Robust Adaptive Algorithm Research Articles

Robust adaptive monopulse algorithm based on main lobe constraints and subspace tracking

In continuous wave (CW) radar and high pulse repetition frequency pulse-Doppler (HPRF-PD) radar, the interference plus noise sample snapshots are hard to be obtained. The desired signal in the received snapshots makes the LCMV-based adaptive monopulse algorithm sensitive to pattern look direction error. A linearly constrained subarray robust adaptive monopulse algorithm based on main lobe maintenance constraint and subspace tracking is developed in this paper. The constraint of main lobe maintenance is obtained by signal subspace projection. The bi-iterative least-square (Bi-LS) subspace tracking is used to update the signal subspace, and a power-associated method is developed to determine the dimension of the projection subspace automatically. The proposed robust adaptive monopulse algorithm can achieve high-angle estimation accuracy and good robustness to look direction error while expending only one additional degree of freedom compared to conventional LCMV-based method.

Multiple Vehicle Formation Control Based on Robust Adaptive Control Algorithm

For formation control of multiple vehicle, the typical method is feedback linearization, wherein followers track the leader within a constant distance. Uncertainties and noises arising from varying external environments may easily lead to tracking errors, however. In this paper, the leader-follower error model is built and an adaptive controller is designed to address the uncertain relative distance using the dynamic estimation of the leader-follower distance. Further, a robust adaptive controller is developed to tackle inevitable white noise in the follower velocity detection. Simulation validations demonstrate that the proposed robust adaptive controller performs better than other controllers, given uncertainties and noise. Finally, the multiple vehicles formation mode is extended from two vehicles to five by using graph theory. This paper simulates five vehicles in triangle formation using a robust adaptive controller. The results confirm the viability of the robust adaptive controller under conditions of uncertain relative distances between vehicles, given external noise.

Robust Fault Detection and Estimation in Nonlinear Systems with Unknown Constant Time-Delays

This paper studies the problem of fault detection and estimation in nonlinear time-delayed systems with unknown inputs, where the time-delays are supposed to be constant but unknown. A new fault detection filter, which can estimate online the time-delays, is first introduced. Then, a reference residual model is proposed to formulate the robust fault detection filter design problem as an H∞ model-matching problem. Furthermore, by a novel robust adaptive fault estimation algorithm, the classical assumption that the time derivative of the output error should be known is removed. In addition, applying a robust H∞ optimization control technique, sufficient conditions for the existence of the fault detection filter (FDF) are derived in terms of linear matrix inequality (LMI). Finally, simulation results are presented to illustrate the effectiveness of the proposed algorithm.

A Novel Robust Adaptive Beamforming Algorithm Based on Total Least Squares and Compressed Sensing

A Novel Robust Adaptive Beamforming Algorithm Based on Total Least Squares and Compressed Sensing

Adaptive control for uncertain discrete-time systems with unknown disturbance based on RNN

A new robust adaptive control algorithm is developed for a class of uncertain discrete-time SISO systems. Different from the existing investigated systems, the concerned discrete system here is with both uncertain smooth nonlinear functions and unknown disturbance. On the basis of the idea of neural network (NN) approximation, a novel recurrent neural network (RNN) is first proposed and used to approximate a backstepping control law following the transformation of the original system into a predictor form. According to Lyapunov stability theorem, a new on-line tuning law for parameters of RNN is obtained. Meanwhile, in order to achieve satisfying robust tracking performance, a novel controller is constructed by virtue of the approximation error of RNN. It has been proved that all the concerned signals are uniformly ultimately bounded. In addition, a very small tracking error can be obtained through appropriate selection of control parameters. Finally, we give a simulation example to demonstrate the validness of the newly proposed control algorithm for the investigated systems.

