Speed Tracking Research Articles

A Novel Hybrid Approach for Global Maximum Power Point Tracking in Standalone PV Systems

The efficiency of Photo-Voltaic (PV) systems is highly dependent on their ability to accurately track the Global Maximum Power Point (GMPP) under varying environmental conditions. Traditional Maximum Power Point Tracking (MPPT) methods often struggle with issues such as slow tracking speed, susceptibility to local maxima, and the need for complex parameter tuning, particularly in dynamically changing environments with Partial Shading Conditions (PSCs) and rapid irradiation changes. To address these challenges, this study introduces a hybrid approach that combines a modified Rao algorithm with the Perturb and Observe (P&O) method. The modified Rao algorithm was employed in the initial tracking stages to quickly locate the global vicinity, benefiting from its simplicity and the absence of algorithm-specific parameters, whereas the P&O method ensured precise tracking in the final stages. The performance of the proposed method was assessed on a PV array subjected to PSCs and compared with several well-known MPPT algorithms, such as Gray Wolf Optimization (GWO), JayaDE, and the Slime Mould Algorithm (SMO). The proposed approach was implemented and analyzed using the MATLAB/Simulink software.

One-Stage Anchor-Free Online Multiple Target Tracking With Deformable Local Attention and Task-Aware Prediction.

The tracking-by-detection paradigm currently dominates multiple target tracking algorithms. It usually includes three tasks: target detection, appearance feature embedding, and data association. Carrying out these three tasks successively usually leads to lower tracking efficiency. In this paper, we propose a one-stage anchor-free multiple task learning framework which carries out target detection and appearance feature embedding in parallel to substantially increase the tracking speed. This framework simultaneously predicts a target detection and produces a feature embedding for each location, by sharing a pyramid of feature maps. We propose a deformable local attention module which utilizes the correlations between features at different locations within a target to obtain more discriminative features. We further propose a task-aware prediction module which utilizes deformable convolutions to select the most suitable locations for the different tasks. At the selected locations, classification of samples into foreground or background, appearance feature embedding, and target box regression are carried out. Two effective training strategies, regression range overlapping and sample reweighting, are proposed to reduce missed detections in dense scenes. Ambiguous samples whose identities are difficult to determine are effectively dealt with to obtain more accurate feature embedding of target appearance. An appearance-enhanced non-maximum suppression is proposed to reduce over-suppression of true targets in crowded scenes. Based on the one-stage anchor-free network with the deformable local attention module and the task-aware prediction module, we implement a new online multiple target tracker. Experimental results show that our tracker achieves a very fast speed while maintaining a high tracking accuracy.

Adaptive sliding mode-based feedback linearization control for floating offshore wind turbine in region II

ABSTRACT Wind turbine systems are highly nonlinear and time-variable. Under external interference, the normal and stable operation of the system is seriously affected. In addition, the inertia of the wind turbine causes a serious lag in speed tracking. These effects are even more severe for floating offshore wind turbines (FOWT). To solve these problems, this paper proposes a new optimal torque control form, and further improves and optimizes it. Firstly, the feedback linearization is used to eliminate the nonlinear part of the system and the time-varying parameters, and a new torque control form is obtained. Then, the adaptive sliding mode is used to further optimize the torque controller to enhance the robustness of the system. Finally, adaptive sliding mode-based feedback linearized optimal torque control (ASMFLOTC) was obtained. ASMFLOTC was applied to the FOWT system to verify the effectiveness of its maximum power point tracking (MPPT) control. The results show that ASMFLOTC can better track the reference speed, effectively reduce the relative error, and improve the utilization rate of wind energy. And from the results, the platform motion of the proposed controller is not significantly different from that of other controllers. The proposed controller does not exacerbate the platform motion while increasing the output power. This shows its feasibility.

Optimal parameter identification of adaptive fuzzy logic MPPT based-bald eagle search optimization algorithm to boost the performance of PEM fuel cell

