Angle Tracking Research Articles

A Successive Approximate Angle Track Converter for Sinusoidal Encoders

ABSTRACTThis paper presents a novel angle track converter for sinusoidal encoders. These encoders provide electronic signals in which the position is encoded in sinusoidal form. This work aims to reduce the complexity and enhance the conversion speed of the sinusoidal‐to‐angle demodulation system. Considering the periodicity of sine and cosine signals (SCSs), the full range is evenly divided into eight segments to determine the coarse angle. According to the specific coarse angle segment, the SCSs are reshaped for proceeding demodulation. Then a fine angle is derived from reshaped signals based on division and arctangent algorithm. Division operation is achieved by a capacitor digital‐to‐analog converter, dynamic comparator, and successive approximation register. The arctangent value is obtained from the division result with the compensation by a small‐size look‐up table. The new converter fabricated in 0.18‐m BCD technology occupies an active area of 0.16 mm2 and consumes 1.5 mW at the sample rate 10 MHz. The measured maximum error is 1.8°, in the full range. Without analog‐to‐digital converters and no nonlinear operation unit, the proposed angle track converter has a significant advantage in cost, speed, and efficiency.

Adaptive Path-Tracking Control Algorithm for Autonomous Mobility Based on Recursive Least Squares with External Condition and Covariance Self-Tuning

This paper introduces an adaptive path-tracking control algorithm for autonomous mobility based on recursive least squares (RLS) with external conditions and covariance self-tuning. The advancement and commercialization of autonomous driving necessitate universal path-tracking control technologies. In this study, we propose a path-tracking control algorithm that does not rely on vehicle parameters and leverages RLS with self-tuning mechanisms for external conditions and covariance. We designed an integrated error for effective path tracking that combines the lateral preview distance and yaw angle errors. The controller design employs a first-order derivative error dynamics model with the coefficients of the error dynamics estimated through the RLS using a forgetting factor. To ensure stability, we applied the Lyapunov direct method with injection terms and finite convergence conditions. Each regression process incorporates external conditions, and the self-tuning of the injection terms utilizes residuals. The performance of the proposed control algorithm was evaluated using MATLAB®/Simulink® and CarMaker under various path-tracking scenarios. The evaluation demonstrated that the algorithm effectively controlled the front steering angle for autonomous path tracking without vehicle-specific parameters. This controller is expected to provide a versatile and robust path-tracking solution in diverse autonomous driving applications.

Design and Performance Characteristics of Single-Axis Tracking Dual Confocal Low-Magnification Parabolic Trough CPV system

With the rapid increase of PV module utilization, the environmental pollution associated with waste photovoltaic (PV) module and its recycling is of concern. This paper proposes a concentrating photovoltaic (CPV) system to reduce the use of PV module. The cross-confocal method is employed for the concentrator to eliminate central dark streaks. In optical simulation, a theoretical uniformity index of 0.0466 and a theoretical optical efficiency of 89.87% were achieved. When the distance between the rotational axis and the center of gravity is less than 125.8mm, the system can withstand winds of 20.4m/s. The temperature range of the PV, obtained from finite element analysis, is from 295.71 K to 363.13 K. With the requirements of the relative optical efficiency of over 90% and the uniformity index of less 0.5, the system has a dimensional translation tolerance of approximately ±10mm, a dimensional rotation tolerance of about 5°, and a tracking angle tolerance of about 1°. The actual daily average output current of this system has increased by 100.91%. The ratio between the increase ratio of output current and the theoretical concentration ratio is 83.4%. The system demonstrates excellent optical performance and system tolerance.

