Filtering Error Research Articles

Improved finite-time command filtered backstepping fault-tolerant control for flexible hypersonic vehicle

The tracking control of a flexible hypersonic vehicle with system perturbation and actuator fault is investigated in this paper. A control-oriented model is constructed, which is composed of velocity loop and altitude loop. A double-layer fast adaptive-gain super-twisting disturbance observer (ASTDO) is designed to provide finite-time estimation of the lumped terms of external disturbances and actuator faults. The ASTDO has the property of the non-overestimation of adaptive gains and chattering reduction with higher precision. Based on the estimation of ASTDO, a novel finite-time command filtered backstepping (FCFB) scheme is proposed for the high-order altitude loop. The merit lies in the modified compensating signals and new virtual commands to compensate the command filter errors. The remarkable features of the developed algorithm include the finite-time convergence with higher accuracy and the chattering suppression. Finally, comparative simulation results are given to demonstrate the superiority of the proposed method.

Improved Calibration of Numerical Integration Error in Sigma-Point Filters

The sigma-point filters, such as the unscented Kalman filter, are popular alternatives to the ubiquitous extended Kalman filter. The classical quadrature rules used in the sigma-point filters are motivated via polynomial approximation of the integrand; however, in the applied context, these assumptions cannot always be justified. As a result, a quadrature error can introduce bias into estimated moments, for which there is no compensatory mechanism in the classical sigma-point filters. This can lead in turn to estimates and predictions that are poorly calibrated. In this article, we investigate the Bayes-Sard quadrature method in the context of sigma-point filters, which enables uncertainty due to quadrature error to be formalized within a probabilistic model. Our first contribution is to derive the well-known classical quadratures as special cases of the Bayes-Sard quadrature method. Based on this, a general-purpose moment transform is developed and utilized in the design of a novel sigma-point filter, which explicitly accounts for the additional uncertainty due to quadrature error.

EBST: An Evolutionary Multi-Objective Optimization Based Tool for Discovering Potential Biomarkers in Ovarian Cancer.

Ovarian cancer is the deadliest gynecologic malignancy, mainly due to limitations in early diagnosis. With advances in high-throughput technologies, research interest in identifying novel and customized tumor biomarkers for early detection and diagnosis is rapidly growing. Here we introduce a new tool called EBST to select microRNAs with biomarker potency in ovarian cancer. This tool has pre-processing options and Its core is the use of Modified Multi Objective Imperialist Competitive Algorithm and six objective functions based on the classifier performance/structure evaluation, clustering error and mRMR filter. In this paper, we used the FDR filter in the pre-processing stage and considered five objective functions, four of which relate to the l1-SVM classifier performance and one to the average mRMR ranking. The proposed method has identified 11 microRNAs including hsa-miR-6784-5p, hsa-miR-1228-5p, hsa-miR-8073, hsa-miR-6756-5p, hsa-miR-1307-3p, hsa-miR-4697-5p, hsa-miR-3663-3p, hsa-miR-328-5p, hsa-miR-1228-3p, hsa-miR-6821-5p, hsa-miR-1268a. Data classification by the proposed model showed 100 percent sensitivity, 99.38 percent specificity, 99.69 percent accuracy and 99.39 percent positive predictive value. In comparison with routine state-of-the-art methods, superiority of our method was confirmed. The biological evaluation of selected microRNAs using bioinformatics tools and published articles confirms their role in cancer signaling pathways. The tool and its MATLAB code are freely available at https://github.com/hanif-y.

