Search

In this paper, a state estimation method of distributed electric drive articulated vehicle dynamics parameters based on the forgetting factor unscented Kalman filter with singular value decomposition (SVD-UKF) is proposed. The 7DOF nonlinear dynamics model of a distributed electric drive articulated vehicle is established. The unscented Kalman filter algorithm is the foundation, with singular value decomposition replacing the Cholesky decomposition. A forgetting factor is introduced to dynamically adapt the observation noise covariance matrix in real time, resulting in a centralized parameter state estimator for the articulated vehicle. The proposed parameter state estimation method based on the forgetting factor SVD-UKF is simulated and compared with the unscented Kalman filter (UKF) estimation method. Key dynamic parameters are estimated, such as the lateral and longitudinal velocities and accelerations, angular velocity, articulated angle, wheel speeds, and longitudinal and lateral tire forces of both the front and rear vehicle bodies. The results show that the proposed forgetting factor SVD-UKF method outperforms the traditional UKF method. Furthermore, a prototype vehicle test is conducted to compare the estimated values of various dynamic parameters with the actual values, demonstrating minimal errors. This verifies the effectiveness of the proposed dynamic parameter estimation method for articulated vehicles. [ABSTRACT FROM AUTHOR]

In order to minimize the effects of inner ambiguity and outer disturbance of the robotic arm model on the controlled system and to enhance the iterative performance, the paper designs an adaptive iterative control (AILC) method with forgetting factor based on the compensation of extended state observer (ESO). This iterative algorithm for controlling the torque is designed by establishing a dynamic model of a two-jointed robotic arm, and proof of the stability and convergence of the system is given theoretically by using the composite energy function based on Lyapunov function, and the efficacy of the algorithm in this paper is demonstrated by simulations and comparison.

【Objective】With the development of the sixth generation mobile communication technology, the inter-carrier interference in the traditional Orthogonal Frequency Division Multiplexing (OFDM) system makes the channel estimation performance insufficient to provide highly reliable communication, and Orthogonal Time-Frequency Space (OTFS) system can effectively solve the problem of communication system reliability degradation caused by fast time variability and Doppler effect, which has received wide attention in recent years.【Methods】In order to effectively meet the channel estimation performance requirements of OTFS systems, this paper uses an Optimized Generalized Complex Exponential (OGCE) Basis Expansion Model (BEM) to calculate the channel impulse response as a time-invariant basis function with basis function coefficients, which can effectively fit fast time-varying channels in high-speed mobile communication scenarios. The OGCE-BEM improves the spectral leakage by more intensive sampling and reduces the error of the high-frequency basis model by adding correction coefficients to reduce the error of the HF-based model.【Results】The simulation results show that the proposed algorithm is suitable for high-speed mobile communication scenarios with more reasonable design of the basis function. The estimation method has lower mean square error than the fixed forgetting factor, and the channel estimation results are more accurate. Compared with Least Square (LS), BEM-LS and BEM-Linear Minimum Mean Square Error (LMMSE) channel estimation methods, the performance of mean square error is significantly improved.【Conclusion】It can be seen that the channel estimation algorithm based on OGCE-BEM can effectively reduce the number of unknown parameters to be estimated and improve the accuracy of channel estimation.

To investigate the parameter characteristics of permanent magnet synchronous motor (PMSM) speed sensorless vector control system and capture the noise matrices quickly and accurately in the speed estimation process of the extended Kalman filter for PMSM, The recursive least square method with forgetting factor is proposed to determine the actual parameters of the system, and then a new variable-scale chaotic particle swarm optimization (VCPSO) algorithm is put forward to accurately obtain the system noise matrix and the measurement noise matrix. The simulation results show that noise matrix optimization of extended Kalman filter by employing VCPSO algorithm under actual motor parameters is better than those employing standard PSO or chaotic PSO algorithms with faster speed and higher accuracy. [ABSTRACT FROM AUTHOR]