Coordinated strategy for path tracking and dynamic stability considering extreme obstacle avoidance in distributed drive electric vehicles

PurposeThis research aims to investigate the trajectory tracking problem for a four-wheel independent drive autonomous vehicle (4WID) and propose an integrated, coordinated control strategy to address the mutual interference between trajectory tracking and stability control in extreme cases.Design/methodology/approachThe authors establish an adaptive preview model that modifies the preview distance based on vehicle speed. They utilize a three-degrees-of-freedom vehicle model and employ model predictive control to calculate the necessary front wheel angle for trajectory tracking. In terms of longitudinal control, a longitudinal coordinated control mechanism is established to achieve the two conflicting objectives of trajectory tracking accuracy and dynamic stability through early deceleration. A stability controller based on sliding mode control (SMC) is designed, considering tire constraints and tracking the optimal yaw angle and sideslip angle. Furthermore, a lateral coordinated control strategy is developed, considering the weight coefficient of stability control, and the yaw moment is calculated and distributed based on the vehicle torque requirements.FindingsThe proposed integrated, coordinated control strategy successfully addresses the mutual interference between trajectory tracking and stability control in extreme cases for the 4WID vehicle. The strategy achieves trajectory tracking accuracy, dynamic stability and reduced energy consumption while taking into account tire constraints.Originality/valueWe have proposed a cooperative control strategy for the trajectory tracking problem of autonomous driving vehicles. This strategy is different from previous methods in that we have taken into account the integrated dynamic control in both longitudinal and lateral directions, balancing the conflicting control requirements and reducing energy consumption, improving trajectory tracking accuracy and vehicle dynamic stability. We have verified the feasibility of this strategy through joint simulation under different driving conditions.

A Model Predictive Backstepping Control Approach for Angle Tracking of Steer-by-Wire System

A Model Predictive Backstepping Control Approach for Angle Tracking of Steer-by-Wire System

Online Safe Flight Control Method Based on Constraint Reinforcement Learning

UAVs are increasingly prominent in the competition for space due to their multiple characteristics, such as strong maneuverability, long flight distance, and high survivability. A new online safe flight control method based on constrained reinforcement learning is proposed for the intelligent safety control of UAVs. This method adopts constrained policy optimization as the main reinforcement learning framework and develops a constrained policy optimization algorithm with extra safety budget, which introduces Lyapunov stability requirements and limits rudder deflection loss to ensure flight safety and improves the robustness of the controller. By efficiently interacting with the constructed simulation environment, a control law model for UAVs is trained. Subsequently, a condition-triggered meta-learning online learning method is used to adjust the control raw online ensuring successful attitude angle tracking. Simulation experimental results show that using online control laws to perform aircraft attitude angle control tasks has an overall score of 100 points. After introducing online learning, the adaptability of attitude control to comprehensive errors such as aerodynamic parameters and wind improved by 21% compared to offline learning. The control law can be learned online to adjust the control policy of UAVs, ensuring their safety and stability during flight.

Defeating angle tracking: A comprehensive analysis of scan rate modulation jamming against sequential lobing radars

AbstractThe authors analyse the sequential lobing radar (SLR) resistance against the scan rate modulation (SRM) jamming technique. Unlike previous studies, the authors analyse SLRs with sinc antenna patterns, enabling investigation of the sidelobe region's vulnerability. By incorporating second‐order angular tracking loops (SOATLs), the first analysis of break‐lock probability for SLRs under SRM jamming is introduced. The analysis reveals the influence of SOATL parameters (damping ratio and natural frequency) on both angular root‐mean‐square‐error at low jamming‐to‐signal‐ratio (JSR) region and break‐lock probability at high JSR region. Additionally, the impact of squint angle on SLR resistance to SRM jamming is examined. Furthermore, the optimal duty cycle as a function of JSR, which maximises break‐lock probability is presented. Significantly, the theoretical results support the 20 dB JSR requirement for SRM effectiveness, often cited experimentally in literature, and extend the theoretical analysis of the SRM technique carried out for the main lobe region of SLRs. This finding bridges the gap between theoretical analysis and practical observations.

