Robust Adaptive Algorithm Research Articles

Zero Attracting Maximum Total Correntropy Algorithm for Sparse System Identification

Recently, a robust maximum total correntropy (MTC) adaptive filtering algorithm has been used in errors-in-variables (EIV) model in which both input and output data are contaminated with noises. As an extension of the maximum correntropy criterion (MCC), the MTC algorithm shows desirable performance in non-Gaussian noise environments. However, the MTC algorithm may suffer from performance deterioration in the sparse system. To overcome this drawback, a robust and sparse adaptive filtering algorithm, called zero attracting maximum total correntropy (ZA-MTC), is derived by adding a l1 norm penalty term to the maximum total correntropy algorithm in this brief. In addition, in the reweighted version, a log-sum function is employed to replace the l1 norm penalty term. Simulation results demonstrate the advantages of the proposed algorithms under sparsity assumptions on the unknown parameter vector.

Adaptive finite‐time control for bilateral teleoperation systems with jittering time delays

SummaryIn this paper, we present a robust adaptive control algorithm for a class of bilateral teleoperation systems with system uncertainties and jittering time delays. The remarkable feature of jittering delays is that time delays change sharply and randomly. Conventional controllers would fail because jittering time delays introduce serious chattering. To address the jittering issue, a novel jittering‐free scheme is developed by relaxing and extending the frequently used constant upper bound. Moreover, an adaptive law was incorporated with the Chebyshev neural network to deal with the system uncertainties. To obtain finite‐time synchronization performance, a fast terminal sliding mode controller is proposed through the technique of “adding a power integrator.” With the proposed control scheme, the robust finite‐time convergence performance is guaranteed. The settling time can be further calculated with the controller parameters. The simulation and experiment results have demonstrated the effectiveness of the proposed method.

Localization and classification of heartbeats using robust adaptive algorithm

Abstract Automated analysis of heart sound recordings (phonocardiograms or PCG) for classification of heart beats in noisy environments is a challenging problem. The major challenges are introduced due to inter- and intra-beat variation, spectral overlap of heart and lung sounds and noise picked from environment. This paper presents a robust algorithm for heart beat localization and classification using variable length window and peak amplitude threshold variation. The technique presented in this paper first attempts to find peaks which correspond to the fundamental heart sound cycle (FHSC) i.e. S1, S2, S1, … or fundamental heart sound period (FHSP) i.e. S1, S1, …. If FHSC/FHSP is found, an attempt is made to extend it to the whole signal. Once extended, the signal is directly labeled using systole and diastole durations. During classification, history of labels is used to counter the heart rate variations. The proposed technique is evaluated on publicly available Pascal Heart Sound Challenge dataset and reports smaller average error for localization and has high sensitivity and accuracy for heart beat classification.

A new robust forward BSS adaptive algorithm based on automatic voice activity detector for speech quality enhancement

This paper presents a new adaptive blind source separation (BSS) algorithm for acoustic noise reduction and speech enhancement applications in a car framework. The forward BSS structure is often used to separate speech from noise and enhances the speech signal at the output processing. The drawback of most speech enhancement methods that are based on BSS structures is the use of a manual voice activity detection (VAD) system to control the source separation process. In this work, we propose a new algorithm based on the forward BSS structure and an automatic VAD (AVAD) system. The new AVAD system uses an adaptive approach based on a modified normalized least mean square (NLMS) adaptive algorithm to get a new speech enhancement algorithm. This proposed algorithm allows to: (i) reduce the computational complexity of previous techniques based on AVAD system; (ii) enhance the quality of the output speech signal. We have carried out intensive experiments on the proposed algorithm and others state of the art algorithms that use VAD or AVAD systems. In this paper, we show the efficiency of the proposed algorithm in terms of objective and subjective criteria.

Coprime array‐based robust beamforming using covariance matrix reconstruction technique

A novel robust adaptive beamforming algorithm based on a coprime array is proposed in this study. First, the authors exploit the virtual array structure of the coprime coarray to construct two subspaces. The first subspace is obtained from the covariance matrix of the virtual uniform linear array, whereas the second one is obtained by integrating a spatial spectrum over each angular sector of the signal. Then, by using a closed-form expression of the projection-based method, the steering vectors (SVs) of the signals are estimated from the intersection of the two subspaces. Moreover, according to the covariance fitting theory, the power associated with the desired signal, interference signals, and noise is obtained from the virtual sample covariance matrix. In addition, to reconstruct the interference-plus-noise covariance matrix (INCM), the maximum correlation principle is used to transfer the virtual SVs of the signals to real ones corresponding to a physical array. Finally, with the estimated SV of the desired signal and INCM, the proposed robust algorithm is devised. Simulation results demonstrate the superiority and effectiveness of the proposed algorithm.

