Strong Tracking Ability Research Articles

Adaptive Human–Robot Interaction Torque Estimation With High Accuracy and Strong Tracking Ability for a Lower Limb Rehabilitation Robot

Adaptive Human–Robot Interaction Torque Estimation With High Accuracy and Strong Tracking Ability for a Lower Limb Rehabilitation Robot

Determination of the number of 235U target nuclei in the irregular target using a fission time projection chamber

Based on multiple measurements of ionization loss, the Time Projection Chamber (TPC) combines strong tracking ability with particle identification ability in a large momentum range, which is an important advantage of TPC detection technology over traditional ionization measurement technology. According to these two characteristics of TPC, applying it to the measurement of fission cross-section can greatly improve the measurement accuracy. During the measurement of the fission cross-section, the number of target nuclei is required to be accurately measured. So this paper introduces a method for measuring the number of 235U target nuclei using a fission TPC system. The measurement result agrees with the reference value, and relative error is around 1 %.

Finite-time ADRC formation control for uncertain nonaffine nonlinear multi-agent systems with prescribed performance and input saturation

AbstractThis paper explores finite-time formation control of multi-agent systems (MASs) with high-order nonaffine nonlinear dynamics and saturated input. Based on active disturbance rejection control theory, extended state observer is employed to identify unknown nonaffine nonlinear functions in MASs. The proposed control law consisting of backstepping control, tracking differentiator, and finite-time performance function is adopted for MASs to achieve the desired formation while reaching performance requirements. An auxiliary dynamic compensator is introduced to correct the control deviation caused by input saturation. Lyapunov stability theory is utilized to analyze the stability of the closed-loop system, which guarantees that the formation tracking error can asymptotically converge to an arbitrarily small neighborhood around zero in finite time. Finally, the simulation results show that compared to the adaptive, cooperative learning, and virtual structure methods, the proposed control algorithm has stronger tracking ability and faster setting time (1.8 s) under the influence of nonaffine nonlinear uncertainties. The integral square error for the formation control strategy in this paper is 0.16, which is much smaller than the abovementioned methods and is therefore provided to manifest the validity and feasibility of the proposed control strategy.

A Review of Variable-Beam Divergence Angle FSO Communication Systems

Free-space optical (FSO) communication is widely used in satellites, ships, aircraft, and ground stations due to its advantages of high speed, large capacity, good confidentiality, and strong anti-interference ability. Variable-beam divergence angle (VBDA) technology makes FSO systems more flexible; this has the benefits of higher acquisition probability, stronger tracking ability, wider communication link range, and lower energy consumption. In other words, the study of VBDA has both theoretical significance and practical relevance. This paper conducts a comprehensive search of relevant scientific databases, journals, conference proceedings, patents, and books to identify publications related to VBDA technology. We analyze these publications, classify and organize various VBDA techniques based on their respective methods. To the best of our knowledge, this is the first review of VBDA technology. In this paper, we first explain the basic principle of changing the beam divergence angle by employing the ABCD matrix, and further furnish a detailed overview of the methods used for VBDA along with their corresponding advantages and disadvantages. In addition, we provide a comprehensive summary of the research conducted using VBDA technology across different link types. Lastly, we identify the challenges and potential future research directions for VBDA technology.

Adaptive Pre-Aim Control of Driverless Vehicle Path Tracking Based on a SSA-BP Neural Network

Aiming at the problem that the tracking accuracy of unmanned vehicle path tracking preview control is greatly affected by the preview time, a BP neural network adaptive preview control method is proposed. Considering that the prediction effect of the BP neural network is limited to the initial value setting, a preview time adjuster based on the SSA-BP neural network was established; by establishing the relationship between the front wheel steering angle and the preview time, a new direction control driver model was formed. The driver model and the preview time adjuster together constitute an adaptive steering controller. In order to solve the influence of the longitudinal speed change on the vehicle stability, a PID variable-speed controller was designed to realize the horizontal and vertical coordinated control of the path tracking of the unmanned vehicle. Compared with the fixed preview time and the BP preview time control method, the results show that the proposed method has strong tracking ability when driving at various speeds on three consecutive curves and Alt 3 test roads, and can be used when driving at a variable speed.