Diffusion maximum correntropy criterion algorithms for robust distributed estimation

Robust diffusion adaptive estimation algorithms based on the maximum correntropy criterion (MCC), including adapt then combine MCC and combine then adapt MCC, are developed to deal with the distributed estimation over network in impulsive (long-tailed) noise environments. The cost functions used in distributed estimation are in general based on the mean square error (MSE) criterion, which is desirable when the measurement noise is Gaussian. In non-Gaussian situations, especially for the impulsive-noise case, MCC based methods may achieve much better performance than the MSE methods as they take into account higher order statistics of error distribution. The proposed methods can also outperform the robust diffusion least mean p-power (DLMP) and diffusion minimum error entropy (DMEE) algorithms. The mean and mean square convergence analysis of the new algorithms are also carried out.

Less-conservative robust adaptive control of neutral systems with mixed time-delays

ABSTRACTIn this paper, the problem of robust stabilisation of a class of neutral-type uncertain linear systems via adaptive control approach is investigated. The neutral systems are assumed to be subjected to model uncertainties, disturbances and mixed time-delays. In comparison to the previous work in the literature, with the assumption that the states of the system and the delayed-states of the system with its derivatives are completely accessible, a new and less-conservative robust adaptive control algorithm is proposed. Based on Lyapunov–Krasovskii function, the model uncertainties and the disturbances are compensated and the stability of the uncertain neutral-type system is guaranteed. The tracking problem via model reference adaptive control is also considered. The most important characteristic of the proposed approach is to make the overall system stability constraints depend on desired arbitrary matrices, and not on the practical system matrices. This is achieved by means of adaptation techniques. Finally, three illustrative examples with numerical simulations are carried out to indicate the significant improvements over some existing results.

Low‐complexity robust adaptive beamforming algorithms exploiting shrinkage for mismatch estimation

This study proposes low-complexity robust adaptive beamforming (RAB) techniques based on shrinkage methods. The authors first review a low-complexity shrinkage-based mismatch estimation batch algorithm to estimate the desired signal steering vector mismatch, in which the interference-plus-noise covariance matrix is also estimated by a recursive matrix shrinkage method. Then they develop low-complexity adaptive recursive versions of stochastic gradient and conjugate gradient to update the beamforming weights, resulting in low-cost robust adaptive algorithms. An analysis of the effect of shrinkage on the estimation procedure is developed along with a computational complexity study of the proposed and existing algorithms. Simulations are conducted in local scattering scenarios and comparisons to existing RAB techniques are provided.

Robust Adaptation in Impulsive Noise

The popular least-mean-squares (LMS) algorithm for adaptive filtering is nonrobust against impulsive noise in the measurements. The presence of this type of noise degrades the transient and steady-state performance of the algorithm. Since the distribution of the impulsive noise is generally unknown, a robust semi-parametric approach to adaptive filtering is warranted, where the output error nonlinearity is adapted jointly with the parameter of interest. In this paper, a robust adaptive filtering algorithm is developed that effectively learns and tracks the output error distribution to improve estimation performance. The performance of the algorithm is analyzed mathematically and validated experimentally.

Robust adaptive beamforming using IAA‐based interference‐plus‐noise covariance matrix reconstruction

A novel robust adaptive beamforming algorithm based on a reconstructed covariance matrix is proposed to combat the desired signal look direction error. First, the iterative adaptive approach (IAA) algorithm is performed to acquire the true signal direction and the spatial spectrum simultaneously. Secondly, the IAA spatial spectrum is used to reconstruct the interference-plus-noise covariance matrix. Compared with other reconstruction-based algorithms, this approach is capable of dealing with coherent interferences without any optimisation procedures.

Convex regularized recursive maximum correntropy algorithm

In this brief, a robust and sparse recursive adaptive filtering algorithm, called convex regularized recursive maximum correntropy (CR-RMC), is derived by adding a general convex regularization penalty term to the maximum correntropy criterion (MCC). An approximate expression for automatically selecting the regularization parameter is also introduced. Simulation results show that the CR-RMC can significantly outperform the original recursive maximum correntropy (RMC) algorithm especially when the underlying system is very sparse. Compared with the convex regularized recursive least squares (CR-RLS) algorithm, the new algorithm also shows strong robustness against impulsive noise. The CR-RMC also performs much better than other LMS-type sparse adaptive filtering algorithms based on MCC.