Actuator fault reconstruction based on a robust adaptive observer

A fault reconstruction method based on a robust adaptive observer is proposed to handle the actuator additive faults that exist in a non-linear descriptor system that satisfies the Lipschitz conditions. First, a robust adaptive fault reconstruction observer is proposed. This method has the desired robust performance and can achieve accurate reconstructions of both abrupt and time-varying faults in the presence of an external unknown disturbance. Then, to improve the transition process of fault reconstruction, an improved robust adaptive fault reconstruction observer is proposed. The improved robust adaptive fault reconstruction algorithm, which uses different coefficients for the proportion output errors and the derivative output errors, can enhance the rapidity and accuracy of fault reconstruction. The robust adaptive and improved robust adaptive observers are based on the linear matrix inequality technique. According to the Lyapunov stability theory, the stability conditions of the methods are derived. Additionally, the influence of an external unknown disturbance on the actuator fault reconstruction is suppressed using a robust method. Finally, two different examples are used to demonstrate the effectiveness of the proposed fault reconstruction methods.

Recursive Maximum Correntropy Learning Algorithm With Adaptive Kernel Size

In this brief, a robust correntropy-based adaptive learning algorithm, called the adaptive kernel recursive maximum correntropy criterion, is proposed by considering an adaptive kernel size based on the Kullback–Leibler divergence minimization. Simulation results confirm that the proposed algorithm can perform better than other adaptive algorithms especially when the environment is disturbed by non-Gaussian noises. The proposed algorithm is beneficial for using in helicopters, since the pilot speech signal is contaminated by the acoustic impulsive noises that are originated from rotor blades.

Efficient adaptive filtering algorithm for IQ imbalance compensation Tx/Rx systems

This study addresses the problem of in-phase and quadratic-phase (IQ) imbalance in digital transmitter and receiver communication systems. Recently, several adaptive and non-adaptive solutions to this problem have been proposed. In this study, an adaptive solution to this problem is given. The authors propose a new robust two-channel adaptive algorithm to compensate the IQ imbalance problem of quadratic receivers. The new algorithm is based on the combination between the backward blind source separation (BBSS) structure and the fast Newton transversal filter (FNTF) technique to form a two-channel algorithm that cancels the noisy IQ signal adaptively. The combination of the Newton recursion of the FNTF algorithm with the BBSS principle shows the efficiency of the new algorithm to enhance the output signal to interference ratio and allows distortion correction at the output. Simulation results show the efficiency of the new algorithm to compensate the IQ imbalance problem in comparison with the conventional two-channel normalised least mean square algorithm.

A Robust Step Detection Algorithm and Walking Distance Estimation Based on Daily Wrist Activity Recognition Using a Smart Band

Human activity recognition and pedestrian dead reckoning are an interesting field because of their importance utilities in daily life healthcare. Currently, these fields are facing many challenges, one of which is the lack of a robust algorithm with high performance. This paper proposes a new method to implement a robust step detection and adaptive distance estimation algorithm based on the classification of five daily wrist activities during walking at various speeds using a smart band. The key idea is that the non-parametric adaptive distance estimator is performed after two activity classifiers and a robust step detector. In this study, two classifiers perform two phases of recognizing five wrist activities during walking. Then, a robust step detection algorithm, which is integrated with an adaptive threshold, peak and valley correction algorithm, is applied to the classified activities to detect the walking steps. In addition, the misclassification activities are fed back to the previous layer. Finally, three adaptive distance estimators, which are based on a non-parametric model of the average walking speed, calculate the length of each strike. The experimental results show that the average classification accuracy is about 99%, and the accuracy of the step detection is 98.7%. The error of the estimated distance is 2.2–4.2% depending on the type of wrist activities.

Robust adaptive filter for shipborne kinematic positioning and velocity determination during the Baltic Sea experiment

Information on trajectory and attitude is essential for analyzing gravimetric data collected on kinematic platforms. Usually, a Kalman filter is used to obtain high-accuracy positional and velocity information. However, this can be affected by measurement outliers and by state disturbances that occur frequently under a fast-changing environment. To overcome these problems, a robust adaptive Kalman filtering algorithm is applied for state estimates, which introduces an equivalent weight to resist measurement outliers and an optimal adaptive factor to balance the contributions of the kinematic model information and the measurements. In addition to the conventional robust estimator, an improved Current Statistical (CS) model is proposed. The improved CS model adopts a variance adaptive learning algorithm, and it can perform self-adaptation of acceleration variance with the innovation information; thus, it can overcome the shortcoming of lower tracking accuracy and avoid setting the maximum acceleration. Following a gravimetry campaign on the Baltic Sea, it is shown in theory and in practice that the robust adaptive Kalman filter is not only simple in its calculation but also more reliable in controlling the colored observation noise and kinematic state disturbance compared with the classical Kalman filter. The improved CS model performs best, especially when analyzing the positioning errors at the turns due to the target maneuvering. Compared to the CS model, the RMS values of the positional estimates derived from the improved CS model decrease by almost 30% in the horizontal direction, and no significant improvement in the vertical direction is found.