An adaptive model predictive control (AMPC) algorithm based on pavement identification was proposed to determine the influence of different pavement inputs on a vehicle suspension system. First, a vehicle dynamics model was established, and a discrete leveling index-based pavement comfort assessment method was proposed based on the international leveling index to quantify the comfort level by calculating the maximum instantaneous vibration index based on the vertical acceleration of the driver's seat. Second, an augmented Kalman filter algorithm with a forgetting factor is proposed to track the pavement time-varying parameters and estimate pavement leveling. Finally, the control of the quarter-vehicle active suspension system is transformed into solving the decay of hard constraints, designing the AMPC strategy, parameterizing the cost function of AMPC with the global cost of the performance index as the evaluation function, and using Bayesian optimization to predict the time domain and weight of the cost function to achieve global optimal performance. While satisfying the dynamic constraints, passenger comfort is improved by reducing the disturbance of the road pavements. Experimental results indicate that the proposed AMPC algorithm reduces the acceleration, suspension displacement and tire displacement by 26.4%, 10.3% and 8.0%, respectively, compared to the passive suspension when the vehicle speed is 30 km/h. The proposed algorithm reduces 5.6%, 2.7% and 4.2%, respectively, compared to the B-MPC algorithm accordingly. At the speed of 60 km/h, the acceleration of the sprung mass, suspension displacement and tire displacement is reduced by 23.4%, 13.1% and 10.3%, respectively, compared to the passive suspension. The proposed algorithm reduces by 7.8%, 3.0% and 3.7%, respectively, compared to the B-MPC algorithm accordingly. In real vehicle experiment, the acceleration of the sprung mass is reduced by 12.80% and 34.2%, respectively, and the tire displacement is reduced by 3.7% and 7.1%, respectively, compared to the B-MPC and MPC controller, improving the smoothness of the suspension and driver comfort. The effectiveness of the proposed control algorithm for different road pavements was verified. [ABSTRACT FROM AUTHOR]

In order to increase the probability of applying more recent information, a forgetting factor is embedded in the nonmonotone line search technique for minimization of the multiobjective problem concerning the partial order induced by a closed, convex, and pointed cone. The method is shown to be globally convergent without convexity assumption on the objective function. Moreover, to improve behavior of the classical steepest descent method, an accelerated scheme is presented. Ultimately, computational advantages of the algorithms are depicted on a class of standard test problems. [ABSTRACT FROM AUTHOR]

In DNA ploidy analysis technology, in order to eliminate the measurement error of DNA relative content caused by uneven illumination and real-time changes in illumination, this article proposes a method of microscope illumination compensation based on forgetting factor. Firstly, multiple images under the microscope are captured, and the cell pixels in these images are cut off by the block threshold segmentation. Secondly, the illumination deviation template library is constructed through the method of illumination deviation template construction based on cumulative background filling proposed in this article. On this basis, a method of microscope illumination compensation with forgetting factor is proposed, and illumination compensation is completed through this method. Experiments show that the method proposed in this article has a good illumination compensation effect on images with uneven illumination for the lack of blank glass slides. Meanwhile, the real-time change of illumination also leads to a poor illumination compensation effect. The method proposed in this article makes the method illumination compensation more stable and accurate, thereby effectively improving the measurement accuracy of DNA ploidy analysis technology. [ABSTRACT FROM AUTHOR]

Summary: This paper studies the finite‐time tracking problem for nonlinear impulsive differential inclusion systems with randomly varying trial lengths. First, we convert the set‐valued mapping in the differential inclusion systems to single‐valued mapping by a Steiner‐type selector. For the tracking problem of random discontinuous output trajectories, this paper defines a piecewise continuous variable by zero‐order holder to correct the tracking error of segmented continuity. Then, we introduce the average operator with forgetting factor to design three novel learning schemes, and establish convergence results by using the mathematical analysis tools such as impulsive Gronwall inequality and λ$$ \lambda $$‐norm. Finally, a numerical example verifies the validity of the theoretical results, and we compare the tracking performance of the output trajectories for different forgetting factors. [ABSTRACT FROM AUTHOR]