Robust adaptive algorithm for active control of impulsive noise

Active noise control (ANC) systems employing adaptive filters suffer from stability issues in the presence of impulsive noise. To overcome this limitation, new methods must be investigated. In this paper, we propose the filtered-x state-space recursive least square (FxSSRLS), an SSRLS-based practical and adaptive algorithm for ANC. Computer simulations are executed to verify the enhanced performance of the FxSSRLS algorithm. Symmetric α-stable (SαS) distributions are used to model impulsive noise. The results show that the proposed FxSSRLS algorithm is more robust in eliminating high-peaked impulses than the recently reported algorithms for ANC applications. Moreover, the suggested solution exhibits better stability and faster convergence, without jeopardizing the performance of the proposed solution in terms of residual noise suppression in the presence of impulses.

Robust adaptive dynamic programming based online tracking control algorithm for real wheeled mobile robot with omni-directional vision system

This paper proposes a new method to design an online robust adaptive dynamic programming algorithm (RADPA) for a wheeled mobile robot which is equipped with an omni-directional vision system. To integrate kinematic and dynamic controllers into the unique controller, we transform the strict feedback system dynamics into tracking error dynamics. Then, we propose a control scheme which uses only one neural network rather than three proposed in the actor-critic-based control schemes for the two-player zero-sum game problem. A neural network weight update law is designed for approximating the solution of the Hamilton–Jacobi–Isaacs equation without knowing knowledge of internal system dynamics. To implement the scheme, we propose the online RADPA, in which control and disturbance laws are updated simultaneously in an iterative loop. The convergence and stability of the online RADPA are proven by Lyapunov techniques. Simulations and experiments on a wheeled mobile robot testbed are carried out to verify the effectiveness of the proposed algorithm.

Robust Adaptive Algorithm by an Adaptive Zero Attractor Controller of ZA-LMS Algorithm

This paper proposes a new approach to identify time varying sparse systems. The proposed approach uses Zero-Attracting Least Mean Square (ZA-LMS) algorithm with an adaptive optimal zero attractor controller which can adapt dynamically to the sparseness level and provide appreciable performance in all environments ranging from sparse to nonsparse conditions. The optimal zero attractor controller is derived based on the criterion that confirms largest decrease in mean square deviation (MSD) error. A simple update rule is also proposed to change the zero attractor controller based on the level of sparsity. It is found that, for nonsparse system, the proposed approach converges to LMS (as ZA-LMS cannot outperform LMS when the system is nonsparse) and, for highly sparse system, as the proposed approach is based on optimal zero attractor controller, it converges either similar to ZA-LMS or even better than ZA-LMS (depending on the value of zero attractor controller chosen for ZA-LMS algorithm). The performance of the proposed algorithm is better than ZA-LMS and LMS when the system is semisparse. Simulations were performed to prove that the proposed algorithm is robust against variable sparsity level.

Robust adaptive control with disturbances compensation

The paper is concerned with design of the robust adaptive algorithm with disturbance compensation under parametric uncertainties and external disturbances. The designed control algorithm provides compensation of disturbances and tracking of reference signal with high accuracy in a finite time. Computer simulations prove the efficiency of the proposed algorithm.

