Large Tracking Errors Research Articles

Performance Evaluation of TGFS Typhoon Track Forecasts over the Western North Pacific with Sensitivity Tests on Cumulus Parameterization

This study employed the new generation Taiwan global forecast system (TGFS) to focus on its performance in forecasting the tracks of western North Pacific typhoons during 2022–2023. TGFS demonstrated better forecasting performance in typhoon track compared to central weather administration (CWA) GFS. For forecasts with large track errors by TGFS at the 120th h, it was found that most of them originated during the early stages of typhoon development when the typhoons were of mild intensity. The tracks deviated predominantly towards the northeast and occasionally towards the southwest, which were speculated to be due to inadequate environmental steering guidance resulting from the failure to capture synoptic environmental features. The tracks could be corrected by replacing the original new simplified Arakawa–Schubert (NSAS) scheme with the new Tiedtke (NTDK) scheme to change the synoptic environmental field, not only for Typhoon Khanun, which occurred in the typhoon season of 2023, but also for Typhoon Bolaven, which occurred after the typhoon season, in October 2023, under atypical circulation characteristics over the western Pacific. The diagnosis of vorticity budget primarily analyzed the periods where divergence in typhoon tracks between control (CTRL) and NTDK experiments occurred. The different synoptic environmental fields in the NTDK experiment affected the wavenumber-1 vorticity distribution in the horizontal advection term, thereby enhancing the accuracy of typhoon translation velocity forecasts. This preliminary study suggests that utilizing the NTDK scheme might improve the forecasting skill of TGFS for typhoon tracks. To gain a more comprehensive understanding of the impact of NTDK on typhoon tracks, further examination for more typhoons is still in need.

Design and analysis of tracking differentiator based on SO(3)

Motivated by the issue of insufficient dynamic performance and tracking accuracy in SO(3)-based attitude tracking differentiators during large-angle maneuvers and complex trajectory tracking, a novel design approach for a three-degree-of-freedom attitude tracking differentiator within the SO(3) framework is proposed by incorporating second-order system theory and Lie group theory and improving the classical tracking differentiator. The kinematics model and error dynamics model of a rigid body on SO(3) are derived, and a reasonable virtual control input on SO(3) is constructed subsequently in order to achieve better dynamic response and tracking performance. Simulation and experimental results validate that the designed tracking differentiator could realize rapid and smooth convergence during large-angle maneuvers, and the initial large tracking error rapidly drops to near zero in a short period of time; additionally, it can also track expected time-varying curves well in complex trajectory tracking, with initial errors rapidly decreasing and maintaining at normal levels, demonstrating excellent tracking and control capabilities. There are strong application prospects for this new approach in addition to its theoretical significance.

Robust finite-time sliding mode control of unmanned surface vehicle with active compensation of pose estimation uncertainty

Robust control of Unmanned Surface Vehicles (USVs) is crucial for their safe and reliable navigation. The control methods of USV generally regard the results of pose estimation (such as GNSS/INS) as an accurate reference, but GNSS/INS is usually fluctuating and uncertain in the open water, which easily leads to large tracking errors or even control failure of USV. To this end, a robust sliding mode control of USV considering the active compensation of pose estimation uncertainty is innovatively presented, which can ensure the stability and accuracy of USV's control. The distinctive features of the proposed method are twofold: (i) To improve the pose estimation accuracy and obtain accurate uncertainty evaluation, an improved adaptive Kalman filtering considering the time-varying solution state is presented, which utilizes the convergence characteristics of INS and the time-varying features of GNSS solution state; (ii) By integrating pose estimation uncertainty assessment and active compensation, a Robust Finite-Time Sliding Mode Control (RFT-SMC) for a twin-propeller unmanned surface vehicle is developed to enhance tracking accuracy and anti-disturbance capability, and its convergence is established using a Lyapunov function, ensuring reliable operation of the USV even in the presence of uncertain pose estimation. Finally, verification experiments are offered to verify the effectiveness of the presented RFT-SMC method.