Copyright of Study on Optical Communications / Guangtongxin Yanjiu is the property of Study on Optical Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Basic Sciences Journal of Textile Universities / Fangzhi Gaoxiao Jichu Kexue Xuebao is the property of Basic Sciences Journal of Textile Universities and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, a state estimation method of distributed electric drive articulated vehicle dynamics parameters based on the forgetting factor unscented Kalman filter with singular value decomposition (SVD-UKF) is proposed. The 7DOF nonlinear dynamics model of a distributed electric drive articulated vehicle is established. The unscented Kalman filter algorithm is the foundation, with singular value decomposition replacing the Cholesky decomposition. A forgetting factor is introduced to dynamically adapt the observation noise covariance matrix in real time, resulting in a centralized parameter state estimator for the articulated vehicle. The proposed parameter state estimation method based on the forgetting factor SVD-UKF is simulated and compared with the unscented Kalman filter (UKF) estimation method. Key dynamic parameters are estimated, such as the lateral and longitudinal velocities and accelerations, angular velocity, articulated angle, wheel speeds, and longitudinal and lateral tire forces of both the front and rear vehicle bodies. The results show that the proposed forgetting factor SVD-UKF method outperforms the traditional UKF method. Furthermore, a prototype vehicle test is conducted to compare the estimated values of various dynamic parameters with the actual values, demonstrating minimal errors. This verifies the effectiveness of the proposed dynamic parameter estimation method for articulated vehicles.

The conventional parameter identification process generally assumes that parameters remain constant. However, under extreme loading conditions, structures may exhibit nonlinear behavior, and parameters could demonstrate time‐variant characteristics. The unscented Kalman filter (UKF), as an efficient online recursive estimator, is widely used for identifying parameters of nonlinear systems. Nevertheless, it exhibits limitations when attempting to identify time‐variant parameters. To address this issue, this paper proposes a covariance matching technique that produces an array of adaptive UKF algorithms. Firstly, the sensitivity parameter η is defined to identify the instant when the parameter change occurs, and its threshold is calculated based on the sensitivity parameter time history curve. Secondly, an adaptive forgetting factor is introduced to simultaneously update the innovation, cross, and state covariance matrices when the kth‐step sensitive parameter surpasses the threshold. Finally, a secondary correction forgetting factor (SCFF) is employed to further re‐update the state covariance values at the identified damage locations. This creative step enhances the adaptive capability and optimizes the identification accuracy of the proposed algorithms. Both the numerical simulations and shaking table test demonstrate that the proposed adaptive algorithms can efficiently identify the time‐variant stiffness‐type parameters, and accurately capture their time‐variant characteristics. [ABSTRACT FROM AUTHOR]

Nonmonotonicity has been considered to be essentially influential on the efficiency of the iterative procedures of nonlinear optimization. A review of the literature shows that the objective function values available from recent iterations provide worthier information in the nonmonotone schemes. So, with the aim of enhancing probability of applying more recent available function values, a nonmonotone trust region ratio is suggested using a forgetting factor. Meanwhile, modification of a recent adaptive formula for the trust region radius is devised by a nonmonotone reflection as well. Then, based on the two mentioned modifications, an adaptive nonmonotone trust region algorithm is given. In addition, convergence of the method is analysed under classic assumptions. To provide support for our theoretical arguments, computational merits of the given algorithm on a set of CUTEr test functions are depicted. [ABSTRACT FROM AUTHOR]