Robust frequency estimation in three‐phase power systems using correntropy‐based adaptive filter

In this study, the authors propose a robust adaptive algorithm for frequency estimation in three-phase power systems when the voltage readings are corrupted by random noise sources. The proposed algorithm employs the Clarke's transformed three-phase voltage (a complex signal) and augmented complex statistics to deal with both of balanced and unbalanced system conditions. To derive the algorithm, a widely linear predictive model is assumed for the Clarke's transformed signal where the frequency of system is related to the parameters of this model. To estimate the model parameters with noisy voltage reading, they utilise the notions of maximum correntropy criterion and gradient-ascent optimisation. The proposed algorithm has the computational complexity of the popular complex least-mean-squares (CLMS) algorithm, along with the robustness that is obtained by using higher-order moments beyond just second-order moments. They compare the performance of the proposed algorithm with a recently introduced augmented CLMS (ACLMS) algorithm in different conditions, including the voltage sags and presence of impulsive noises and and higher-order harmonics. Their simulation results demonstrate that the proposed algorithm provides improved frequency estimation performance compared with ACLMS especially when the measured voltages are corrupted by impulsive noise.

Robust Adaptive Dynamic Programming of Two-Player Zero-Sum Games for Continuous-Time Linear Systems.

In this brief, an online robust adaptive dynamic programming algorithm is proposed for two-player zero-sum games of continuous-time unknown linear systems with matched uncertainties, which are functions of system outputs and states of a completely unknown exosystem. The online algorithm is developed using the policy iteration (PI) scheme with only one iteration loop. A new analytical method is proposed for convergence proof of the PI scheme. The sufficient conditions are given to guarantee globally asymptotic stability and suboptimal property of the closed-loop system. Simulation studies are conducted to illustrate the effectiveness of the proposed method.

Robust modeling of acoustic paths using a sparse adaptive algorithm

Acoustic impulse response functions are generally sparse in nature and traditionally these are modeled by adaptive finite impulse response (FIR) filters trained using a least mean square (LMS) algorithm. The conventional LMS algorithm is not effective in modeling sparse systems and sparse LMS algorithms have been recently developed to improve the modeling in such scenarios. However, the traditional sparse LMS algorithms are not robust to disturbances at the error sensor and may diverge in some scenarios. With an objective to overcome this limitation of conventional sparse adaptive algorithm, this paper presents a robust sparse adaptive algorithm. The new algorithm has been shown to effectively model sparse systems in a robust manner. In addition, the algorithm has been successfully applied in modeling the acoustic feedback path in a behind the ear digital hearing aid.

Robust beamforming with imprecise array geometry using steering vector estimation and interference covariance matrix reconstruction

Compared with state-of-the-art robust adaptive beamforming methods, the recently developed steering vector estimation-based beamformer and interference-plus-noise covariance matrix reconstruction-based beamformer are known to provide better robustness against model mismatch. However, both methods may suffer from performance degradation in presence of antenna array geometry error. To alleviate this problem, a novel algorithm is proposed in this paper. In contrast to previous works, the true desired signal steering vector is estimated by solving a new optimization problem, the objective function of which is minimizing the beamformer sensitivity, while the constraint of which is obtained by optimizing the worst-case performance of the constraint adopted in conventional steering vector estimation-based method. We show that the solution of the newly constructed optimization problem can be obtained in closed form by using the Lagrange multiplier methodology, which has a comparable computational complexity with that of the standard Capon beamformer. Subsequently, the precise interference-plus-noise covariance matrix is reconstructed by eliminating the estimated desired signal component from the sample covariance matrix, which mitigates computational burden and accumulated errors induced by the integration process employed in the conventional covariance matrix reconstruction-based method. Owing to the high accuracy of estimated and reconstructed results, the proposed robust adaptive beamforming algorithm can maintain satisfactory performance in cases with imprecise array geometry. Simulation results verify that the proposed method outperforms the existing ones in many scenarios.

Global adaptive control for uncertain nonaffine nonlinear hysteretic systems

In this paper, the global output tracking is investigated for a class of uncertain nonlinear hysteretic systems with nonaffine structures. By combining the solution properties of the hysteresis model with the novel backstepping approach, a robust adaptive control algorithm is developed without constructing a hysteresis inverse. The proposed control scheme is further modified to tackle the bounded disturbances by adaptively estimating their bounds. It is rigorously proven that the designed adaptive controllers can guarantee global stability of the closed-loop system. Two numerical examples are provided to show the effectiveness of the proposed control schemes.