Event-triggered dynamic prescribed performance control for oxygen stoichiometry and stack temperature in PEM fuel cell systems with actuators faults

Oxygen stoichiometry (OS) and stack temperature (ST) significantly affect the output performance and durability of proton exchange membrane fuel cells. This paper investigates the prescribed performance tracking control strategy of OS and ST under external disturbances, modeling errors, actuators faults and limited communication resources. The total disturbances are compensated by a variable-bandwidth extended state observer to simplify the control design and enhance the system robustness. To tolerate excessive disturbances and relax the strict requirement on the initial conditions, a new event-triggered adaptive sliding mode controller that integrates modified dynamic performance constraints and nonsingular error transformation is developed to ensure the user-designed transient and steady-state performance constraints, while saving the communication costs and avoiding the chattering and jump in control signals. The numerical validation results under combined working scenarios indicate that the proposed controller always ensures the preset error constraints and possesses superior performance in tracking the optimal OS value 2 and the optimal ST value 353.15K than other three representative controllers, with the integral absolute errors of 0.732 for OS tracking and 12.94 for ST tracking; besides, it achieves satisfactory robustness to uncertain model parameters and relatively large initial tracking errors.

Solar-pumped dual-rod Ce:Nd:YAG laser with 58W continuous-wave output power and 5.1° tracking error compensation width.

The pursuit of high-power solar laser systems with high efficiency and capacity for large tracking error compensation is determinant for the applicability of this renewable technology. A side-pumped dual-rod Ce:Nd:YAG solar laser was developed and tested at the focus of a 2 m diameter parabolic concentrator. Maximum continuous-wave total solar laser power of 58 W was measured. To the best of our knowledge, this is the highest laser power from a Ce:NdYAG solar laser. Moreover, wide tracking error compensation width of 5.1° in the azimuthal direction was reached, being 4.25 times higher than the previous measurement without solar tracking assistance.

Predictability of the Most Long‐Lived Tropical Cyclone Freddy (2023) During Its Westward Journey Through the Southern Tropical Indian Ocean

AbstractThis study aimed to explore the predictability of the most long‐lived tropical cyclone (TC) Freddy in 2023 while it traversed westward across the southern tropical Indian Ocean during the first 18 days of its existence. Global ensemble forecasts revealed southward track deflection and intensity underestimation of Freddy. We identified three key factors contributing to the limited predictability of Freddy, which are associated with the Mascarene High, Storm Dingani, and Freddy itself. The large track errors of Freddy can be attributed to the underestimated strength of the Mascarene High, the more northeastern position of Dingani, and the presence of excessively large or small sizes of Freddy. These findings were further validated through a high‐resolution regional model. Specifically, Freddy's track and intensity most closely matched the observations when these three factors were most closely represented. It underscores the pivotal role played by the interaction between TCs and multi‐scale systems in TC forecasts.

In situ skid estimation for mobile robots in outdoor environments

AbstractSkidding is a surface hazard for mobile robots' navigation and traction control systems when operating in outdoor environments and uneven terrains due to the wheel–terrain interaction. It could lead to large trajectory tracking errors, losing the robot's controllability, and mission failure occurring. Despite research in this field, the development of a real‐world feasible in situ skid estimation system with the capability of operating in harsh and unforeseen environments using low‐cost/power and ease of integrating sensors is still an open problem in terramechanics. This paper presents a novel velocity‐based definition for skidding that enables a real‐world feasible estimation for the mobile robot's undesired skidding at the vehicle‐level in outdoor environments. The proposed technique estimates the undesired skidding using a combination of two proprioceptive sensors (e.g., inertial measurement unit and wheel encoder) and deep learning. The practicality of a velocity‐based definition and the performance of the proposed undesired skid estimation technique are evaluated experimentally in various outdoor terrains. The results show that the proposed technique performs with less than 11.79 mm/s mean absolute error and estimates the direction of undesired skidding with approximately 98% accuracy.

Low Current Ripple Parameter-Free MPCC of Grid-Connected Inverters for PV Systems

With an emphasis placed on a low-carbon economy, photovoltaic grid-connected inverters are moving toward the center of the stage. In order to address the problems related to the strong parameter dependence of the conventional model’s predictive control in grid-connected inverters, an improved parameter-free predictive current control is proposed. Relying on an extended state observer, an ultralocal model is employed to predict future currents without any parameters. The system can achieve a satisfactory performance in terms of dynamic response and robustness. Additionally, 30 virtual voltage vectors are extended for lower current ripples, which is followed by the use of a triangle candidate strategy to significantly ease the computing burden. In general, the proposed strategy omits parameter dependence, complex tuning work, and large tracking errors. The effectiveness of the proposed model is verified through the experimental results.