In today's world, energy is undoubtedly one of the most significant problems. As the global electricity consumption continues to increase, electric motors, which are widely used as power devices, account for an increasingly prominent proportion of the energy consumed. Motors now consume about 45% of the total electricity in the world (60% in China); therefore, improving motor efficiency has become an important way to achieve carbon emission reduction and sustainable development. The aim of this research was to devise a new strategy to reduce CO2 emissions other than by building green power factories, because even the building of green power factories produces a great deal of CO2 emissions, and improving motor efficiency to reduce CO2 emissions could contribute to sustainable development worldwide. However, the improvement of motor efficiency encounters challenges, such as nonlinearity and disturbances, which affect the motor performance and energy efficiency. To address this issue, this paper proposes a control algorithm for permanent-magnet synchronous motors (PMSMs) that is highly efficient and would be most widely used based on a fuzzy control adaptive forgetting factor. It aims to enhance the efficiency and accuracy of the online parameter estimation for the PMSM flux linkage, thereby achieving more precise and energy-efficient motor control. Firstly, the recursive least-squares parameter estimation algorithm is used to identify the parameters of the PMSM. This ensures that the parameter estimation values can be dynamically updated with data changes, adapting to the time-varying parameters. Secondly, the Padé approximation method is adopted, which is a method that does not depend on the motor hardware, to improve the accuracy of the linearized model of the motor. Finally, a control algorithm based on the fuzzy control adaptive forgetting factor algorithm is constructed on a physical experimental platform. A comparison of these results proves that the control technology under this algorithm provides a new energy-saving control strategy that can estimate the motor flux linkage parameters more accurately, help to reduce energy consumption, promote the use of clean energy, and achieve sustainable performance optimization. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Structural damage assessment under external loading, such as earthquake excitation, is an important issue in structural safety evaluation. In this regard, an appropriate data analysis and system identification technique is required to interpret the measured data and to identify the state of the structure. Generally, the recursive system identification algorithm is used. In this study, the recursive subspace identification (RSI) algorithm based on the matrix inversion lemma algorithm with oblique projection technique (RSI-Inversion-Oblique) is applied to investigate the time-varying dynamic characteristics. The user-defined parameters used in the RSI-Inversion-Oblique technique are carefully discussed, which include the size of the data Hankel matrix (i), model order to extract the physical modes, and forgetting factor (FF) to detect the time-varying system modal frequencies. Response data from the Northridge earthquake from the Sherman Oaks building (CSMIP) is used as an example to examine a systematic method to determine the suitable user-defined parameters in RSI. It is concluded that the number of rows in the data Hankel matrix significantly influences the identification of the time-varying fundamental modal frequency of the structure. An algorithmic model order selection method using the eigenvalue distribution of RSI-Inversion can detect the system modal frequencies at each appending data window without causing any abnormality.

This paper presents a data-driven adaptive steady state-integral-derivative (SS-ID) control algorithm that uses gradient descent and recursive least squares (RLS) with a forgetting factor. A simplified first-order differential equation of the control system was designed and its parameters were estimated in real-time using the RLS algorithm. The steady-state control input for target-state tracking was derived based on the estimated parameters and steady-state performance conditions. The gradient of the integrated control error to the gain was estimated based on the least-squares method, using the saved past error and gain data in a finite sliding window to determine the control input. The integral gain was adapted based on the gradient descent method, using the estimated gradient, integrated error, and adaptation rate. Simplified control error dynamics were designed, and their parameter was estimated using the RLS algorithm. The derivative control gain can be adapted in real time using the estimated parameters from the simplified control error dynamics and time constant-based performance conditions. The proposed controller was designed in the MATLAB/Simulink environment. A performance evaluation was conducted under various scenarios using a DC motor simulation model and an actual test platform equipped with an optical encoder.