Evaluating the effectiveness of safety countermeasures at highway–railway grade crossing based on a machine learning framework

Objective This research aims to cluster similar highway–railway grade crossings (HRGCs) to examine the safety countermeasures at HRGCs. Methods The methodology integrates inventory and collision data from Federal Railroad Association (FRA) data set during years 2010 to 2022 . The XGBoost and random forest (RF) algorithms are employed to identify influential collision severity factors. Then, the deep latent class analysis (DLCA) method is utilized on selected inventory factors as important features to cluster similar HRGCs. Afterward, collision modification factor (CMF) and standard error (SE) measures are computed for each countermeasure through collisions within each HRGC cluster. Results XGBoost successfully identified 20 important collision and inventory factors with importance levels exceeding 94%, such as the number of daily trains and the surface material. Then, the DCLA method achieved 4 distinct clusters optimized by high similarity within each cluster and significant independence among clusters. The effectiveness of countermeasures was computed in terms of CMF and SE. The CMF results demonstrated that bells achieved superior safety compared to other countermeasures in clusters with sharper track angles and high maximum train speeds. Implementing bells decreased collisions across Clusters 1 and 4, with reductions of 53% (CMF = 0.47) and 46% (CMF = 0.54), respectively. Conclusions The results highlight XGBoost’s capability to identify important collision and inventory factors, successfully uncovering 20 of the most important factors. The DCLA clustering method forms 4 distinct groups marked by substantial internal similarity within each cluster. This approach contributes to a clearer understanding of how each countermeasure impacts collision frequency. The findings highlight the varying effectiveness of different countermeasures across clusters, improving decision making for safety at HRGCs. The study highlights the efficacy of crossbucks in addressing safety concerns during moderate traffic conditions, particularly evident in environments with a highway speed limit between 100 and 125 mph. Additionally, bells demonstrate notable effectiveness in areas with sharper track angles.

Controller design and remote piloting training module development for unmanned cropped delta reflex wing configuration

In this study, a cost-effective high-fidelity six-degree of freedom module augmented with an onboard controller is developed. The unmanned aerial vehicle (UAV) under consideration is a wing-alone configuration with a cropped delta planform and reflex airfoil cross-section designed to perform manoeuvres at high angles of attack. The six-degree of freedom dynamics for the aforementioned configuration is developed based on Newtonian mechanics incorporating linear and nonlinear aerodynamic models to expand flight simulations in corresponding regimes. Aerodynamic stability and control parameters for the simulations are obtained from full-scale wind tunnel measurements and flight tests. It is observed that the linear aerodynamic model is valid in angle of attack (α) domain −5°≤α≤10°, followed by nonlinearity up to α≈21°, post which lift coefficient remains almost constant with angle of attack till 45°. The simulation model is validated with the corresponding flight data obtained from flight tests. A user-friendly graphical interface with real-time animation of UAV has been developed for the aforementioned configuration by integrating MATLAB and Flightgear environment with real-time control input from the hardware to enable the pilot for visual and tactile feedback, respectively. A controller based on total energy control is designed for velocity and altitude hold modes, while an attitude hold controller is designed for pitch and roll angle tracking to enable controller-in-the-loop remote piloting simulations. In order to enhance practical applicability, the designed module has been tested under various simulated wind conditions. It is noticed from the results that the steady-state error in attitude remains below 2% with a maximum overshoot of 5° from reference. The total energy controller achieves a zero steady-state error with a maximum overshoot of 2 m/s for velocity and 6 m for altitude, respectively. The developed module enhances remote piloting training, flight response assessment, and control algorithm testing for cropped delta planform UAVs.

A Design of Model Predictive Control and Nonlinear Disturbance Observer-based Backstepping Sliding Mode Control for Terrain Following

In this study, we propose the terrain following algorithm using model predictive control and nonlinear disturbance observer-based backstepping sliding mode controller for an aircraft system. Terrain following is important for military missions because it helps the aircraft avoid detection by the enemy radar. The model predictive control is used to replace the generating trajectory and guidance with the flight path angle constraint. In addition, the aircraft is affected to the parameter uncertainty and unknown disturbance such as wind near the mountainous terrain. Therefore, we suggest the nonlinear disturbance-based backstepping sliding mode control method for the aircraft that has highly nonlinearity to enhance flight path angle tracking performance. Through the numerical simulation, the proposed method showed the better tracking performance than the traditional backstepping method. Furthermore, the proposed method presented the terrain following maneuver maintaining the desired altitude.