Eight-Day Typhoon Quantitative Precipitation Forecasts in Taiwan by the 2.5 km CReSS Model, Part II: Reduced Control of Track Errors on Rainfall Prediction Quality for Typhoons Associated with Southwesterly Flow

Due to the enhancement by its steep mesoscale topography, the overall rainfall amount and distribution in Taiwan from typhoons, to a first degree, are determined by the storm track relative to the island. Therefore, the quality of typhoon quantitative precipitation forecasts (QPFs) from numerical models is often controlled by track errors, with better quality from those with smaller track errors. However, the present work demonstrates that in daily QPFs over Taiwan made by a cloud-resolving model during five seasons of 2012–2016, targeted for 84 days during 27 typhoons and at ranges of day one (0–24 h) to day eight (168–192 h), the control of track errors on QPF quality is reduced for typhoons associated with southwesterly flow, compared to those without, and decent QPFs could still be obtained with large track errors in some cases. Subsequently, the circumstances and reasons for good (or bad) QPFs in selected examples are further investigated to deepen our understanding of typhoon QPFs in Taiwan. Some common ingredients are found in three cases where good QPFs were produced at a longer range (day 7 or 8) without a good track: these typhoons passed near northern Taiwan and the southwesterly flow prevailed over much of the island during the accumulation period. Responsible for much of the rainfall in Taiwan, the southwesterly flow was reasonably captured, resulting in good QPFs. In another example where the typhoon moved across southern Taiwan, on the contrary, the rainfall was produced by the storm’s circulation, and the QPF was degraded without a good enough track prediction.

VOT and VFT-Based hybrid digital hysteresis control for UPS inverter

When the digital hysteresis current control (DHCC) strategy is applied to control the uninterruptible power supply (UPS) inverter, here are some issues such as large hysteresis tracking error caused by sampling interval and severe switching frequency swing caused by rapid change rate of inductor current. To address these issues, this paper presents a digital control strategy based on the hybrid digital hysteresis current control (HDHCC) of variable opening time (VOT) and variable falling time (VFT) for UPS inverter. Firstly, the HDHCC structure is established and the working principle of the inverter system is described in detail. Then, the tracking error and switching frequency error of the DHCC are derived. Based on the performance requirement of quasi-constant frequency control of UPS inverter, VOT and VFT control criteria without the dependency of circuit parameters are given. The experimental results demonstrate that the hybrid digital hysteresis control strategy based on VOT and VFT has the advantages of high current tracking accuracy, accurate switching frequency control, small output voltage waveform distortion, and simple implementation.

Motion Trajectory Error of Robotic Arm Based on Neural Network Algorithm

In order to solve the problems of unstable motion and large trajectory tracking error of the manipulator when it is disturbed by the outside world, the author proposes an adaptive neural network manipulator motion trajectory error method. The author gives the dynamic equation of the manipulator and uses the positive feedback neural network to study the dynamic characteristics of the manipulator. An adaptive neural network control system is designed, and the stability and convergence of the closed-loop system are proved by the Lyapunov function. A schematic diagram of the manipulator model is established, and MATLAB/Simulink software is used to simulate the dynamic parameters of the manipulator. At the same time, it is compared and analyzed with the simulation results of the PID control system. Simulation results show that in robot arm 3, the expected motion trajectory is θ3 = 0.4cos(2πt), the initial condition θ(0) = [000]τ, the control parameter K = diag(40,40),40), the disturbance parameter τ’ = 20cos(πt), robot arm link parameters l1 = 0.62 m, l2 = 0.41 m, l3 = 0.34 m, m1 = 3.5, m2 = 2.5 kg, m3 = 2 kg, g = 9.82 m/s2, under t = 2s, the motion trajectory of the robotic arm is disturbed by the outside world, and the adaptive neural network is used to control the motion trajectory with a small tracking error, input torque ripple is small. Conclusion. The manipulator adopts the adaptive neural network control method, which can improve the control accuracy of the motion trajectory and weaken the jitter phenomenon of the manipulator motion.

RadarPDR: Radar-Assisted Indoor Pedestrian Dead Reckoning

Pedestrian dead reckoning (PDR) is the critical component in indoor pedestrian tracking and navigation services. While most of the recent PDR solutions exploit in-built inertial sensors in smartphones for next step estimation, due to measurement errors and sensing drift, the accuracy of walking direction, step detection, and step length estimation cannot be guaranteed, leading to large accumulative tracking errors. In this paper, we propose a radar-assisted PDR scheme, called RadarPDR, which integrates a frequency-modulation continuous-wave (FMCW) radar to assist the inertial sensors-based PDR. We first establish a segmented wall distance calibration model to deal with the radar ranging noise caused by irregular indoor building layouts and fuse wall distance estimation with acceleration and azimuth signals measured by the inertial sensors of a smartphone. We also propose a hierarchical particle filter(PF) together with an extended Kalman filter for position and trajectory adjustment. Experiments have been conducted in practical indoor scenarios. Results demonstrate that the proposed RadarPDR is efficient and stable and outperforms the widely used inertial sensors-based PDR scheme.