Founded upon a sparse estimation of the Hessian obtained by a recent diagonal quasi-Newton update, a conjugacy condition is given, and then, a class of conjugate gradient methods is developed, being modifications of the Hestenes–Stiefel method. According to the given sparse approximation, the curvature condition is guaranteed regardless of the line search technique. Convergence analysis is conducted without convexity assumption, based on a nonmonotone Armijo line search in which a forgetting factor is embedded to enhance probability of applying more recent available information. Practical advantages of the method are computationally depicted on a set of CUTEr test functions and also, on the well-known signal processing problems such as sparse recovery and nonnegative matrix factorization. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive generalized predictive control for dynamic positioning of ships with unknown model parameters and unknown time-varying environmental disturbances. The controlled auto-regressive integrated moving average (CARIMA) model is employed to describe the movements of ships with model parameter and disturbance uncertainties. The recursive least square method with forgetting factor is utilized to design adaptive laws to estimate the CARIMA model parameters online. Considering both the positioning accuracy and the change intensity of control increments, a cost function is constructed. The optimal control increments that minimize the cost function are obtained by using Lagrange multiplier method. Then, the adaptive control law is designed to maintain ship's position and heading at desired values. Finally, simulation studies in different cases are carried out and simulation results demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Fractional differential equations are used to construct mathematical models and can describe the characteristics of real systems. In this paper, the parameter estimation problem of a fractional Wiener system is studied by designing linear filters which can obtain smaller tunable parameters and maintain the stability of the parameters in any case. To improve the identification performance of the stochastic gradient algorithm, this paper derives two modified stochastic gradient algorithms for the fractional nonlinear Wiener systems with colored noise. By introducing the forgetting factor, a forgetting factor stochastic gradient algorithm is deduced to improve the convergence rate. To achieve more efficient and accurate algorithms, we propose a multi-innovation forgetting factor stochastic gradient algorithm by means of the multi-innovation theory, which expands the scalar innovation into the innovation vector. To test the developed algorithms, a fractional-order dynamic photovoltaic model is employed in the simulation, and the dynamic elements of this photovoltaic model are estimated using the modified algorithms. Concurrently, a numerical example is given, and the simulation results verify the feasibility and effectiveness of the proposed procedures. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive model predictive fault-tolerant control (FTC) algorithm based on sensitivity estimation and exponential forgetting-based recursive least squares (RLS) for four-wheel independent steering vehicles. The model predictive control algorithm was designed according to physical constraints for four-wheel independent steering control with adaptive integral action. To improve the control performance in transient and steady-state regions, sensitivity-based adaptive rules for the weighting factor of the model predictive controller and integral gain were developed using the gradient descent method. The sensitivity was defined by a virtual relationship function and was estimated using RLS with a forgetting factor. Additionally, a FTC strategy with the equality constraint was proposed for enhancing the yaw-rate tracking control performance despite the existence of faults in the steering system. The proposed fault-tolerant steering control algorithm was developed in a MATLAB/Simulink environment, and its performance was evaluated via co-simulation in the MATLAB/Simulink and CarMaker software programs under various evaluation scenarios. [ABSTRACT FROM AUTHOR]

In view of the underwater manipulator system with more uncertainty and random interference, the effect of the underwater environment on the dynamic model and impedance controller is analyzed, and an impedance controller based on environmental parameter identification is constructed to solve the force tracking problem of underwater compliant grasping. The environmental stiffness is identified online by recursive least squares with the introduction of a forgetting factor, so as to correct the reference position. The steady-state error of the contact force is reduced, and real-time and accurate force tracking is achieved. Simulation with the MATLAB/Simulink are carried out. It is shown that the controller can quickly achieve tracking of the desired force and real-time estimation of contact stiffness under the introduction of continuous external interference force and desired force adjustment, and has good robustness. Finally, experiments prove that the controller has fast and stable force tracking performance in real underwater compliant grasping tasks. Furthermore, the average steady-state force tracking deviation is about 0.014 N, and the maximum force tracking deviation is about 0.138 N. [ABSTRACT FROM AUTHOR]

This paper considers the identification problem of the linear continuous time-delay systems. By using the multi-frequency responses, a stochastic gradient gradient-based iterative (SG-GI) algorithm is derived. The proposed algorithm can estimate the unknown parameters and the unknown time delays simultaneously. To improve the parameter estimation accuracy of the SG-GI algorithm, a multi-innovation stochastic gradient gradient-based iterative (MISG-GI) algorithm is derived by using the multi-innovation identification theory. In addition, a forgetting factor is introduced to increase the parameter estimation accuracy. The resulting algorithm is called the multi-innovation forgetting gradient gradient-based iterative (MIFG-GI) algorithm. The effectiveness of the proposed strategies is illustrated by a numerical example. [ABSTRACT FROM AUTHOR]