Distributed resilient filtering of large-scale systems with channel scheduling

This paper addresses the distributed resilient filtering for discrete-time large-scale systems (LSSs) with energy constraints, where their information are collected by sensor networks with a same topology structure. As a typical model of information physics systems, LSSs have an inherent merit of modeling wide area power systems, automation processes and so forth. In this paper, two kinds of channels are employed to implement the information transmission in order to extend the service time of sensor nodes powered by energy-limited batteries. Specifically, the one has the merit of high reliability by sacrificing energy cost and the other reduces the energy cost but could result in packet loss. Furthermore, a communication scheduling matrix is introduced to govern the information transmission in these two kind of channels. In this scenario, a novel distributed filter is designed by fusing the compensated neighboring estimation. Then, two matrix-valued functions are derived to obtain the bounds of the covariance matrices of one-step prediction errors and the filtering errors. In what follows, the desired gain matrices are analytically designed to minimize the provided bounds with the help of the gradient-based approach and the mathematical induction. Furthermore, the effect on filtering performance from packet loss is profoundly discussed and it is claimed that the filtering performance becomes better when the probability of packet loss decreases. Finally, a simulation example on wide area power systems is exploited to check the usefulness of the designed distributed filter.

Fault-tolerant H∞ filtering for fuzzy networked control systems with quantisation effects

ABSTRACTThe issue of memory filter design is investigated for a class of discrete-time Takagi–Sugeno (T-S) fuzzy networked control systems with time-varying delay and quantisation effects. The main objective of this paper is to formulate an memory filter that can guarantee the resulting filtering error system to be finite-time bounded. Further, by considering the communication limitations, the state signals are quantised before transmission through a logarithmic quantiser and the quantisation error is considered in the filter design. In particular, the quantisation effects are dealt by employing the generalised sector bound approach. Based on Lyapunov stability theory, novel sufficient constraints are established in the form of linear matrix inequalities, to ensure the finite-time boundedness of the augmented filtering error system. Then, a new fault-tolerant memory filter is formulated and also the filter gain parameters are attained by solving the derived LMI based constraints. Finally, a numerical example is furnished to support the efficacy and applicability of the developed filter design.

Outlier-robust Kalman filters with mixture correntropy

We consider the robust filtering problem for a nonlinear state-space model with outliers in measurements. To improve the robustness of the traditional Kalman filtering algorithm, we propose in this work two robust filters based on mixture correntropy, especially the double-Gaussian mixture correntropy and Laplace-Gaussian mixture correntropy. We have formulated the robust filtering problem by adopting the mixture correntropy induced cost to replace the quadratic one in the conventional Kalman filter for measurement fitting errors. In addition, a tradeoff weight coefficient is introduced to make sure the proposed approaches can provide reasonable state estimates in scenarios where measurement fitting errors are small. The formulated robust filtering problems are iteratively solved by utilizing the cubature Kalman filtering framework with a reweighted measurement covariance. Numerical results show that the proposed methods can achieve a performance improvement over existing robust solutions.

H∞ Filter Design for Networked Control Systems: A Markovian Jump System Approach

This paper puts forward a method to design the H∞ filter for networked control systems (NCSs) with time delay and data packet loss. Based on the properties of Markovian jump system, the packet loss is treated as a constant probability independent and identically distributed Bernoulli random process. Thus, the stochastic stability condition can be acquired for the filtering error system, which meets an H∞ performance index level γ. It is shown that, by introducing a special structure of the relaxation matrix, a linear representation of the filter meeting an H∞ performance index level for NCSs with time delay and packet loss can be obtained, which uses linear matrix inequalities (LMIs). Finally, numerical simulation examples demonstrate the effectiveness of the proposed method.

Adaptive Finite-Time Containment Control of Uncertain Multiple Manipulator Systems.

This article is concerned with the containment control of multiple manipulators with uncertain parameters. A novel distributed adaptive backstepping strategy is given in the finite-time control framework. The finite-time command filters (FTCFs) used in the strategy can avoid the explosion of complexity problem for conventional backstepping. To further improve the control performance, the filtering errors caused by the used FTCFs are removed by using the error compensation mechanism (ECM). The proposed virtual control signal, the control torque, and the adaptive updating law can guarantee the set tracking errors converge to an adjustable neighborhood of the origin in finite time in the presence of uncertain parameters. Because the virtual control signal and ECM only use the local information, the established method is completely distributed. Two simulation examples are given to show the effectiveness of the proposed scheme.