Research on dynamic landing guidance algorithm for the maritime quadrotor

The dynamic landing guidance of the marine quadrotor is composed of trajectory guidance and landing guidance, which can't be completed by the traditional land-based landing guidance method. In addition, complex sea environment will increase the difficulty in dynamic landing guidance such as enhancing the position deviation, which will make the quadrotor unable to land accurately. To address the large trajectory tracking error when the quadrotor is flying, an adaptive step-based line-of-sight (LOS) guidance algorithm is adopted in this work to achieve dynamic three-dimensional (3D) trajectory tracking. The algorithm can not only track the trajectory of the quadrotor in a 3D space based on the position information of the wave glider, but also dynamically adjust the tracking angle and orientation in real time based on the distance and orientation information to improve the tracking accuracy. To address the low accuracy in dynamic positioning of the quadrotor while landing on the wave glider floating body, a visual landing guidance method with fused electromagnetic compensation is used. It can improve accuracy in the dynamic landing guidance of the April Tag visual reference system and reduce the interference from shaking of the wave glider floating body on landing positioning guidance of the quadrotor. The simulation results verify that the proposed algorithm can effectively reduce the trajectory tracking error of the quadrotor and accelerate trajectory convergence. The sea trial experiments prove that the trajectory tracking and landing guidance algorithm adopted in this work can well guide the quadrotor in dynamic and accurate landing on the wave glider floating body, and shorten the time.

Evaluation and Characterization of the Influence of Solar Position Algorithm on the Performance of Parabolic Trough Solar System

For the open loop control system based on the solar position algorithm (SPA), without sensor correction, the error of SPA will bring the tracking error directly to decrease the efficiency of the solar system. By comparing with SPA proposed by NREL, this paper first evaluates main SPAs with different accuracy and presents the error of SPA on tracking error. Based on the annual average efficiency of the solar trough system, this paper evaluates the impact of the tracking error caused by SPA on the solar trough system, and proposes that the average SPA calculation error can be applied to characterize the impact of SPA on the trough solar system. By making a comparison of solar trough system efficiency calculated with fixed annual average tracking error and normal constantly changing tracking error within a year, respectively, the evaluation results show that for most SPAs, the introduced error is less than 0.05%, and only a few empirical algorithms with large tracking errors introduce larger errors, but they are not suitable for the trough solar system. Therefore, the SPA evaluation method proposed in this paper is applicable to the solar trough system.

Autonomous cooperative formation control of underactuated USVs based on improved MPC in complex ocean environment

A dual model predictive control (DMPC) method based on virtual trajectory is proposed in this paper, in order to achieve autonomous cooperative formation control of underactuated unmanned surface vehicles (USVs) in complex ocean environment. Firstly, the formation tracking error model of the USVs is designed, and considering the large initial state tracking error of USVs, a virtual transition trajectory is put forward to guide the design of tracking controller. To solve the mutation problem caused by misalignment of the follower USV's desired trajectory in the early stage, an improved differential tracker is introduced into virtual leader USV to smooth the desired trajectory of the follower USV. In addition, a dual mode switching strategy is designed to decide when and how to quit the transition of the virtual USV. A nonlinear disturbance observer is introduced to compensate the complex marine environment interference. Finally, by introducing terminal penalty function and linear state feedback controller, the Lyapunov function is constructed to prove the control stability of the proposed model predictive control method in finite time domain, and the effectiveness and reliability of the proposed method are verified by simulation experiments.

Design of Nonlinear Active Disturbance Rejection Controller Based on the Adaptive Particle Swarm Optimization Algorithm for the Maglev Train Traction Control System

This paper focuses on speed tracking control of the maglev train operation system. Given the complexity and instability of the maglev train operation system, traditional speed‐tracking control algorithms demonstrate poor tracking accuracy and large tracking errors. The maglev train is easily affected by external interference, increasing train energy consumption and reducing passengers’ riding comfort. This study proposes a control algorithm called APSO‐NLADRC to address the deficiencies of the automatic train operation control algorithms. The APSO‐NLADRC is based on adaptive particle swarm optimization (APSO) algorithm parameter optimization nonlinear active disturbance rejection controller (NLADRC). The method of population comparison, linear update of learning factors, and adaptive updating of inertia weight values addresses the premature convergence phenomenon that occurs during the parameter optimization of the traditional particle swarm algorithm. The APSO algorithm solves the problem that the parameters of the NLADRC are difficult to adjust. Compared with PID, NLADRC, and NLADRC based on traditional particle swarm optimization algorithms, the proposed control algorithm has higher tracking accuracy and more robust anti‐interference capability and provides better comfort.