Development of an Anti-rollover Warning and Active Intervention Controller for Wheeled Tractor

Background: Tractor rollover accidents are the most severe problem in agricultural production. According to the statistical analysis of the rollover incidents, some rollover accidents can be avoided by timely anti-rollover warning and active control. There are a few researches on the anti-rollover control for agricultural tractors, although great progress in the research on tractor security has been achieved, such as rollover protective structure design. Objective: The purpose of this patent study was to develop a controller with the function of warning as well as active anti-rollover control employing the power motor installed on the steering column to adjust the front wheel angle and control the rollover index within the safety range. Methodology: The structure block for the hardware of the controller was devised that included data obtaining, rollover risk estimation, rollover warning, and front-wheel steering control sections. The main circuits have been designed in detail by adopting module methods, such as motor control circuit, positioning and transmission circuit, etc. The control scheme has been demonstrated by designing an overview of the overall architecture, including rollover indicator calculation, load transfer ratio (LTR) controller design, and front-wheel steering angle tracking controller design. C language has been used to accomplish the software design. Results: The active anti-rollover controller can calculate and keep the LTR within the stable range by adjusting the front wheel angle in real-time. The fast response and smoothness of the results demonstrate that the controller can fulfill the designed function. Conclusion: The developed controller can meet the control requirement due to several advantageous aspects, such as reliability, effectiveness, and practicability. It is prospective for the developed controller to provide a valuable reference to reduce tractor rollover accidents.

Quantifying the effectiveness of an active treatment in improving highway-railway grade crossing safety in Canada: an empirical Bayes observational before–after study

Transportation engineers face challenging safety investment decisions, particularly for highway-railway grade crossings (HRGCs), where rare collision occurrences and incomplete historical records complicate the assessment of countermeasure cost-effectiveness. This study introduces an empirical Bayes (EB) observational before–after approach to address these challenges, specifically examining the impact of a widely used safety countermeasure: flashing lights, bells, and gates (FLBG). The research covers a total 171 FLBG crossings with pre-implementation conditions varying between standard reflective crossing signs and flashing lights and bells, utilizing collision data from 2000 to 2012 for analysis. This study is the first to substantiate quantitatively the effectiveness of FLBG treatment in improvising the safety of HRGCs and show that the effect is dependent of crossing characteristics, such as track angle, pavement conditions, and train speed limits. These findings can provide valuable support to decision-makers, enabling them to make more effective decisions for grade crossing safety improvement projects.

A modified handling stability control strategy for steer-by-wire vehicles based on variable steering ratio and active front steering control

Steer-by-wire (SBW) systems with steering actuator motors rotating the front wheels allow more flexibility for handling stability control; however, determining the desired steering angle and accurately tracking the desired steering angle are still key challenges. Therefore, a modified handling stability control strategy for handling stability is proposed in this paper. Firstly, based on the normal function, a unified variable steering ratio (UniVSR) model that varies with vehicle speed and steering wheel angle is developed. Then, the UniVSR model parameters are optimized by particle swarm optimization (PSO) algorithm in speed segments to improve the steering sensitivity at low speeds and the stability at medium and high speeds. Next, the active front steering (AFS) controller that considers the intervention timing and intervention threshold is proposed to improve the stability of the SBW vehicle under the driving conditions of low-adhesion road, split-μ road, and lateral wind interference. Lastly, the linear active disturbance rejection control (LADRC) based angle tracking control algorithm is presented to guarantee the desired angle tracking accuracy of the steering actuator motor under both internal parameter and external torque perturbations. The simulations validate the effectiveness of the proposed UniVSR model and AFS controller in improving handling stability.