A modified heading rate active disturbance rejection controller (ADRC) for miniature unmanned helicopters is presented to improve the transient performance and adaptability of working conditions. First, a tail-locking mathematical model is introduced, and the amplification factor is defined. Second, a standard ADRC controller is presented. Because the amplification factor plays an important role in both parts of the content and is primarily influenced by the main rotor speed, an online forgetting factor recursive least square algorithm is used to identify it, and the identification result is condensed into a function of the main rotor speed, adapting to various working conditions. This function is also included in the proposed ADRC controller to supplement the standard scheme. Finally, experiments were conducted on a small electric helicopter. A reduction of approximately 40% in the transient time (compared with an off-the-shelf PID controller) was achieved in the experiment. The comparative results show that the proposed ADRC scheme outperforms the classic PID and standard ADRC schemes in terms of transient performance and adaptability to working conditions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Improving maintenance strategies is an important component in reducing wastes of energy in industrial systems and especially energy systems, as they represent a large proportion of our total energy use. The use of fault detection methods can help transition to proactive maintenance methods and automate the detection of faults using data from the systems themselves. However, the presence of normal operational changes must be accounted for to avoid false alarms, as static fault detection methods cannot handle them. A recursive fault detection method, specifically recursive principal component analysis (RPCA), is applied to real building data in this paper to aid in addressing this challenge. Additionally, improvements to the aspect of updating forgetting factors, which are integral to RPCA's functionality, is also proposed which allows for enhanced adaptability. The results show that the improvement increases adaptability when setpoints are changed and provides similar performance otherwise. Lastly, the application of this was shown to significantly reduce false alarms in the building application, while still detecting the known faults. [ABSTRACT FROM AUTHOR]

Due to the advantages of high convergence accuracy, fast training speed, and good generalization performance, the extreme learning machine is widely used in model identification. However, a gas turbine is a complex nonlinear system, and its sampling data are often time-sensitive and have measurement noise. This article proposes an online sequential regularization extreme learning machine algorithm based on the forgetting factor (FOS_RELM) to improve gas turbine identification performance. The proposed FOS_RELM not only retains the advantages of the extreme learning machine algorithm but also enhances the learning effect by rapidly discarding obsolete data during the learning process and improves the anti-interference performance by using the regularization principle. A detailed performance comparison of the FOS_RELM with the extreme learning machine algorithm and regularized extreme learning machine algorithm is carried out in the model identification of a gas turbine. The results show that the FOS_RELM has higher accuracy and better robustness than the extreme learning machine algorithm and regularized extreme learning machine algorithm. All in all, the proposed algorithm provides a candidate technique for modeling actual gas turbine units. [ABSTRACT FROM AUTHOR]

This paper presents a sliding mode control (SMC)-based path-tracking algorithm for autonomous vehicles by considering model-free adaptive feedback actions. In autonomous vehicles, safe path tracking requires adaptive and robust control algorithms because driving environment and vehicle conditions vary in real time. In this study, the SMC was adopted as a robust control method to adjust the switching gain, taking into account the sliding surface and unknown uncertainty to make the control error zero. The sliding surface can be designed mathematically, but it is difficult to express the unknown uncertainty mathematically. Information of priori bounded uncertainties is needed to obtain closed-loop stability of the control system, and the unknown uncertainty can vary with changes in internal and external factors. In the literature, ongoing efforts have been made to overcome the limitation of losing control stability due to unknown uncertainty. This study proposes an integrated method of adaptive feedback control (AFC) and SMC that can adjust a bounded uncertainty. Some illustrative and representative examples, such as autonomous driving scenarios, are also provided to show the main properties of the designed integrated controller. The examples show superior control performance, and it is expected that the integrated controller could be widely used for the path-tracking algorithms of autonomous vehicles. [ABSTRACT FROM AUTHOR]

The parameter estimation methods for the nonlinear exponential autoregressive model are investigated in this paper. We develop a forgetting factor gradient parameter estimation algorithm for improving the estimation accuracy. For the purpose of improving the identification accuracy further, a forgetting factor multi-innovation stochastic gradient algorithm is derived by using the multi-innovation theory. The effectiveness of the proposed algorithms is proved by a simulation example. [ABSTRACT FROM AUTHOR]

In this paper, a new filter referred to as the alpha sliding innovation filter (ASIF) is presented. The sliding innovation filter (SIF) is a newly developed estimation strategy that uses innovation or measurement error as a switching hyperplane. It is a sub-optimal filter that provides a robust and stable estimate. In this paper, the SIF is reformulated by including a forgetting factor, which significantly improves estimation performance. The proposed ASIF is applied to several systems including a first-order thermometer, a second-order spring-mass-damper, and a third-order electrohydrostatic actuator (EHA) that was built for experimentation. The proposed ASIF provides an improvement in estimation accuracy while maintaining robustness to modeling uncertainties and disturbances. [ABSTRACT FROM AUTHOR]