특징점 배치의 기하학적 유사성을 이용한 GS-RANSAC

특징점 배치의 기하학적 유사성을 이용한 GS-RANSAC

Prescribed Performance Adaptive Backstepping Control for Winding Segmented Permanent Magnet Linear Synchronous Motor

In this paper, a prescribed performance adaptive backstepping control (PPABC) strategy is proposed to control the speed of a winding segmented permanent magnet linear synchronous motor (WS-PMLSM) with variable parameters and an unknown load disturbance. Firstly, a mathematical model of WS-PMLSM is provided. Then, the prescribed performance technique is introduced in the adaptive backstepping control to improve the transient performance and ensures the tracking error converges within a predetermined range. In addition, a constrained command filter is introduced to address the problem of differential expansion which exists in the traditional backstepping method, and a filter compensation signal is designed against the filter error. Moreover, the adaptive law is designed based on Lyapunov stability theory to estimate the uncertainties caused by parameter changes and load disturbances. The stability of the proposed control strategy is given and the simulation of the control system is carried out under the proposed PPABC in contrast with another backstepping control and traditional PI control. Finally, the experiment is conducted to further show the effectiveness of the proposed controller.

Dynamic surface control for nonlinear fractional-order chaotic systems with time delays

Due to the uncertainty and randomness, the chaotic behavior of nonlinear system has become an important factor affecting the stability of the system. Therefore, it is very necessary to ensure that the nonlinear system has strong robustness against the external stochastic interference. This paper presents a new dynamic surface controller for nonlinear systems with time delays. In order to be closer to the actual engineering complex operation environment, the fractional calculus operator is added in the system. The dynamic surface control and time-delay effect is first applied to the second-order nonlinear fractional-order system. To deal with the problem of number explosion and time-delay in traditional inversion methods, a fractional-order filter and an alpha-order Pade approximation method are designed. After fully considering the tracking error, virtual control error and filtering error, the dynamic surface controller is established and its stability is verified. It can be seen from the simulation results that the system tracks the set function trajectory in finite time, and the controller has a good effect.

Dissipative Fuzzy Filtering for Nonlinear Networked Systems With Limited Communication Links

This paper aims to design a dissipative fuzzy filter for a class of discrete-time nonlinear networked systems. In order to adopt the limited communication links, we employ an event-triggered scheme to reduce the number of transmitted data in the network by preventing the unnecessary ones from releasing. Due to the digital channel, the data to be transmitted should be quantized and a logarithmic quantizer is employed. Then, when the part of released data is transmitted in the network, data losses is captured by a Bernoulli process. Consequently, the uncomplete data sequence is compensated by the buffer and then send to the filter. During this process, a new random series is developed to help constructing the filtering systems. Thus, a novel method is presented to guarantee the filter error system to be dissipative based on the T–S fuzzy model approach. Finally, an example concerned with Henon mapping system is provided to verify the validity of the proposed design method.

Integrated Prediction Error Filter Analysis Application On Lake Towuti

Every naturally-occurred phenomenon on earth is related to cyclicity. On a larger scale, it can be defined as the occurrence of climate periodicity which is caused by the revolution of earth towards the sun. It can also be examined on a smaller process such as the days and nights cycles, as the effect of earths rotation. This research will specifically discuss about the cyclicity of grain size changes in sediments from lake Towuti, Indonesia. The cyclicity of the sediments is deduced using Integrated Prediction Error Filter Analysis (INPEFA) trends. The INPEFA trends are used to analyse the probability of sediments distribution by simply calculating the cumulative errors between predicted and actual data. Unlike any other implementation of INPEFA that mainly observing lithology controlled by sea level changes, this research is aimed at applying INPEFA to enhance well correlation process across an area that is strongly influenced by rainfall intensity and some climatically-driven processes. By correlating the sediment units, the lateral distribution of the climatically-driven diatom ooze will eventually help a better understanding of paleoclimate events on lake Towuti. This research is aimed at constructing and applying systematic algorithm on INPEFA logs calcultation. Two main cores that construct the INPEFA calculation are trend removal process and autoregressive coefficients calculation using Burgs method. When dealing with real datasets the trend removal process is an imperative process to prevent ambiguous INPEFA trend. Moreover, the use of trend removal process is also favourable in interpreting INPEFA trends for various cyclicity periods, this is achieved by varying the input parameters on the trend removal process. Autoregressive coefficients calculation on the other hand is the keystone that constructs the INPEFA logs calculation process. Well correlations process is successfully achieved through interpreting the INPEFA trends logs. Validation of the INPEFA logs shows good correlation between the result and core sample from lake Towuti with widely-distributed tephra being the main key validator. The changes in INPEFA trends is interpreted to be linked with the change in grain size and also in sediments impedance. Comparing and validating the INPEFA trends with two seismic traces from the lake reveals that the turning point of INPEFA trends are associated with strong reflection on the seismic traces. We approach the building of pseudo-INPEFA section through applying optimum Wiener filter (OWF) during the multi-attribures analysis. The lateral continuation of predicted pseudo-log was improved, overall correlation showed an increase by 15% and a decreased in error value by 25%.

