Tracking Error Estimation Research Articles

Prediction of Point in Time with High Crash Risk by Integration of Bayesian Estimation of Drowsiness, Tracking Error, and Subjective Drowsiness

Prediction of Point in Time with High Crash Risk by Integration of Bayesian Estimation of Drowsiness, Tracking Error, and Subjective Drowsiness

레이다 전파굴절에 의한 발사체 추적오차 추정

본 논문은 발사체를 추적하는 단일 레이다 시스템에서 측정한 데이터에 포함된 오차를 추정하는 기법에 관한 내용을 다룬다. 레이다 시스템의 발사체 추적 데이터에는 발사체의 실제 위치, 방위각 혹은 고각 정보와 무작위 잡음, 그리고 전파굴절에 의한 바이어스가 포함되어져 있는 것으로 알려져 있다. 본 논문에서는 기존연구내용과는 달리, GPS와 같은 타추적 데이터를 사용하지 않고 단일 레이다 시스템의 발사체 추적 데이터만을 사용해 레이다 추적 데이터에 포함된 바이어스를 정확하게 추정하는 기법을 소개한다. 제안된 기법을 실제 나로호(KSLV-I) 추적 데이터에 적용하여 그 정확성을 검증하였다.

An Enhanced Non-Coherent Pre-Filter Design for Tracking Error Estimation in GNSS Receivers.

Tracking error estimation is of great importance in global navigation satellite system (GNSS) receivers. Any inaccurate estimation for tracking error will decrease the signal tracking ability of signal tracking loops and the accuracies of position fixing, velocity determination, and timing. Tracking error estimation can be done by traditional discriminator, or Kalman filter-based pre-filter. The pre-filter can be divided into two categories: coherent and non-coherent. This paper focuses on the performance improvements of non-coherent pre-filter. Firstly, the signal characteristics of coherent and non-coherent integration—which are the basis of tracking error estimation—are analyzed in detail. After that, the probability distribution of estimation noise of four-quadrant arctangent (ATAN2) discriminator is derived according to the mathematical model of coherent integration. Secondly, the statistical property of observation noise of non-coherent pre-filter is studied through Monte Carlo simulation to set the observation noise variance matrix correctly. Thirdly, a simple fault detection and exclusion (FDE) structure is introduced to the non-coherent pre-filter design, and thus its effective working range for carrier phase error estimation extends from (−0.25 cycle, 0.25 cycle) to (−0.5 cycle, 0.5 cycle). Finally, the estimation accuracies of discriminator, coherent pre-filter, and the enhanced non-coherent pre-filter are evaluated comprehensively through the carefully designed experiment scenario. The pre-filter outperforms traditional discriminator in estimation accuracy. In a highly dynamic scenario, the enhanced non-coherent pre-filter provides accuracy improvements of 41.6%, 46.4%, and 50.36% for carrier phase error, carrier frequency error, and code phase error estimation, respectively, when compared with coherent pre-filter. The enhanced non-coherent pre-filter outperforms the coherent pre-filter in code phase error estimation when carrier-to-noise density ratio is less than 28.8 dB-Hz, in carrier frequency error estimation when carrier-to-noise density ratio is less than 20 dB-Hz, and in carrier phase error estimation when carrier-to-noise density belongs to (15, 23) dB-Hz (26, 50) dB-Hz.

Adaptive compensation for infinite number of actuator failures/faults using output feedback control

Compensation for infinite number of actuator failures/faults using output feedback control is of both theoretical and practical importance. However, it is challenging and thus still remains open so far in the field of adaptive control. In this paper, we solve this problem by proposing a new tuning function based control scheme. It is demonstrated that all the signals of the closed-loop system are globally bounded and the steady-state tracking error can be reduced as small as desired, regardless of a possibility that there are infinite number of actuator failures. Moreover, when the number of actuator failures becomes finite, our scheme further guarantees that both the tracking error and state estimation errors converge to zero asymptotically.

Finite-Time Sliding Mode Control for Trajectory Tracking of WMRs with Wheel-Soil Friction Disturbance Adaptability

ABSTRACTThis paper presents a trajectory tracking approach based on terminal sliding mode control (TSMC) technique for wheeled mobile robot where the wheel-soil friction can be measured and the estimated errors will converge to their stable values within finite-time, by which the control system is able to be designed in accordance with the prescribed performance. Three kinds of friction effects and their acting mechanisms, at the first place, are argued by exploring the disturbance-like friction behaviors, and then two updated laws, i.e., taking uncertain friction as an entire term or as a parametric form for varied effects, are proposed. In addition, the adoption of TSMC enables the tracking error estimation being faster and more reliable, and the stability and reliability of closed-loop control system are guaranteed by means of Lyapunov stability theorem. Subsequently, the numerical simulation validates the feasibility and effectiveness of the proposed controllers by which the terrain adaptability, movement precision, and maneuvering capability will be improved significantly.