Optimal predictive voltage control of a wind driven five phase PMSG system feeding an isolated load

Wind energy conversion systems (WECS) are considered to be a promising alternative to traditional power generation systems. The principal purpose of this study is to enhance the performance of a stand-alone wind-driven five-phase PMSG system. The initial step involves providing a clear explanation of the system model, which includes the wind turbine, five-phase PMSG, and a battery storage system. Then, the maximum power point tracking (MPPT) and pitch angle control (PAC) technique is employed to optimize the power delivered to an isolated load. Furthermore, the system incorporates the machine-side converter (MSC) and battery converter, which require control to efficiently regulate the power delivered from them to an isolated load. In addition, the MSC is regulated by two predictive control algorithms: predictive torque control (PTC) and a newly formulated predictive voltage control (PVC). The PTC faces limitations such as considerable ripple, significant load commutation, and the weighting factor of its cost functions. The proposed prediction technique aims to overcome these constraints by using a simple cost function and eliminating the need for weighting factors to address stability issues also another study's objective revolves around proposing a PVC controller that achieves an optimal design. The results demonstrate that the newly proposed predictive controller outperforms the conventional control method for the wind generator. The developed controller produces consistently distributed sinusoidal currents with significantly fewer ripples and current harmonics. This fact is verified mathematically as the total harmonic distortion (THD) is reduced to 1.168 % as an average value.

Deep reinforcement learning attitude control of stabilized platform for rotary steerable system based on extended state observer

A Deep Deterministic Policy Gradient with Extended State Observer (DDPG_ESO) method is proposed to address control accuracy and robustness issues in the attitude control of stabilized platforms for rotary steerable systems. A nonlinear extended state observer is developed to estimate the total system disturbance, which is then integrated into a deep reinforcement learning framework to enhance the controller’s adaptive capabilities. The controller is trained using variable amplitude and frequency external disturbances to improve its ability to suppress these disturbances. The superiority of the DDPG_ESO controller is validated through simulation experiments, demonstrating rapid tracking of toolface angles, reduced overshoot, and maintained steady-state errors. Compared to Proportional Integral Derivative (PID), Active Disturbance Rejection Control (ADRC), and Deep Deterministic Policy Gradient (DDPG) controllers, the DDPG_ESO algorithm exhibits higher robustness and adaptability under varying operational conditions, effectively mitigating the impact of external disturbances and modeling errors. The results indicate potential for practical application in the drilling industry

Nonlinear behavior analysis of smooth bore unbonded flexible pipes under radial compression

Smooth bore unbonded flexible pipes are widely used to transport oil and gas in offshore fields. Due to its main application in shallow water, the radial bearing capacity is usually designed to be low for economic reasons. If the compressive load applied by tensioner equipment is too high during installation, the flexible pipe will fail due to excessive deformation. In this paper, the nonlinear behavior of smooth bore flexible pipes passing through tensioner equipment was investigated numerically and experimentally. A three-dimensional numerical model considering the nonlinear behavior of the materials, layer-to-layer contacts, and friction for flexible pipe radial compression analysis was established. A radial compression test was conducted to verify the accuracy of the numerical model; Then the numerical model was used to analyze the radial compressive behavior of each layer, and its load-bearing capacities and failure mechanisms were investigated. Finally, a parametric study was performed on the opening angle and number of tensioner tracks. The results of this study can provide a useful reference for the installation of smooth bore flexible pipes.

Adaptive Dynamic Model-Based Path Following Controller Design for an Unmanned Surface Vessel

Abstract The effective design of a path following controller for Unmanned Surface Vessels (USVs) under uncertain influences induced by various factors such as environmental disturbances is a challenging task. In this study, we propose to ful?lling this task through taking benefits from an online parameter identi?cation technique, the discrete-time sliding mode control (DSMC) method, and the improved line of sight (LOS) algorithm. The Particle Swarm Optimization algorithm (PSO) was adopted to provide initial settings for the straightforward online identification method, i.e., the Forgetting Factor Recursive Least Square method (FFRLS). In order to handle the time-varying sideslip angle of a ship that exists in reality due to environmental disturbances, a multi-model course control scheme is proposed to improve the control performance. For this control scheme, a flexible selection mechanism in-between a heading angle or a course angle tracking controller based on the DSMC method is designed. A solution to ?xing the tracking deviation problem of the LOS guidance law is investigated for which the gradient descent method is introduced. A series of experiments are carried out at sea with a USV called Orca to verify and validate the proposed hybrid path following approach. The results showed that tracking errors mainly induced by environmental disturbances existed but the maximum magnitude among them was small enough and remaining within the acceptable range especially from the marine engineering point of view. These results, to a high degree, validated the robustness and precision of the proposed controller.