The output power of proton exchange membrane (PEM) fuel cell is dependent on operating temperature and the membrane water content. Changing the operation condition changes the location maximum power point (MPP) on the nonlinear curve of power versus the current. Therefore, a robust MPP tracking strategy is obligatory to grantee the work of PEM fuel cell around or close the MPP and boost the harvested energy. The suggested tracker is based on the bald eagle search (BES) optimization algorithm to identify the best values to entirely advantage the inherent flexibility of fuzzy logic control (FLC) system furthermore to accomplish rapid and precise tracking. Throughout the optimization procedure, the gains of membership functions of FLC are assigned to be decision variables, while the integral of error is the objective function. To approve the advantage of BES, a comparison with gradient-based optimizer (GBO), osprey optimization algorithm (OOA), particle swarm optimization (PSO), pelican optimization algorithm (POA), blue monkey algorithm (BMA) and henry gas solubility optimization (HGS) is performed. The BES has the highest performance compared with other optimizers; it achieves the best value of 1.7638 flowed by GBO (1.7667) whereas the worst value of 1.7987 is obtained by POA. Additionally, the lowest STD of 0.0046 is achieved by BES followed by GBO (0.0078) whereas the worst value of STD of 0.0243 is obtained by HGS. Finally, the optimized FLC-MPPT-based BES is compared with commonly MPPTs including classical FLC and perturb & observe in terms of dynamic and steady state conditions. The comparison demonstrated that the suggested FLC-MPPT-based BES has fast tracking speed and diminishes the oscillations around MPP in steady state.

A hybrid active control method for a 3-DOF wave-compensated gangway

ABSTRACT Wave-compensated gangways are affected by forces from internal joint coupling and vessel motion, making the control process complex and challenging. Previously, three-degree-of-freedom gangways adopted passive compensation methods, utilizing end-effector sensor feedback for control. However, these methods are constrained by slow response times and significant lag. This study proposes an active hybrid compensation approach to enhance response speed and reduce tracking errors. The kinematics and dynamics of the gangway under vessel motion were analyzed. Subsequently, an improved velocity feedforward second-order super-twisting sliding mode control scheme was designed, complemented by fuzzy control for small torque compensation. Two experiments were conducted to evaluate and compare the performance of three control methods. Experiments demonstrated that the proposed hybrid control method exhibited superior performance in joint trajectory tracking, significantly reducing both root mean square and maximum errors, validating its effectiveness in improving tracking speed and accuracy.

A switched model predictive control with parametric weights-based mode transition strategy for a novel parallel hybrid electric vehicle

In a novel parallel hybrid electric vehicle (HEV) configuration, the transition from pure electric mode to hybrid mode encompasses critical operations such as engine startup, coordinated control of motor and engine torque, and engagement of the clutch. Addressing the intricate challenges associated with enhancing speed tracking performance during and after mode transition, mitigating jerk during mode transition, and minimizing mode transition time, this paper conducts a meticulous analysis of the vehicle configuration and mode transition process. The mode transition process is systematically delineated into four stages, with each stage characterized by the establishment of dynamic models. Subsequently, a mode transition strategy is proposed, leveraging switched model predictive control with parametric weights (SMPC-PW). This controller framework includes the design of two model predictive controllers (MPC) tailored for two pivotal stages, the formulation of a parametric weights pattern based on pre-transition acceleration, and the development of a stage switching strategy to ensure seamless switches between controllers. The efficacy of the proposed strategy is validated through co-simulations in the Simulink and GT-Power environment. The fine-tuning of MPC parameters is grounded in multiple sets of prediction horizons and sampling time simulation results. In comparison to strategies based on MPC and PID under various acceleration scenarios, the SMPC-PW strategy consistently maintains acceleration control below 10 m/s3. It not only achieves superior speed tracking during and after mode transition but also reduces mode switch time by 0.1 s-0.3 s. These compelling results unequivocally demonstrate that the proposed mode transition strategy significantly elevates the quality of mode transition for this specific parallel HEV configuration.

Improved sliding mode disturbance observer-based model-free finite-time terminal sliding mode control for IPMSM speed ripple minimization

The model-free sliding mode control with sliding mode disturbance observer (SMDO) for interior permanent magnet synchronous motor (IPMSM) is affected by feedback delays caused by mismatch of motor parameters. The observer is lagged behind the change of external disturbances, the speed tracking accuracy and transient control performance for IPMSM drives are reduced. In order to solve the issues, an improved higher-order sliding mode disturbance observer-based model-free finite-time terminal sliding mode control (HOSMDO-MFFTTSMC) strategy is proposed in this paper. First, a finite-time terminal sliding mode surface (FTTSMS) is designed, and a rotation speed-loop-based MFFTTSMC strategy is designed by combining the ultra-local model. The system control state is converged in finite time and the accurate tracking of observation error is realized. In addition, the non-singular fast terminal sliding mode is introduced into the observer, the higher-order SMDO is designed. The unknown part of disturbances is observed and compensated in real time, the fast-tracking response capability and anti-disturbance capability for IPMSM are improved, and the stator current harmonics are effectively suppressed. Finally, the proposed HOSMDO-MFFTTSMC strategy is experimentally demonstrated with a 6.6kW motor. The correctness and effectiveness of the HOSMDO-MFFTTSMC strategy are verified by simulation and experimental results.