Robust Adaptive Control of Two Chaotic Systems with Unknown Parameters

A kind of robust adaptive control algorithm of two chaotic systems is proposed in this paper. The design strategy of the algorithm lies in constructing an update law of unknown parameters estimation of driven system. The chaotic systems are not required to satisfy the Lipschitz condition. The robust adaptive controller is designed to complete synchronization. A numerical situation was proposed to show the effectiveness.

Aerodynamic model-based robust adaptive control for close formation flight

This paper presents an aerodynamic model-based robust adaptive control algorithm for close formation flight. The leader aircraft is assumed to be at level and straight flight which characterizes the most common scenario for close formation flight. The formation aerodynamic effects are assumed to be unknown, but an online formation aerodynamic model is available to predict those effects. In light of the online formation aerodynamic model, a robust adaptive control algorithm is thereafter developed on the follower aircraft to counteract the unknown formation aerodynamic effects and obtain highly accurate formation tracking performance. The proposed control algorithm is composed of a baseline controller and an integrator-augmented robust adaptive controller, which can efficiently deal with both matched and mismatched formation aerodynamic effects and external disturbances. The major advantage of the proposed design is that it can achieve at least ultimate bounded tracking control with certain transient performance guaranty. The efficiency and robustness of the proposed control design are eventually validated via numerical simulations of close formation flight at two different scenarios.

Fault Estimation and Accommodation for a Class of Nonlinear System Based on Neural Network Observer

The problem of fault estimation and accommodation of nonlinear systems with disturbances is studied using adaptive observer and neural network techniques. A robust adaptive learning algorithm based on switching βs-modification is developed to realize the accurate and fast estimation of unknown actuator faults or component faults. Then a fault tolerant controller is designed to restore system performance. Dynamic error convergence and system stability can be guaranteed by Lyapunov stability theory. Finally, simulation results of quadrotor helicopter attitude systems are presented to illustrate the efficiency of the proposed techniques.

Quantitative phase-field lattice-Boltzmann study of lamellar eutectic growth under natural convection.

The influence of natural convection on lamellar eutectic growth was determined by a comprehensive phase-field lattice-Boltzmann study for Al-Cu and CBr_{4}-C_{2}Cl_{6} eutectic alloys. The mass differences resulting from concentration differences led to the fluid flow and a robust parallel and adaptive mesh refinement algorithm was employed to improve the computational efficiency. By means of carefully designed "numerical experiments", the eutectic growth under natural convection was explored and a simple analytical model was proposed to predict the adjustment of the lamellar spacing. Furthermore, by alternating the solute expansion coefficient, initial lamellar spacing, and undercooling, the microstructure evolution was presented and compared with the classical eutectic growth theory. Results showed that both interfacial solute distribution and average curvature were affected by the natural convection, the effect of which could be further quantified by adding a constant into the growth rule proposed by Jackson and Hunt [Jackson and Hunt, Trans. Metall. Soc. AIME 236, 1129 (1966)].

A SINS/SRS/GNS Autonomous Integrated Navigation System Based on Spectral Redshift Velocity Measurements.

In order to meet the requirements of autonomy and reliability for the navigation system, combined with the method of measuring speed by using the spectral redshift information of the natural celestial bodies, a new scheme, consisting of Strapdown Inertial Navigation System (SINS)/Spectral Redshift (SRS)/Geomagnetic Navigation System (GNS), is designed for autonomous integrated navigation systems. The principle of this SINS/SRS/GNS autonomous integrated navigation system is explored, and the corresponding mathematical model is established. Furthermore, a robust adaptive central difference particle filtering algorithm is proposed for this autonomous integrated navigation system. The simulation experiments are conducted and the results show that the designed SINS/SRS/GNS autonomous integrated navigation system possesses good autonomy, strong robustness and high reliability, thus providing a new solution for autonomous navigation technology.