For the regression problem of support vector machine, the solution processes of the most existing methods use offline datasets, which cannot be realized online. For this problem, this paper presents a new online approach to identify these unknown parameters contained in the support vector machine. A new cost function is constructed by substituting the error term into the standard cost function, which is different from the standard support vector machine, and the gradient descent approach is then used to minimize the newly created loss function, thus proposing a stochastic gradient support vector machine algorithm to estimate the unknown parameters based on the recursive identification methods. Furthermore, to advance the property of the stochastic gradient support vector machine algorithm, a moving data window is used to widen the scalar information into a fixed-length innovation vector, thereby increasing the amount of information used in the parameter estimation based on the multi-innovation identification theory. In addition, the forgetting factor is brought into the proposed algorithms, and the corresponding forgetting factor recursive algorithms are derived. These methods are recursive identification methods, which may be implemented online and are more efficient in terms of computing. Finally, utilizing the MatLab platform, the validity and usefulness of the explored methodologies are proven using several numerical simulation examples. • Present a stochastic gradient support vector machine algorithm by a recursive scheme. • Present a multi-innovation stochastic gradient support vector machine algorithm. • Use the forgetting factor to improve the performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Formation control of multi-USV system in complex marine environments is investigated in this study, and a data-driven disturbance observer (DDO)-based model free adaptive control (MFAC) framework is suggested. The FMAC scheme is designed by using only the input and output data of complex USV dynamics, including the calculation of pseudo-Jacobian matrix (PJM) and the calculation of control law. To avoid the problem that the MFAC method cannot be directly applied to USV systems, an improved DDO is used to estimate the complex environmental disturbances and PJM estimation errors. Further, forgetting factor based MFAC (FMFAC) is proposed to avoid overshoot caused by redundant data. Then, the PJM estimation is proved to be accurate while the control structure of DDO-FMFAC is proved to be bounded-input and bounded-output (BIBO). Finally, the performances of the proposed method including effectiveness and robustness are shown in simulation results. [ABSTRACT FROM AUTHOR]

This paper carries out marine vehicle maneuverability prediction based on nonlinear innovation. An improved Extended Kalman Filter (EKF) algorithm combined with a forgetting factor is developed by virtue of nonlinear innovation for ship maneuverability using full-scale data. Compared with existing algorithms, the proposed algorithm has high prediction consistency, a good prediction effect, and takes a shorter time to reach the agreement. Furthermore, the real-time prediction data are more than 95% consistent with the actual ship navigation. The forgetting factor is introduced to reduce the cumulative impact of historical interference data. Then, the tangent function is used to process errors; this can solve the problem of inaccurate maneuvering prediction of traditional identification algorithms, making up for the limitations of existing methods. The real-time prediction results are compared with the full-scale data, showing that the proposed ship prediction model has significant prediction accuracy and that the algorithm is reliable. This parameter identification method can be used to establish ship maneuvering prediction models. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The online error compensation of the three-axis magnetic sensor is the key to a high accuracy geomagnetic assisted navigation. Traditionally, the least squares or its improved methods are used for error compensation, which are offline and need to assume that measurement errors are Gaussian. In this paper, the geomagnetic measurement error is modeled based on the error source analysis, and then the compensation parameters are converted through the ellipsoid hypothesis. To eliminate the negative effect of abnormal values, iterative least squares is designed to calculate static compensation parameters and corresponding residuals, then different weights are assigned to each geomagnetic measurement data through robust estimation. Also, to avoid data saturation phenomenon and finally complete online error compensation, a forgetting factor is introduced into the iterative least squares, and the dynamic coefficients are constructed by weights of measurement data to derive the online updated compensation parameters. Simulation and experiment results both show that the online combined compensation method possesses higher adaptability, which can eliminate the influence of abnormal data effectively. And its accuracy and stability of error compensation are better than other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