Vehicle Motion Control for Overactuated Vehicles to Enhance Controllability and Path Tracking

The motion control of vehicles poses distinct challenges for both vehicle stability and path tracking, especially under critical environmental and driving conditions. Overactuated vehicles can effectively utilize the available tyre–road friction potential by leveraging additional actuators, thus enhancing their stability and controllability even in challenging scenarios. This paper introduces a novel modular upstream control architecture for overactuated vehicles, integrating a fast and robust linear time-varying model predictive path and speed tracking controller with a model following approach and nonlinear control allocation to form a holistic vehicle motion controller. The architecture decouples the path and speed tracking task from the actuator allocation, where torque vectoring and rear-wheel steering are applied to achieve linear understeer reference vehicle behavior. It allows for the use of a simpler path tracking controller, enabling long preview horizons and enhanced computational efficiency. Nonlinearities, such as the mutual influence of lateral and longitudinal tyre forces, are accounted for within the control allocation. The simulation results demonstrate that the proposed control architecture and overactuation improve vehicle stability in critical driving conditions and reduce path tracking errors compared to a dual-motor vehicle.

Hierarchical Distributed Model‐Free Adaptive Fault‐Tolerant Vehicular Platooning Control

ABSTRACTIn this article, a hierarchical distributed model‐free adaptive fault‐tolerant vehicular platooning control scheme for nonlinear vehicular platooning systems (VPSs) is studied. First of all, the dynamic linearization technique (DLT) is utilized to acquire an equivalent linear data model for nonlinear VPSs. Secondly, a hierarchical control structure is developed to realize the vehicular platooning tracking control task, which consists of the upper‐layer adaptive distributed observer and the under‐layer decentralized model‐free adaptive vehicular platooning tracking controller. In order to make each follower vehicle get the leader's information, the adaptive distributed observer is employed to get the estimation value of leader's information. In addition, for the purpose of ensuring the safety of driving, the radial basis function neural network (RBFNN) algorithm is utilized to address the problem of sensor failures. Based on this, a novel hierarchical distributed model‐free adaptive fault‐tolerant vehicular platooning control scheme is designed to achieve simultaneous tracking of vehicular position and speed. Lastly, the validity of the theoretical control scheme is demonstrated through a realistic and detailed simulation example of a VPS.

Polarization division multiplexing link using a high-speed lithium niobate automatic polarization demultiplexer.

We propose and experimentally demonstrate a polarization division multiplexing (PDM) link employing an integrated dual-polarization thin-film lithium niobate (TFLN) modulator and an on-chip automatic polarization demultiplexer. The tracking speed of an 80-Gb/s PDM link achieves a maximum of 600 rad/s with a bit error rate (BER) below 2.4 × 10-2. Furthermore, at a polarization scrambling rate (SR) of 100 rad/s, BERs maintain around 1.7 × 10-3 over a period of half an hour. This configuration takes advantage of the TFLN platform, which includes high-speed modulation, fast polarization control, and a compact footprint, effectively doubling capacity per wavelength using simple direct detection. The demonstration highlights the effectiveness of on-chip polarization multiplexing and demultiplexing, particularly in scenarios involving polarization fluctuations, offering promising prospects for applications in commercial short-reach data centers.

The sliding mode controller with composite reaching law for upper limb rehabilitation robot

Purpose Assisted training using upper limb rehabilitation robots is beneficial for flaccid paralysis patients in recovering their functional abilities. In the assisted training mode, the patient’s motor ability is limited by factors such as limb muscle tension, and it is prone for the rehabilitation robot to deviate from the prescribed training trajectory. A sliding mode control method based on a fixed time observer is proposed to address the problem of delayed trajectory tracking response of upper limb rehabilitation robots caused by external disturbances such as patient limbs. Design/methodology/approach First, aiming at the problem of estimating and compensating for external disturbances in the upper limb rehabilitation robot system, a fixed time observer was designed based on the robot’s dynamic model. Second, the composite sliding mode reaching law combining the smooth function and the power-exponential function is proposed to shorten the convergence time of system states in the startup phase, thereby reducing chattering in the control process and realizing the real-time tracking of the training trajectory by the control system. Findings The proposed method provides a solution for the trajectory tracking speed of upper limb rehabilitation robot controllers. In the circular trajectory tracking control, compared to the sliding-mode control method combined with the variable-exponential composite reaching law based on the fixed-time observer, the method in this paper reduces the time for the system state to reach the sliding surface by 0.89 s and improves the response speed by 0.66%. Originality/value The composite sliding mode approach law based on smooth function and power exponent function can reduce the time it takes for the system state to reach and remain on the sliding surface and improve the trajectory tracking speed of upper limb rehabilitation robots. This controller improves the accuracy of trajectory control and ensures the robustness of auxiliary rehabilitation training.