Comparing the Drop Vertical Jump Tracking Performance of the Azure Kinect to the Kinect V2.

Traditional motion analysis systems are impractical for widespread screening of non-contact anterior cruciate ligament (ACL) injury risk. The Kinect V2 has been identified as a portable and reliable alternative but was replaced by the Azure Kinect. We hypothesize that the Azure Kinect will assess drop vertical jump (DVJ) parameters associated with ACL injury risk with similar accuracy to its predecessor, the Kinect V2. Sixty-nine participants performed DVJs while being recorded by both the Azure Kinect and the Kinect V2 simultaneously. Our software analyzed the data to identify initial coronal, peak coronal, and peak sagittal knee angles. Agreement between the two systems was evaluated using the intraclass correlation coefficient (ICC). There was poor agreement between the Azure Kinect and the Kinect V2 for initial and peak coronal angles (ICC values ranging from 0.135 to 0.446), and moderate agreement for peak sagittal angles (ICC = 0.608, 0.655 for left and right knees, respectively). At this point in time, the Azure Kinect system is not a reliable successor to the Kinect V2 system for assessment of initial coronal, peak coronal, and peak sagittal angles during a DVJ, despite demonstrating superior tracking of continuous knee angles. Alternative motion analysis systems should be explored.

Integral barrier Lyapunov function-based fixed-time integrated guidance and control with asymmetric field-of-view angle constraints

The missile integrated guidance and control (IGC) problem with seeker’s asymmetric field-of-view (FOV) angle constraints is addressed. In the introduced model, the fin deflections controller is used to drive the body line-of-sight angle rate, which avoids the solving and tracking of aerodynamic angles in traditional IGC method. A novel fixed-time convergence virtual input based on the integral barrier Lyapunov function is designed to ensure the asymmetric FOV angle constraints are never violated. The virtual input is tracked by a new proposed pre-defined fixed time controller with adjustable initial convergence speed. The lumped uncertainty including aerodynamic coefficient and target maneuvering is coped by the fixed-time disturbance observer. It is proved that the closed-loop system states are converged to the bounded region in a fixed-time and the asymmetric FOV angle constraints are satisfied. The 6-degree of freedom flight simulations and comparisons verified the advantages of the proposed algorithm.

Rack force fault tolerance estimation of steer-by-wire system under different resolver faults based on sensor flows

In the context of vehicle intelligence, steer-by-wire (SbW) has great potential for development and has become a current research hotspot. The key challenge for SbW systems is to obtain the nonlinear rack force generated by tire-road interaction, which is the source of the driver's real steering feel and the main disturbance of steering angle tracking control. Existing rack force estimators either are only suitable for non-aggressive steering or assume that the measured signals are reliable. This paper proposes a high-precision rack force estimation algorithm that is robust to primary sensor faults of a SbW system. Further, a rack force estimator based on the established high-order SbW mechanism model is designed, and a detailed stability proof and feedback gain selection guidance are given. The resolver sensor model is provided and analyzed, and a fault detection method based on discrete Fourier transform (DFT) and data fusion is developed, which has good real-time performance and high accuracy. The performance of rack force estimator was verified and compared by simulations and hardware-in-loop (HIL) experiments with various steering maneuvers and different resolver faults. The experiment results show that the proposed rack force estimation method can not only effectively improve the estimation accuracy even in aggressive steering condition and uneven road profiles, but also achieve accurate estimation of the rack force in the case of resolver faults. The proposed method obtains high-precision and high-reliability rack force estimation results while being easily embedded applied in engineering practice.