Robust adaptive algorithm for nonlinear systems with unknown measurement noise and uncertain parameters by variational Bayesian inference

SummaryThis paper studies an adaptive algorithm for the estimation problem of nonlinear systems with unknown or missing measurement noise and uncertain parameters using variational Bayesian (VB) inference. We combine VB inference with the Monte Carlo sampling technique to settle this problem. There are many cases of missing information, and because of the difficulty in obtaining the analytical results, the existing control methods for uncertain systems lack generality. We present a set of nonlinear recursive adaptive filtering algorithms that address the unknown parameters and probability density function. The proposed algorithms yield a separable variational approximation of the joint posterior distribution of noise parameters with uncertain parameters and states on each step separately. Estimation convergence and robustness against disturbances are guaranteed. A convergence result for VB inference is presented. Extensive simulation examples are provided to demonstrate the efficacy of the proposed algorithms.

A family of robust adaptive filtering algorithms based on sigmoid cost

In this paper, a new framework of cost function for designing robust adaptive filtering algorithms is developed. This new cost framework, called sigmoid cost function, results from imbedding the conventional cost function into the sigmoid framework. Utilizing this proposed sigmoid cost framework, several important members (e.g., the sigmoid least mean square, sigmoid least absolute difference, sigmoid least mean fourth, sigmoid least mean logarithmic square, sigmoid least logarithmic absolute difference algorithms) of this family of robust adaptive filtering algorithms are proposed. In the proposed sigmoid cost framework with a steepness parameter α, a greater (smaller) value of α results in a slower (faster) convergence. Thus, an adaptive α is proposed which is based on an exponential function with respect to the L1-norm of the system error. In addition, the mean-square deviation analysis of the proposed algorithms is also carried out and their accuracies are verified via system identification experiment. Simulations in system identification and acoustic echo-cancellation scenarios have demonstrated that the proposed algorithms outperform the corresponding order's generalized maximum correntropy criterion, normalized least mean square using step-size scaler, sign algorithm and least logarithmic absolute difference algorithms.

Adaptive Robust Tracking Control for Multiple Unknown Fractional-Order Nonlinear Systems

By applying the fractional Lyapunov direct method, we investigate the robust consensus tracking problem for a class of uncertain fractional-order multiagent systems with a leader whose input is unknown and bounded. More specifically, multiple fractional-order systems with heterogeneous unknown nonlinearities and external disturbances are considered in this paper, which include the second-order multiagent systems as its special cases. First, a discontinuous neural network-based (NN-based) distributed robust adaptive algorithm is designed to guarantee the consensus tracking error exponentially converges to zero under a fixed topology. Also the derived results are further extended to the case of switching topology by appropriately choosing multiple Lyapunov functions. Second, a continuous NN-based distributed robust adaptive algorithm is further proposed to eliminate the undesirable chattering phenomenon of the discontinuous controller, where the consensus tacking error is uniformly ultimately bounded and can be reduced as small as desired. It is worth noting that all the proposed NN-based robust adaptive algorithms are independent of any global information and thus are fully distributed. Finally, numerical simulations are provided to validate the correctness of the proposed algorithms.

Robust adaptive trajectory tracking control of underactuated surface vessel in fields of marine practice

To enhance the control system robustness of an underactuated surface vessel with model uncertainties and environmental disturbances, a robust adaptive trajectory tracking algorithm based on proportional integral (PI) sliding mode control and the backstepping technique is proposed. In this algorithm, a continuous adaptive term is constructed to reduce the chattering magenta phenomenon of the system due to the sliding mode surface, and the backstepping technique is employed to force the ship position and the orientation on the desired values. In addition, we have proved the Lyapunov stability of the closed-loop system under the discontinuous environment disturbances or the thruster discontinuity. Finally, numerical simulations are performed to demonstrate the effectiveness of this novel methodology.

Robust Adaptive Neural Prescribed Performance Control for MDF Continuous Hot Pressing System With Input Saturation

This paper presents a novel nonlinear control approach for medium-density fiberboard continuous hot pressing electro-hydraulic servo system (EHSS). The EHSS is subject to input saturation and unknown external disturbance. The approach is developed in the framework of the prescribed performance control design. The transient and steady behaviors are both considered by virtue of an appropriate performance function. A new robust adaptive neural control algorithm is then developed by introducing a dynamic surface control scheme. Moreover, the compensation control is designed for the nonlinear term arising from the input saturation. It is shown with the Lyapunov stability analysis that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. In addition, the system tracking performance regarding transient and steady-state behaviors is guaranteed effectively with input saturation via the designed control approach. Finally, numerical simulation results are presented to authenticate and validate the effectiveness of the proposed control scheme.