Adaptive neural finite-time bipartite consensus tracking of nonstrict feedback nonlinear coopetition multi-agent systems with input saturation

This paper concentrates on the adaptive neural finite-time bipartite consensus tracking of nonstrict feedback nonlinear coopetition multi-agent systems with input saturation. A novel consensus tracking method combined the adaptive neural control with the finite-time command filtered backstepping is proposed. During each backstepping process, the Radical Basis Function Neural Network (RBF NN) is used to approximate the unknown nonlinear dynamics and the finite-time sliding mode differentiator (FTSMD) is used to obtain intermediate signals and their derivative. Moreover, the filtering errors are eliminated by using error compensation signals. By using the finite-time Lyapunov stability theory, it can be proved that the bipartite consensus tracking errors can converge to a sufficient small region of the origin in finite-time and all signals in the closed-loop systems are bounded in finite-time although there exists the input saturation. The effectiveness of the proposed method is shown by simulation results.

Recursive weighted least squares estimation algorithm based on minimum model error principle

Kalman filter is commonly used in data filtering and parameters estimation of nonlinear system, such as projectile's trajectory estimation and control. While there is a drawback that the prior error covariance matrix and filter parameters are difficult to be determined, which may result in filtering divergence. As to the problem that the accuracy of state estimation for nonlinear ballistic model strongly depends on its mathematical model, we improve the weighted least squares method (WLSM) with minimum model error principle. Invariant embedding method is adopted to solve the cost function including the model error. With the knowledge of measurement data and measurement error covariance matrix, we use gradient descent algorithm to determine the weighting matrix of model error. The uncertainty and linearization error of model are recursively estimated by the proposed method, thus achieving an online filtering estimation of the observations. Simulation results indicate that the proposed recursive estimation algorithm is insensitive to initial conditions and of good robustness.

An improved integrated navigation method based on RINS, GNSS and kinematics for port heavy-duty AGV

The current navigation methods for port heavy-duty automated guided vehicle mainly include the antenna radar-transponder navigation and the global navigation satellite system. However, the former has a huge cost and the latter will generate multi-path error easily. To avoid these problems, an improved integrated navigation method based on single-axis rotating inertial navigation system, global navigation satellite system and kinematics is proposed. First, the rotating inertial navigation system/ global navigation satellite system and rotating inertial navigation system/Kinematics integrated navigation methods generate corresponding estimates and filtering error covariances through their respective extended Kalman filter filters, and then the two sets of results are fused by the optimal weighted voting fusion method. The proposed method is applied to a heavy-duty automated guided vehicle for engineering verification. Without multi-path error, the navigation accuracy is 1.8–2.98 times higher than that of the traditional global navigation satellite system navigation. In the case of multi-path error, the improved method still has high fault tolerance and high navigation accuracy. The accuracy of this method satisfies the requirements of port heavy-duty automated guided vehicle, which can greatly reduce the number of transponders and has high practical value.

