols designed for long track speed skaters. Highly trained ( n = 26), national level athletes performed single leg jumps with a horizontal robotic resistance across 3 external load conditions (10 N, 7.5% of body mass and 15% of body mass) using their dominant limb. Jumps were performed in both the horizontal (Jump Horz ) and lateral (Jump Lat ) direction to replicate the body position and line of force application observed during the running and gliding phases of on-ice acceleration. Subjects completed 2 consecutive trials of the same jump protocol to examine the intraday reliability of the peak velocity achieved for each loading condition. Peak velocity across each jump type and loading condition had good reliability (intraclass correlation coefficient >0.8, coefficient of variation <5%). Significant positive relationships ( r = 0.5-0.8, p < 0.05; n = 22) were observed between all jump conditions and on-ice sprint race split times obtained including 100, 400, and 500 m. Our results indicate that unilateral loaded jump tests are reliable in speed skating athletes and may help practitioners diagnose and monitor lower-limb maximal muscle power capacity in a sport-specific manner.

Maximum Power Point Tracking of Photovoltaic Module Arrays Based on a Modified Gray Wolf Optimization Algorithm

In this study, a modified gray wolf optimization algorithm (GWOA) was proposed to facilitate the maximum power point tracking (MPPT) of photovoltaic module arrays (PMAs). To increase the voltage conversion ratio and achieve a voltage boost through reduced duty cycles, a high-voltage step-up converter with a coupled inductor was used to replace the conventional energy storage inductor. To achieve global MPPT, the iteration parameters of the proposed GWOA were adjusted according to the slope of the PMA power–voltage (P–V) curve. According to the simulation results, the modified GWOA is more effective in MPPT than the perturbation and observation algorithm and conventional GWOA when multiple peaks appear in the P–V curve of a shaded PMA. In addition, the modified GWOA exhibits an improved tracking speed response and steady-state response.

A Survey of Intelligent Driving Vehicle Trajectory Tracking Based on Vehicle Dynamics

&lt;div&gt;Trajectory tracking control, as one of the core technologies of intelligent driving vehicles, determines the driving performance and safety of intelligent driving vehicles and has received extensive attention and research. In recent years, most of the research results of trajectory tracking control are only applicable to conventional working conditions; however, the actual operating conditions of intelligent driving vehicles are complex and variable, so the research of trajectory tracking control algorithm should be extended to the high-speed low-adhesion coefficient, large curvature, variable curvature, and other compound limit working conditions. This requires more consideration of the vehicle dynamics in the controller design. In this article, a comprehensive review of trajectory tracking control under extreme operating conditions is conducted from three levels: vehicle dynamics model, vehicle speed tracking (longitudinal motion control), and path tracking (transverse motion control), and the existing research results are analyzed and summarized to obtain the research trends and pain points and difficulties in each field. On this basis, the future outlook of trajectory tracking control is proposed, which is expected to provide some help and inspiration to the research workers in this field.&lt;/div&gt;

Convergence map with action-angle variables based on square matrix for nonlinear lattice optimization

To analyze nonlinear dynamic systems, we developed a new technique based on the square matrix method. We propose this technique called the \convergence map" for generating particle stability diagrams similar to the frequency maps widely used in accelerator physics to estimate dynamic aperture. The convergence map provides similar information as the frequency map but in a much shorter computing time. The dynamic equation can be rewritten in terms of action-angle variables provided by the square matrix derived from the accelerator lattice. The convergence map is obtained by solving the exact nonlinear equation iteratively by the perturbation method using Fourier transform and studying convergence. When the iteration is convergent, the solution is expressed as a quasi-periodic analytical function as a highly accurate approximation, and hence the motion is stable. The border of stable motion determines the dynamical aperture. As an example, we applied the new method to the nonlinear optimization of the NSLS-II storage ring and demonstrated a dynamic aperture comparable to or larger than the nominal one obtained by particle tracking. The computation speed of the convergence map is 30 to 300 times faster than the speed of the particle tracking, depending on the size of the ring lattice (number of superperiods). The computation speed ratio is larger for complex lattices with low symmetry, such as particle colliders.