Robust adaptive control for vehicle active suspension systems with uncertain dynamics

This paper proposes a new robust adaptive law for adaptive control of vehicle active suspensions with unknown dynamics (e.g. non-linear springs and piece-wise dampers), where precise estimation of essential vehicle parameters (e.g. mass of vehicle body, mass moment of inertia for the pitch motions) may be achieved. This adaptive law is designed by introducing a novel leakage term with the parameter estimation error, such that exponential convergence of both the tracking error and parameter estimation error may be proved simultaneously. Appropriate comparisons with several traditional adaptive laws (e.g. gradient and σ-modification method) concerning the convergence and robustness are presented. The mitigation of the vertical and pitch displacements can be achieved with the proposed control to improve the ride comfort. The suspension space limitation and the tyre road holding are also studied. A dynamic simulator consisting of commercial vehicle simulation software Carsim® and Matlab® is built to validate the efficacy of the proposed control scheme and to illustrate the improved estimation performance with the new adaptive law.

Improving transient performance of adaptive control via a modified reference model and novel adaptation

SummaryThis paper presents a new model reference adaptive control (MRAC) framework for a class of nonlinear systems to address the improvement of transient performance. The main idea is to introduce a nonlinear compensator to reshape the closed‐loop system transient, and to suggest a new adaptive law with guaranteed convergence. The compensator captures the unknown system dynamics and modifies the given nominal reference model and the control action. This modified controlled system can approach the response of the ideal reference model. The transient is easily tuned by a new design parameter of this compensator. The nominal adaptive law is augmented by new leakage terms containing the parameter estimation errors. This allows for fast, smooth and exponential convergence of both the tracking error and parameter estimation, which again improves overall reference model following. We also show that the required excitation condition for the estimation convergence is equivalent to the classical persistent excitation (PE) condition. In this respect, this paper provides an intuitive and numerically feasible approach to online validate the PE condition. The salient feature of the suggested methodology is that the rapid suppression of uncertainties in the controlled system can be achieved without using a large, high‐gain induced, learning rate in the adaptive laws. Extensive simulations are given to show the effectiveness and the improved response of the proposed schemes. Copyright © 2016 John Wiley & Sons, Ltd.

Fault Diagnosis and Fault Tolerant Control for Non-Gaussian Singular Time-Delayed Stochastic Distribution Systems with Disturbance Based on the Rational Square-Root Model

For the non-Gaussian singular time-delayed stochastic distribution control (SDC) system with unknown external disturbance where the output probability density function (PDF) is approximated by the rational square-root B-spline basis function, a robust fault diagnosis and fault tolerant control algorithm is presented. A full-order observer is constructed to estimate the exogenous disturbance and an adaptive observer is used to estimate the fault size. A fault tolerant tracking controller is designed using the feedback of distribution tracking error, fault, and disturbance estimation to let the postfault output PDF still track desired distribution. Finally, a simulation example is included to illustrate the effectiveness of the proposed algorithms and encouraging results have been obtained.

Optical losses due to tracking error estimation for a low concentrating solar collector

The determination of the accuracy of a solar tracker used in domestic hot water solar collectors is not yet standardized. However, while using optical concentration devices, it is important to use a solar tracker with adequate precision with regard to the specific optical concentration factor. Otherwise, the concentrator would sustain high optical losses due to the inadequate focusing of the solar radiation onto its receiver, despite having a good quality.This study is focused on the estimation of long-term optical losses due to the tracking error of a low-temperature collector using low-concentration optics. For this purpose, a testing procedure for the incidence angle modifier on the tracking plane is proposed to determinate the acceptance angle of its concentrator even with different longitudinal incidence angles along the focal line plane. Then, the impact of maximum tracking error angle upon the optical efficiency has been determined. Finally, the calculation of the long-term optical error due to the tracking errors, using the design angular tracking error declared by the manufacturer, is carried out.The maximum tracking error calculated for this collector imply an optical loss of about 8.5%, which is high, but the average long-term optical loss calculated for one year was about 1%, which is reasonable for such collectors used for domestic hot water.