Appraising the Financial Sustainability of a Pension System with Signal Processing

One key issue of the Spanish pension system is its financial sustainability in regard to slumping fertility rate and rising longevity of the Spanish population. The paper presents a versatile and robust model that may help pension managers gain insight into the future Spanish pyramid of ages, from which they will appraise cash inflows and outflows of the pension system. The model forecasts ninety years of the Spanish population for each cohort of the pyramid of ages. Borrowed from the signal processing discipline, the model relies on the Burg method which fits a pth order autoregressive (AR) model to the input signal by minimizing (least squares) the forward and backward prediction errors while constrai-ning the AR parameters to satisfy the Levinson-Durbin recursión, then uses an infinite impulse response prediction error filter. Results add better perspective and insight to the Spanish population projection forecasted by the United Nations Population Division.

A novel set membership fast NLMS algorithm for acoustic echo cancellation

In order to improve the performances of the Fast Normalized Least Mean Square type (FNLMS) adaptive filtering algorithm in the context of Acoustic Echo Cancellation (AEC), this work proposes an Improved Set Membership version by exploiting, firstly, the theory of set membership identification (SMI) to the FNLMS algorithm to get more complexity reduction, then by using the estimated output error to update the step size which results to better convergence speed and tracking ability. The obtained algorithm is called the Improved Set Membership FNLMS (ISM-FNLMS) algorithm. Simulation results have demonstrated the superiority and the good performances of the proposed algorithm compared with NLMS, SM-NLMS, FNLMS, SM-FNLMS and SM-NLMS with Robust Error Bound (SMREB-NLMS) algorithms in terms of convergence speed, steady-state mean square filtering error (MSE), tracking capability and computational complexity performances.

Invariant extended Kalman filter on matrix Lie groups

We derive symmetry preserving invariant extended Kalman filters (IEKF) on matrix Lie groups. These Kalman filters have an advantage over conventional extended Kalman filters as the error dynamics for such filters are independent of the group configuration which, in turn, provides a uniform estimate of the region of convergence. The proposed IEKF differs from existing techniques in literature on the account that it is derived using minimal tools from differential geometry that simplifies its representation and derivation to a large extent. The filter error dynamics is defined on the Lie algebra directly instead of identifying the Lie algebra with an Euclidean space or defining the error dynamics in local coordinates using exponential map, and the associated differential Riccati equations are described on the corresponding space of linear operators using tensor algebra. The proposed filter is implemented for the attitude dynamics of the rigid body, which is a benchmark problem in control, and its performance is compared against a conventional extended Kalman filter (EKF). Numerical experiments support that the IEKF is computationally less intensive and gives better performance than the EKF.

Neural Integral Non-Singular Fast Terminal Synchronous Sliding Mode Control for Uncertain 3-DOF Parallel Robotic Manipulators

Robotics have been substituting humans increasingly and effectively to operate repeated, dangerous, heavy, complicated works in human life, production industry, and discovery missions. This work designs a Neural Integral Non-singular Fast Terminal Synchronous Sliding Mode Control (NINFTSSMC) approach for 3-DOF parallel robotic manipulators with uncertain dynamics, using synchronous nonlinear sliding surface, where this sliding surface is formed through the integration of the Synchronization Control (SC) and the Integral Non-singular Fast Terminal Sliding Mode Control (INFTSMC). Accordingly, position errors and synchronization errors quickly converge to the sliding surface at the same time. Next, a Feed-Forward Neural Network (FNN) is applied to estimate uncertain dynamics, whose novelty, compared to a classic FNN, is that they utilize a Non-singular Fast Terminal Sliding Mode (NFTSM) error filter to replace a classic error filter. Thanks to this procedure, the lumped uncertain dynamics are compensated more quickly and more accurately, thus, the malfunction in the reaching phase of state variables approaching the sliding surface is handled thoroughly. Finally, the control approach is designed for a robotic system to achieve the prescribed performance, obtaining rapid error convergence, robustness with uncertain dynamics, minimum chattering, synchronization, and high precision. The stability of the control loop is secured according to the Lyapunov theory. To test the robustness and confirm the effectiveness of the suggested controller for a 3-DOF parallel manipulator, computer simulations and performance comparisons are conducted.