Bandwidth Expansion of Atomic Spin Gyroscope With Transient Response

Atomic spin gyroscopes have ultra-high sensitivity on inertial measurement, but the narrow bandwidth limits its application. We describe a bandwidth expansion method for atomic spin gyroscope based on the transient response of electron spin. The gyroscope is modeled as a four inputs two outputs linear state-space system by commonly used linearization. As the transverse component of electron spin polarization decays much faster than that of nuclear spin, a quasi-steady-state approximation on electron spin is proposed to estimate the transverse polarization component of nuclear spin in real-time. In experiment, the system matrix, input matrix, and output matrix of the state-space model are decided with a series of calibrations. A bandwidth expansion method is proposed to solve the rotation input with the transient response of the electron spin. The proposed method shows strong rotation tracking ability compared with the steady-state method. The bandwidth of atomic spin gyroscope is extended and 3 dB/Oct lower signal attenuation in the frequency range of 0.4~5 Hz is obtained with the proposed method.

Research on online parameter identification and SOC estimation methods of lithium‐ion battery model based on a robustness analysis

Under complex working conditions in variable temperatures, the accuracy of SOC is reduced due to the low robustness of the lithium-ion battery model online parameter identification method as well as the SOC estimation approach. Given this problem, a parameter identification method called FF-AGLS (alternative generalized least squares with forgetting factor) is proposed. The proposed method was combined with the robust H∞-CKF (cubature Kalman filter) based on singular value decomposition (SVD) in order to achieve an accurate estimation of lithium-ion battery SOC. FF-AGLS, which adopts unbiased estimation, has strong parameter tracking ability in low temperatures and low SOC regions, as well as high model parameter identification accuracy. As a result, combining the H∞ filter with SVD-CKF can maintain the robustness of the algorithm when the model parameters are uncertain, which may solve issues related to the decrease in SOC estimation accuracy caused by temperature changes. Finally, a series of experiments were conducted on the proposed method at different temperatures, while its performance was verified with the current under different working conditions. Accordingly, the joint algorithm based on FF-AGLS and H∞-SVD-CKF was able to accurately track model parameters and SOC with a strong degree of robustness.

Unified and Self-Stabilized Parallel Algorithm for Multiple Generalized Eigenpairs Extraction

Generalized eigenvalue decomposition has many advantages when it is applied in modern signal processing. Compared with other methods, neural network model-based algorithms provide an efficient way to solve such problems online. Generalized feature extraction algorithms based on neural network models have been described in the literature. However, the majority of the existing algorithms can only extract the principal generalized eigenvector(s) or eigensubspace. To extract principal and minor generalized eigenvectors from two vector sequences, in this paper, two different information criteria are proposed, and a unified algorithm for the extraction of multiple components in a parallel way by simply altering the sign is derived based on these information criteria, which is feasible for generalized principal and minor component analysis. Moreover, all the corresponding principal and minor generalized eigenvalues can be extracted simultaneously because the desired equilibrium point depends on these values. Thus, the proposed algorithm can perform multiple generalized eigenpair extraction. The proposed algorithm possesses four properties: unification, self-stability, parallel extraction and generalized eigenpair extraction, that few of the existing algorithms can encompass. The global convergence and self-stability property of the proposed algorithm are proved through the Lyapunov method and ordinary differential equation method, respectively. The proposed algorithm has a fast convergence speed, high precision and strong tracking ability. Finally, numerical examples and applications are explored to further demonstrate the efficiency of the proposed algorithm.