Ultimate Boundedness Theorem for Model Reference Adaptive Control Systems

This paper presents a novel formulation of the Lyapunov-based ultimate boundedness theorem with explicit specification of transient and ultimate bounds, time to the ultimate bound, and the set of admissible initial conditions. The formulation is tailored to the structure of model reference adaptive control systems accounting for a partitioning of the state vector into a tracking error and a parameter estimation error. It allows for a computation of separate bounds for the single state vector partitions in favor of a single bound on the compound state, consisting of both partitions. This is of great practical importance because tracking error and parameter estimation error are of different magnitudes in general. Regarding model reference adaptive control systems, it is the state of the technology so far that the ultimate boundedness theorem is tailored to the structure of the considered system each time, again and again. It is hence manifest to formulate once a generic version of the theorem, tailored to model reference adaptive control systems, but not to reinvent the wheel for each case.

On the Stability of Linear Active Disturbance Rejection Control

This paper investigates the estimation ability of linear extended state observer(ESO) and the stability of the closed-loop system under the linear active disturbance rejection control(LADRC).The bounds of the estimation tracking errors for linear extended state observer(ESO) are proved when the mathematical model of the plant is not given,and the following results are given:under the condition that the tracking error of ESO converges to zero,the accurate tracking to the setpoint signal and the bounded input and bounded output(BIBO) stability can be achieved for the closed loop system under the control of LADRC.

Series-Parallel Approach to On-line Observer based Neural Control of a Helicopter System

This paper is concerned with the control of an under-actuated, uncertain, delayed non-linear system through the implementation of artificial neural networks(ANNs). The aim is the development of a series-parallel training scheme for the on-line observer to ensure faster convergence and more accurate estimations. Reinforcement learning is used to improve future performance and maintain stability while an estimated tracking error is minimised. Lyapunov stability measures are employed to guarantee the uniform ultimate boundedness of the closed-loop tracking error. Real-time learning algorithms are derived for the individual components (observer, actor, critic). Final performance is tested on a mathematical helicopter model and real-world helicopter flight data.

Adaptive neural network control of robot manipulator using reinforcement learning

In this paper, a stable reinforcement learning control approach using neural networks (NNs) is developed for the trajectory tracking of an n-link rigid robot manipulator. The considered systems are in discrete time form. The proposed controller design consists of two NNs. One is the critic network that is used to approximate the long-term cost function, whereas other is that the action NN is employed to generate the system input. Then, an optimal control input can be obtained compared with other robot manipulator systems. Using the Lyapunov approach, the tracking error and weight estimates are proven to be semi-global uniformly ultimately bounded. A simulation example is employed to illustrate the effectiveness of the proposed controller.

Automatic tracking of implanted fiducial markers in cone beam CT projection images

This paper describes a novel method for simultaneous intrafraction tracking of multiple fiducial markers. Although the proposed method is generic and can be adopted for a number of applications including fluoroscopy based patient position monitoring and gated radiotherapy, the tracking results presented in this paper are specific to tracking fiducial markers in a sequence of cone beam CT projection images. The proposed method is accurate and robust thanks to utilizing the mean shift and random sampling principles, respectively. The performance of the proposed method was evaluated with qualitative and quantitative methods, using data from two pancreatic and one prostate cancer patients and a moving phantom. The ground truth, for quantitative evaluation, was calculated based on manual tracking preformed by three observers. The average dispersion of marker position error calculated from the tracking results for pancreas data (six markers tracked over 640 frames, 3840 marker identifications) was 0.25 mm (at iscoenter), compared with an average dispersion for the manual ground truth estimated at 0.22 mm. For prostate data (three markers tracked over 366 frames, 1098 marker identifications), the average error was 0.34 mm. The estimated tracking error in the pancreas data was < 1 mm (2 pixels) in 97.6% of cases where nearby image clutter was detected and in 100.0% of cases with no nearby image clutter. The proposed method has accuracy comparable to that of manual tracking and, in combination with the proposed batch postprocessing, superior robustness. Marker tracking in cone beam CT (CBCT) projections is useful for a variety of purposes, such as providing data for assessment of intrafraction motion, target tracking during rotational treatment delivery, motion correction of CBCT, and phase sorting for 4D CBCT.

Multisensor fusion fault tolerant control

In this paper, a multisensor fusion fault tolerant control system with fault detection and identification via set separation is presented. The fault detection and identification unit verifies that for each sensor–estimator combination, the estimation tracking errors lie inside pre-computed sets and discards faulty sensors when their associated estimation tracking errors leave the sets. An active fault tolerant controller is obtained, where the remaining healthy estimates are combined using a technique based on the optimal fusion criterion in the linear minimum-variance sense. The fused estimates are then used to implement a state feedback tracking controller. We ensure closed-loop stability and performance under the occurrence of abrupt sensor faults. Experimental validation, illustrating the multisensor fusion fault tolerant control strategy is included.