Enhancing Electric Vehicle Propulsion: PMSM Motor Speed Control via a Fuzzy PI Controller Developed Using a Line-Integral Lyapunov Fuzzy Function

This paper suggests a robust control strategy to enhance the stability and ensure optimal tracking of reference speed for electric vehicles (EVs) equipped with Permanent Magnet Synchronous Motors (PMSM). The strategy involves designing a specific Proportional-Integral (PI) controller using the Takagi-Sugeno (TS) fuzzy model and employing the Linear Integral Lyapunov Function (LILF). Additionally, an H∞ approach is integrated to effectively manage external disturbances such as load torque. The PI controller's gains are determined using the Linear Matrix Inequality (LMI) tool, ensuring efficient and reliable performance under varying operating conditions. Simulation studies confirm the effectiveness of the proposed control strategy and highlighting its capability to achieve robust speed regulation and disturbance rejection in EV propulsion systems

A Novel Nonlinear Adaptive Control Method for Longitudinal Speed Control for Four-Independent-Wheel Autonomous Vehicles

As autonomous driving technology and four-independent-wheel chassis systems advance, four-independent-wheel autonomous vehicles have increasingly become a focal area of modern research. The longitudinal control problem for four-independent-wheel autonomous vehicles presents challenges such as complex models, high nonlinearity, and strong system uncertainties. This paper proposes a novel hierarchical control algorithm to address these challenges, innovatively combining the advantages of adaptive backstepping and dynamic sliding mode control algorithms in the upper controller, allowing it to effectively overcome the impact of uncertain system parameters and suppress the common chattering phenomenon in the output of typical sliding mode control methods. Based on the design of the upper controller, an innovative optimized longitudinal force distribution strategy and the construction of a tire reverse longitudinal slip model are proposed, followed by the design of a fuzzy PID controller as the lower slip ratio controller to achieve precise whole-vehicle longitudinal speed tracking and improve overall control performance. This method not only improves the accuracy of speed tracking but also enhances the robustness and adaptability of the control system. Finally, the effectiveness and superiority of the proposed hierarchical control method are verified through CarSim simulations.

Intelligent Vehicle Trajectory Tracking Based on Horizontal and Vertical Integrated Control

To address the common issues of accuracy and stability in trajectory tracking tasks for autonomous vehicles, this study proposes an innovative composite control strategy that skillfully integrates lateral and longitudinal dynamic control. For lateral control, model predictive control (MPC) theory is introduced to compute the front wheel steering angle that ensures optimal trajectory following. On the longitudinal control level, the vehicle’s acceleration and deceleration logic are finely tuned to ensure precise adherence to the preset speed trajectory. More importantly, by deeply integrating these two control methods, the comprehensive coordination of the vehicle’s lateral and longitudinal movements is achieved. To validate the effectiveness of the proposed control strategy, simulations were conducted using the CarSim and MATLAB/Simulink platforms. The analysis of the simulation results confirms that the proposed method effectively improves speed tracking stability and significantly enhances path tracking accuracy and overall driving stability.

CTIFTrack: Continuous Temporal Information Fusion for object track

In visual tracking tasks, researchers usually focus on increasing the complexity of the model or only discretely focusing on the changes in the object itself to achieve accurate recognition and tracking of the moving object. However, they often overlook the significant contribution of video-level linear temporal information fusion and continuous spatiotemporal mapping to tracking tasks. This oversight may lead to poor tracking performance or insufficient real-time ability of the model in complex scenes. Therefore, this paper proposes a real-time tracker, namely Continuous Temporal Information Fusion Tracker (CTIFTrack). The key of CTIFTrack lies in its well-designed Temporal Information Fusion (TIF) module, which cleverly performs a linear fusion of the temporal information between the (t-1)-th and the t-th frames and completes the spatiotemporal mapping. This enables the tracker to better understand the overall spatiotemporal information and contextual spatiotemporal correlations within the video, thereby having a positive impact on the tracking task. In addition, this paper also proposes the Object Template Feature Refinement (OTFR) module, which effectively captures the global information and local details of the object, and further improves the tracker’s understanding of the object features. Extensive experiments are conducted on seven benchmarks, such as LaSOT, GOT-10K, UAV123, NFS, TrackingNet, VOT2018 and OTB-100. The experimental results validate the significant contribution of the TIF module and OTFR module to the tracking task, as well as the effectiveness of CTIFTrack. It is worth noting that while maintaining excellent tracking performance, CTIFTrack also shows outstanding real-time tracking speed. On the Nvidia Tesla T4-16GB GPU, the FPS of CTIFTrack reaches 71.98. The code and demo materials will be available at https://github.com/vpsg-research/CTIFTrack.