A Robust Single GPS Navigation and Positioning Algorithm Based on Strong Tracking Filtering

Kalman filter (KF) algorithm is a form of recursive optimal estimations, which utilizes information in time domain with less computational efforts to reduce the system errors. Recently, Kalman filtering technique has become a standard approach for reducing errors in a least squares sense and is widely applied in navigation and positioning fields. Unfortunately, the traditional KF needs accurate statistical information about mobile terminal; otherwise, it will result in lower precision and even divergence. To make up for the deficiency of KF algorithm, this paper presents a novel robust single global position system (GPS) navigation algorithm based on dead reckoning and strong tracking filter (STF), which still has strong tracking ability even when precise knowledge of the system models is not available. The proposed algorithm utilizes adaptive fading factor to adjust the gain matrix in real time to satisfy the orthogonality principle (OP), which indicates all useful information has been extracted from residual. Furthermore, we define residual covariance (RC) as an important index to evaluate the performance of the filtering algorithm and deduce the closed relationship of RCs in KF under the case of: 1) inaccurate system model; 2) erroneous initial value; and 3) abrupt change of states, respectively. We find that the RCs of the KF algorithm under all three above-mentioned cases have a bias compared with desired ones; as a result, they do not conform to the OP and have poor tracking ability, while the RC of STF conforms to the OP due to the fading factor which indicates the tracking performance is greatly improved in the proposed algorithm. The theoretical analysis and simulation results demonstrate that the performance of the proposed single GPS navigation algorithm based on STF outperforms that of the traditional KF algorithm.

Nonlinear Adaptive Predictive Functional Control Based on the Takagi–Sugeno Model for Average Cracking Outlet Temperature of the Ethylene Cracking Furnace

The conventional PID control has been proven insufficient and incapable for this particular petro-chemical process. This paper proposes a nonlinear adaptive predictive functional control (NAPFC) algorithm based on the Takagi–Sugeno (T-S) model for average cracking outlet temperature (ACOT) of the ethylene cracking furnace. In this algorithm, in order to overcome the effect on system performance under model mismatch, the structure parameters of the T-S fuzzy model are confirmed, and the model consequent parameters are identified online using the forgetting factor least-square method. Prediction output is calculated according to the identified parameters instead of computing the Diophantine equation, thereby obtaining directly the predictive control law and avoiding the complex computation of the inverse of the matrix. Application results on ACOT of the ethylene cracking furnace show the proposed control strategy has strong tracking ability and robustness.

Process fault prognosis using a fuzzy‐adaptive unscented Kalman predictor

AbstractBy monitoring the future process status via information prediction, process fault prognosis is able to give an early alarm and therefore prevent faults, when the faults are still in their early stages. A fuzzy‐adaptive unscented Kalman filter (FAUKF)‐based predictor is proposed to improve the tracking and forecasting capability for process fault prognosis. The predictor combines the strong tracking concept and fuzzy logic idea. Similar to the standard adaptive unscented Kalman filter (AUKF) that employs an adaptive parameter to correct the estimation error covariance, a Takagi–Sugeno fuzzy logic system is designed to provide a better adaptive parameter for smoothing this regulation. Compared with the standard AUKF, the proposed FAUKF has the same strong tracking ability but does not suffer from the drawback of serious tracking fluctuation. Two simulation examples demonstrate the effectiveness of the proposed predictor. Copyright © 2011 John Wiley & Sons, Ltd.

An improved fuzzy Kalman filter for state estimation of non-linear systems

The extended fuzzy Kalman filter (EFKF) of non-linear systems which can deal with fuzzy uncertainty effectively has been developed recently. But it seems to be inapplicable to the cases where the states change abruptly or there exist model mismatches in non-linear systems. Therefore, based on the EFKF, a new concept of the improved fuzzy Kalman filter (IFKF) is proposed in this article. Due to the introduction of the extension orthogonality principle given as a criterion to design the new algorithm, the IFKF can track the abrupt changes of the states and has definite robustness against the model mismatches. Finally, computer simulations with a MIMO non-linear model are presented, which illustrate that the proposed IFKF has the strong tracking ability and robustness against the model mismatches.