Four-Phase Interleaved Boost Converter for Maximum Power Extraction in PV System

The use of renewable energy is increasing, thus affecting the rapid use of DC-DC converter technology. The use of a conventional boost converter has a weakness, namely high current ripple. This input current ripple will enter the voltage source, so that the switching noise will spread to other circuits. This problem can be overcome by using a multiphase converter topology that can suppress current ripple. But the conventional multiphase topology has large power losses, because the value of the inductor used tends to be large. Ripple currents will also reduce the efficiency of solar power plants. This study aims to design a four-phase interleaved boost converter that is applied to PV (photovoltaic) with maximum power extraction. The topology method of this circuit is to assemble a conventional boost converter in parallel up to 4 levels, so that it can suppress the input current ripple. This topology modification series also uses the interleaved scaling method in order to reduce heat distribution on each switch. The test method in this study uses a standard boost converter circuit as a comparison, and observes its characteristics and performance through simulation. The converter system is also operated as Maximum Power Point Tracking (MPPT) using the PSO and P&amp;O algorithm controls as a comparison. Based on the simulation results, the MPPT tracking speed has the same speed, which is 2 ms, but the speed to achieve ripple stability in the standard circuit and four-phase interleaved boost converter is 4.5 ms and 6.1 ms, respectively.

A Lightweight UAV System: Utilizing IMU Data for Coarse Judgment of Loop Closure

Unmanned aerial vehicles (UAVs) can experience significant performance issues during flight due to heavy CPU load, affecting their flight capabilities, communication, and endurance. To address this issue, this paper presents a lightweight stereo-inertial state estimator for addressing the heavy CPU load issue of ORB-SLAM. It utilizes nonlinear optimization and features to incorporate inertial information throughout the Simultaneous Localization and Mapping (SLAM) pipeline. The first key innovation is a coarse-to-fine optimization method that targets the enhancement of tracking speed by efficiently addressing bias and noise in the IMU parameters. A novel visual–inertial pose graph is proposed as an observer to assess error thresholds and guide the system towards visual-only or visual–inertial maximum a posteriori (MAP) estimation accordingly. Furthermore, this paper introduces the incorporation of inertial data in the loop closure thread. The IMU data provide displacement direction relative to world coordinates, which is serving as a necessary condition for loop detection. The experimental results demonstrate that our method maintains excellent localization accuracy compared to other state-of-the-art approaches on benchmark datasets, while also significantly reducing CPU load.

Design and performance analysis of hybrid MPPT controllers for fuel cell fed DC-DC converter systems

Fuel cell-based power generation is the most utilized renewable energy source in the automotive industry because of its features clean energy, and less environmental pollution. The fuel cell output power is mainly depending on the operating temperature of the fuel cell. The fuel cell gives nonlinear voltage versus current characteristics. As a result, the extraction of maximum power from the fuel stack is very difficult. In order to extract the peak power from the fuel cell, a Maximum Power Point Tracking (MPPT) controller is used at various working temperature conditions of the fuel cell. The main contribution of this study is the introduction and comparative performance analysis of different hybrid MPPT controllers for selecting the optimum duty cycle for the fuel cell-fed boost converter system. The studied MPPT controllers are Adaptive Adjustable Step-based Perturb and Observe (AAS-P&O) controllers, Variable Step Value-Radial Basis Function Controller (VSV-RBFC), Adaptive Step Hill Climb (ASHC) based fuzzy technique, Variable P&O with Particle Swarm Optimization (VP&O-PSO), and Variable Step Grey Wolf Algorithm (VSGWA) based fuzzy logic controller. These hybrid MPPT controllers’ comparative performance analysis has been done in terms of tracking speed of MPP, oscillations across MPP, design complexity of controller, ability to handle fast changes of temperature values, and accuracy of MPP tracking. From the simulative performance results, it is identified that the VSGWA-based fuzzy controller gives superior performance when compared to the other controllers.

Real-time implementation of deep reinforcement learning controller for speed tracking of robotic fish through data-assisted modeling

This article proposes real-time speed tracking of two-link surface swimming robotic fish using a deep reinforcement learning (DRL) controller. Hydrodynamic modelling of robotic fish is done by virtue of Newtonian dynamics and Lighthill’s kinematic model. However, this includes external unsteady reactive forces that cannot be modeled accurately due to the distributed nature of hydrodynamic behavior. Therefore, a novel data-assisted dynamic model and control method is proposed for the speed tracking of robotic fish. Initially, the cruise speed motion data are collected through experiments. The water-resistance coefficient is estimated using the least mean square fit, which is then adopted in the model. Subsequently, a closed-loop discrete-time DRL controller trained through a soft actor-critic (SAC) agent is implemented through simulations. SAC overcomes the brittleness problem encountered by other policy gradient approaches by encouraging the policy network for maximum exploration and not assigning a higher probability to any single part of actions. Due to this robustness in the policy learning, the convergence error becomes low in RL-SAC than RL-DDPG controller. The simulation results verify that the DRL-SAC control with data-assisted modelling substantially improves the speed tracking performance. Further, this controller is validated in real-time, and it is observed that the SAC-trained controller tracks the desired speed more accurately than the DDPG controller.