An Expectation-Maximization-Based Estimation Algorithm for AOA Target Tracking With Non-Gaussian Measurement Noises

This paper considers angle-of-arrival (AOA) target tracking in the two-dimensional plane with non-Gaussian noise. Practical situations arise where sensor measurement noise is non-Gaussian and the standard extended Kalman filter (EKF) based tracker may not be robust, resulting in large tracking error or even lack of convergence. This motivated development of a new tracker, designed for white non-Gaussian noise cases. The noise is decomposed into a sum of Gaussian components using expectation maximization (EM), and the target state is estimated by adapting an extended Kalman filter (EKF). Adaptations are necessary to combine the EM with the iterative EKF. From the analysis of the estimation error, a closed-form expression was derived revealing the presence of a bias in the estimate. On investigation, a sensor path optimization scheme is found that can eliminate bias. Furthermore, a compensation algorithm is presented, which reduces bias for the case of static sensors. The new tracker is suitable for real-time implementation and simulation results demonstrate significant improvements for the target tracking with non-Gaussian noise.

V-Stability Based Control for Energy-Saving Towards Long Range Sailing

Ensuring an adequate supply of energy is a challenge in the development of autonomous sailboats for long-range sailing capability, as the continuous high-frequency execution of control commands leads to high energy consumption. However, as the control of sailboat is much more complex than the other USVs, especially in a complicated and windy environment, simply reducing control frequency will lead to large path tracking error, and even unsuccessful navigation. Based on the V-stability controller of sailboats, this letter proposes an energy-saving (E-saving) control scheme for a sailboat, to enable the energy planning of actuators. While keeping the sailboat traveling within the acceptable range along a desired path, we develop an approach to analyze and reach a satisfying tradeoff between path tracking error and consumed energy. The E-saving method is examined in simulated environments and field experiments using an autonomous sailboat (OceanVoy). The results show that E-saving method reduces energy consumption by approximately 11% compared to that of the previous V-stability controller, indicating that our method can elongate sailing distance.

Evaluation of Lung and Liver Tumor Dose Coverage Treated With the CyberKnife Synchrony System With Consideration of Measured Tracking Errors.

Lung and liver tumor dose coverage was evaluated for the CyberKnife synchrony respiratory tracking system (SRTS) with consideration of the motion tracking accuracy measured for motion patterns of individual patients. Seven treatment plans of six cases treated with the SRTS were evaluated. The motion phantom was moved with the motion data derived from the treatment log files. A laser emitted from the linac head to the moving phantom block was recorded with a webcam, and the tracking accuracy was evaluated. The dose volume histogram (DVH) of planning target volume (PTV) and gross tumor volume (GTV) were calculated by a pencil beam algorithm with shifting the beams with Gaussian random numbers mimicking the measured tracking errors. The tracking errors measured with the motion phantom in the lateral direction were within ±2 mm for 90% of beam-on time. The tracking errors in the longitudinal direction were within ±3.0 mm and ±1.1 mm for 90% and 50% of beam-on time, respectively. Although one case showed a decrease in the dose covering 95% of PTV (D95%) by 1.8%, the change in the dose covering 99% of GTV (D99%) was within 1%. This study evaluated the motion tracking errors of the SRTS by a motion phantom moved with the patients' respiration signal, and the impact of the tracking errors on the target coverage was calculated. Even for respiratory patterns with large maximum tracking errors, sufficient GTV coverage is achievable if the beam is accurately delivered for high percentage of beam-on time.

Negative quadrant glitch suppression control of ball‐screw‐driven stage for machine tool by friction compensation and initial value compensation

AbstractBall‐screw‐driven stages are widely used as feed systems of industrial equipment such as machine tools. Rolling friction in the stages causes a large tracking error called a quadrant glitch at a velocity reversal. Although the model‐based friction compensation reduces the quadrant glitch, it often leads to a tracking error in the direction opposite to the quadrant glitch. This study aims to present a control system that reduces both the quadrant glitch and opposite‐direction error. This is achieved by combining the model‐based friction compensation and initial value compensation. The effectiveness of the proposed control system is verified by simulations and experiments with two‐axis experimental setup.