A Maximum Power Point Tracking (MPPT) Strategy Based on Harris Hawk Optimization (HHO) Algorithm

To address the multi-peak characteristics of the P-U output characteristic curve when the photovoltaic array is partially shaded, a Maximum Power Point Tracking (MPPT) method combining Harris Hawk optimization, and a variable step conductance increment method is proposed in this article. Although the Harris Hawk Optimization algorithm has advantages in optimizing multi-modal P-U curves, when the lighting conditions suddenly change, the output characteristic curve will undergo drastic changes, causing the algorithm to oscillate repeatedly at the maximum power point. Moreover, due to its inherent limitations, the Harris Hawk algorithm has a low convergence accuracy, slow tracking speed, and is prone to getting trapped in a local optimum. In order to compensate for the shortcomings of the Harris Hawk Optimization algorithm, the variable step conductance increment method is switched for local exploration to improve the algorithm’s ability to escape from the local optimal solution and reduce power oscillation. Finally, the MPPT control of the proposed strategy was simulated and verified on the MATLAB/Simulink simulation (2021) platform. In the MPPT test experiment, the proposed composite algorithm was applied to simulate and verify uneven irradiance conditions.

Fuzzy-PID controller design for RGV speed track based on improved PSO algorithm

Fuzzy-PID controller design for RGV speed track based on improved PSO algorithm

Design of a Robust Controller for Induction Motor Drive Systems Based on Extendable Fuzzy Theory

In this paper, an extendable fuzzy robust speed controller suitable for induction motor drive systems was proposed. Firstly, the two-degrees-of-freedom (2DOF) robust control technology with feedforward control and disturbance elimination method was adopted. Upon parameter variation and load disturbance, the motor drive system could utilize a robust controller to generate compensation signals and reduce the impact on the controlling performance of the motor drive system. The magnitude of the compensation signal was adjusted via the weighting factor. However, should a fixed weighting factor be adopted, system instability might be generated easily when time delay and saturation of control force occur. Based on the above, the smart method of extendable fuzzy theory (EFT) was adopted in this paper to adjust adequate weighting factors, where the controlling performance of the induction motor drive system could be improved accordingly. Lastly, the simulation software Matlab/Simulink (R2023b version) was applied to simulate the utilization of the controlling method proposed for the induction motor drive system. The simulation results proved that the extendable fuzzy robust speed controller proposed provided better speed tracking and load regulation-controlling performance than the conventional robust controller.

Track trajectory, molten pool and defect characterization in NiTi single-track selective laser melting (SLM) experiments

In order to explore the law of selective laser melting (SLM) forming of Nitinol alloy, and obtain high quality additive manufacturing parameters of Nitinol alloy, a series of single-track SLM experiments on Nitinol alloy were conducted under different combined parameters of laser power and scanning speed. The effects of laser power, scanning speed and linear energy density on track forming, molten pool evolution and pore formation were systematically discussed by characterizing the track trajectory, molten pool morphology, dimensions and defects of the molten pool, and the ideal process parameters were obtained. The experimental results revealed that the average track width and width uniformity decreased with the increase of the scanning speed, the spheroidization phenomenon and height fluctuation became more pronounced, and the phenomena of necking, subsection and interruption increased significantly at the fixed laser power. Under the fixed scanning speed, the width, height and width uniformity of the molten track gradually increased with the increase of the laser power, and the phenomena of spheroidization, subsection and interruption gradually reduced. Moreover, the upper zone of the molten pool is shaped like an inverted bowl, and the lower zone is mainly divided into five categories, which are lack of fusion, shallow bowl shape, deep bowl shape, keyhole shape and deeper keyhole shape under different process parameters. The defects of the molten pool mainly include lack of fusion and trapped gas. An ideal molten pool without defects can be obtained under the parameter conditions of 100–150W laser power and 100–200 mm/s spanning speed.

Particle Swarm Optimization Algorithm Based Fuzzy PID Controller Design for Speed Tracking Control of Separately Excited DC Motor

Particle Swarm Optimization Algorithm Based Fuzzy <scp>PID</scp> Controller Design for Speed Tracking Control of Separately Excited <scp>DC</scp> Motor