State space models (SSMs) are important for multi-variable performance analysis and controller design of aero-engines. In order to solve the problems of the traditional state space modeling methods that rely on component-level models (CLMs) and cannot be carried out in real time, an aero-engine state space modeling method based on adaptive forgetting factor online sequential extreme learning machine (AFOS-ELM) is proposed in this paper. The structure of the extreme learning machine (ELM) is determined according to the form of the state space model, and the inverse-free ELM algorithm is used to automatically select the appropriate number of hidden nodes to improve the efficiency of offline initialization. The focus of the ELM on current operation performance is enhanced by the adaptive renewed forgetting factor, which reduces the impact of aero-engine history and deviated data on the current output and improves the accuracy of the model. Then, according to the analytical equation of the ELM model, the state space model of an aero-engine at each sampling time is obtained by using the partial derivative method. The simulation results based on engine test data show that the real-time performance and accuracy of the state space model established online in this paper can meet the needs of aero-engine control system requirement. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ballistics / Dandao Xuebao is the property of Journal of Ballistics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problem that the noise characteristics of high-speed spinning lying bodies can not be accurately acquired during operation, an improved adaptive extended Kalman filter (AEKF) algorithm is proposed to adaptively adjurt the measurement noise, and an exiended Kalman filter (EKF) algorithm based on a new modeiing method of state variables is proposed based on the EKF to improve the real-time pefformance of the algorithm. The Beidou/strapdown inertial navigation system (SINS) integrated navigation scheme is adopted, and on the baiis of EKF, a noise estimator with forge-ting factor is introduced, and the integrated navigation data is fused through AEKF to estimate the measurement noise. The simulation results show that the proposed integrated navigation method has smaller poritioning errors for the altitude and porition of high-speed spinning lying objects, and has better convergence than the unmodified AEKF. [ABSTRACT FROM AUTHOR]

The RSOR algorithm is a recursive algorithm that has been proposed as an alternative to the RLS algorithm for updating adaptive filter parameters. As with other algorithms, the forgetting factor, filter length and relaxation parameter significantly affects the performance of the RSOR algorithm. In this study, using an adaptive FIR filter in system identification mode, the effect of forgetting factor, filter length and relaxation parameter on the leakage phenomenon of the RSOR algorithm was analyzed. For this purpose, firstly, the effect of measurement noise on the adaptive filter output, namely the leakage phenomenon, was explained analytically, and then the influence of the forgetting factor and other filter parameters on this leakage phenomenon was examined. The results obtained from the simulation studies are compared with similar algorithms.

This paper proposes a new variant of the projection approximation subspace tracking (PAST) algorithm with multiple deflation (MD) and its efficient hardware architecture. It extends the PAST with deflation (PAST-d) algorithm by performing multiple deflations at each step and relies on a recently introduced variable forgetting factor, and variable regularized PAST algorithm to improve the overall convergence rate, steady-state error, and numerical properties. It shares the same simple hardware structure of the PAST-d algorithm in pipeline realization but offering a more flexible tradeoff between complexity and performance. Moreover, methods for estimating the eigenvalues and the dimension of the signal subspace are proposed. Novel simplifications of the proposed variable forgetting factor (VFF) and variable regularization (VR) PAST-MD algorithm are also developed to avoid the expensive cubic root and division operations involved to facilitate its hardware implementation. Moreover, a combined data-regularization update is introduced to avoid the additional QR decomposition (QRD) update associated with the regularization, at the expense of very slight performance degradation. A novel pipelined hardware implementation of the simplified VFF-VR-PAST-MD algorithm based on the QRD and the COordinate Rotation DIgital Computer (CORDIC) is also proposed and implemented in Xilinx field programmable gate array (FPGA). Thanks to the proposed “root- and division- free” schemes, our proposed architecture can achieve around 20.2% higher working speed and save 1.9% lookup tables (LUTs), 1.8% slice register, and 22.8% digital signal processors (DSPs) over conventional implementation of the proposed architecture. Compared to the previous work, which is based on PAST-d algorithm, the proposed QRD-based algorithms offer better performance and a more flexible tradeoff between hardware resources and performance.