다중경로 추적오차 모사 알고리즘 연구

This paper describes the radar tracking errors due to the presence of surface reflected waves. We investigate the previous studies on tracking errors and developed a tracking error estimation program using Matlab. We suggest an accurate error prediction method by considering the antenna beamwidth and surface reflection coefficient. The presented results well agree with theoretical predictions.

Reinforcement-Learning-Based Output-Feedback Control of Nonstrict Nonlinear Discrete-Time Systems With Application to Engine Emission Control

A novel reinforcement-learning-based output adaptive neural network (NN) controller, which is also referred to as the adaptive-critic NN controller, is developed to deliver the desired tracking performance for a class of nonlinear discrete-time systems expressed in nonstrict feedback form in the presence of bounded and unknown disturbances. The adaptive-critic NN controller consists of an observer, a critic, and two action NNs. The observer estimates the states and output, and the two action NNs provide virtual and actual control inputs to the nonlinear discrete-time system. The critic approximates a certain strategic utility function, and the action NNs minimize the strategic utility function and control inputs. All NN weights adapt online toward minimization of a performance index, utilizing the gradient-descent-based rule, in contrast with iteration-based adaptive-critic schemes. Lyapunov functions are used to show the stability of the closed-loop tracking error, weights, and observer estimates. Separation and certainty equivalence principles, persistency of excitation condition, and linearity in the unknown parameter assumption are not needed. Experimental results on a spark ignition (SI) engine operating lean at an equivalence ratio of 0.75 show a significant (25%) reduction in cyclic dispersion in heat release with control, while the average fuel input changes by less than 1% compared with the uncontrolled case. Consequently, oxides of nitrogen (NO(x)) drop by 30%, and unburned hydrocarbons drop by 16% with control. Overall, NO(x)'s are reduced by over 80% compared with stoichiometric levels.

Active vs. Passive Management: New Evidence from Exchange Traded Funds

This paper expands the debate about vs. management using data from active and passive ETFs listed in the U.S. market. The results reveal that the active ETFs underperform both the corresponding passive ETFs and the market indexes. With respect to risk-adjusted returns, both active and passive ETFs provide investors with no positive excess returns, an expectable finding for the passive ETFs but not for the active ETFs which are aimed at beating the market. Going further, the underperformance of active ETFs is depicted to the low performance rates such as the Sharpe or the Treynor ratios they receive relative to the passive ETFs and the indexes. Furthermore, regression analysis on the selectivity and market timing skills of ETF managers indicate that the managers of both the active and passive ETFs are lacking in such skills. However, the passive managers are not expected to have such skills. Finally, tracking error estimates indicate that the discrepancy between ETF and index returns is greater for active ETFs. However, this result is to be expected as the active ETFs do not target to replicate the performance of the indexes.

Fuzzy-identification-based adaptive backstepping control using a self-organizing fuzzy system

In this paper, a fuzzy-identification-based adaptive backstepping control (FABC) scheme is proposed. The FABC system is composed of a backstepping controller and a robust controller. The backstepping controller, which uses a self-organizing fuzzy system (SFS) with the structure and parameter learning phases to on-line estimate the controlled system dynamics, is the principal controller, and the robust controller is designed to dispel the effect of approximation error introduced by the SFS. The developed SFS automatically generates and prunes the fuzzy rules by the proposed structure adaptation algorithm and the parameters of the fuzzy rules and membership functions tunes on-line in the Lyapunov sense. Thus, the overall closed-loop FABC system can guarantee that the tracking error and parameter estimation error are uniformly ultimately bounded; and the tracking error converges to a desired small neighborhood around zero. Finally, the proposed FABC system is applied to a chaotic dynamic system to show its effectiveness. The simulation results verify that the proposed FABC system can achieve favorable tracking performance even with unknown controlled system dynamics.

Real-Time Tracking Error Estimation for Augmented Reality for Registration with Linecode Markers

Augmented reality tasks require a high-reliability tracking method. Large tracking error causes many problems during AR applications. Tracking error estimation should be integrated with them to improve the reliability of tracking methods. Although some tracking error estimation methods have been developed, they are not feasible to be integrated because of computational speed and accuracy. For this study, a tracking error estimation algorithm with screen error estimation based on the characteristic of linecode marker was applied. It can rapidly estimate tracking error. An evaluation experiment was conducted to compare the estimated tracking error and the actual measured tracking error. Results show that the algorithm is reliable and sufficiently fast to be used for real-time tracking error warning or tracking accuracy improvement methods.