Robust Adaptive Method Research Articles

A novel robust adaptive control for nonlinear uncertain quarter-vehicle suspension system in presence of unknown time delay actuation

This paper presents a novel robust adaptive control approach for nonlinear uncertain vehicle suspension system with time delayed actuation and bounded disturbances. The uncertainty and disturbance as well as the input delay on the system are all limited and unknown. This paper explores a control-oriented nonlinear model to accurately describe the dynamics of the vehicle suspension which incorporates uncertainty, disturbance and actuator delay. The controller is designed based on robust and adaptive approaches, which along with guaranteeing general goals for the suspension system is able to assure the stability of the closed-loop system in the Lyapunov concept. Also, due to the use of smooth functions in the robust controller structure, sudden changes in the behavior of system states are prevented. The simulation and comparison results in MATLAB environment show the efficiency of the proposed robust adaptive method in covering the effects of uncertainty, disturbance and time-variant actuator delay.

Cultivation of Limnospira maxima under extreme environmental conditions in Saudi Arabia: Salinity adaptation and scaling-up from laboratory culture to large-scale production

Limnospira maxima has been adapted to grow in high salinity and in an economically alternative medium using industrial-grade fertilizers under harsh environmental conditions in Saudi Arabia. A sequence of scaling-up processes, from the laboratory to large-scale open raceways, was conducted along with gradual adaptation to environmental stress (salinity, light, temperature, pH). High biomass concentration at harvest point and areal productivity were achieved during the harsh summer season (1.122 g L−1 and 60.35 g m−2 day−1, respectively). The average protein content was found to be above 40 % of dry weight. Changes in the color and morphological appearance of the L. maxima culture were observed after direct exposure to sunlight in the outdoor raceways. These results demonstrate a successful and robust adaptation method for algal cultivation at outdoor large-scale in harsh environment (desert conditions) and also prove the feasibility of using hypersaline seawater (42 g kg−1) as an algal growth medium.

Robust adaptive deep brain stimulation control of in-silico non-stationary Parkinsonian neural oscillatory dynamics

Objective. Closed-loop deep brain stimulation (DBS) is a promising therapy for Parkinson’s disease (PD) that works by adjusting DBS patterns in real time from the guidance of feedback neural activity. Current closed-loop DBS mainly uses threshold-crossing on-off controllers or linear time-invariant (LTI) controllers to regulate the basal ganglia (BG) Parkinsonian beta band oscillation power. However, the critical cortex-BG-thalamus network dynamics underlying PD are nonlinear, non-stationary, and noisy, hindering accurate and robust control of Parkinsonian neural oscillatory dynamics. Approach. Here, we develop a new robust adaptive closed-loop DBS method for regulating the Parkinsonian beta oscillatory dynamics of the cortex-BG-thalamus network. We first build an adaptive state-space model to quantify the dynamic, nonlinear, and non-stationary neural activity. We then construct an adaptive estimator to track the nonlinearity and non-stationarity in real time. We next design a robust controller to automatically determine the DBS frequency based on the estimated Parkinsonian neural state while reducing the system’s sensitivity to high-frequency noise. We adopt and tune a biophysical cortex-BG-thalamus network model as an in-silico simulation testbed to generate nonlinear and non-stationary Parkinsonian neural dynamics for evaluating DBS methods. Main results. We find that under different nonlinear and non-stationary neural dynamics, our robust adaptive DBS method achieved accurate regulation of the BG Parkinsonian beta band oscillation power with small control error, bias, and deviation. Moreover, the accurate regulation generalizes across different therapeutic targets and consistently outperforms current on-off and LTI DBS methods. Significance. These results have implications for future designs of closed-loop DBS systems to treat PD.

A novel robust adaptive Kalman filter with application to urban vehicle integrated navigation systems

This study addresses the formidable challenge presented by non-Gaussian, time-varying, heavy-tailed process noise in integrated vehicle navigation systems. While robust adaptive filtering methods exist, their efficacy is constrained, and certain algorithms exhibit suboptimal usability due to an excess of a priori parameters. We deduce an adaptive robust Kalman filter, characterizing process noise with a Student's t distribution and measurement noise adhering to a Gaussian distribution. To enhance robustness against unknown or varying noise statistics, our method simultaneously estimates the scale matrix, degrees of freedom parameters, state vector, and measurement noise covariance through the expectation–maximization (EM) algorithm and Bayesian variational inference theory. Rigorous simulations and empirical experiments validate its superior accuracy and stability, requiring minimal a priori parameter information. Vehicle-based experiments further demonstrate positioning error below 2.78 m, velocity error below 0.34 m/s, and heading error less than 1.28° after achieving a steady state. This approach exhibits significant potential for advancing the precision and reliability of vehicle navigation systems.

An adaptive robust service composition and optimal selection method for cloud manufacturing based on the enhanced multi-objective artificial hummingbird algorithm

The smooth execution of tasks in the cloud manufacturing (CMfg) process can be significantly affected by various real-world uncertainties, resulting in inefficiencies or even failures of the composite manufacturing service (CMS). Nevertheless, current research primarily concentrates on post-exception emergency handling methods, heavily dependent on the planned CMS and real-time status of manufacturing resources, often leading to suboptimal temporary adjustments or even the inability to find a viable solution. Hence, there is an urgent necessity to enhance the robustness of the planned CMS during the service composition and optimal selection (SCOS) phase. In pursuit of this goal, the paper initially presents an adaptive robust service composition and optimization selection (ARSCOS) model. This model dynamically determines whether to await exception resolution or invoke an alternative service, considering the circumstances when a service exception arises and employing task delay time to characterize the robustness of a specific CMS. Subsequently, an enhanced multi-objective artificial hummingbird algorithm (EMOAHA) is devised for this model. Finally, the efficacy of EMOAHA is demonstrated by applying it to nine different scales of civilian unmanned aerial vehicle manufacturing SCOS problems and 17 benchmark functions. The results illustrate that the proposed EMOAHA exhibits superior convergence and diversity in comparison to the other 11 classical intelligent optimization algorithms.

Calculation of single-event characteristics for voltage dip based on the goodness of fit test

The typical single-event characteristics of a voltage dip include residual voltage, duration, dip type, point-on-wave and phase-angle jump, which may influence the operation of the sensitive equipment under voltage dip. This operation is the foundation on which the single-event characteristics of a voltage dip can be accurately calculated to mitigate the voltage dip. The challenge of existing studies is that no unified and systematic method to calculate multiple single-event characteristics. This study proposes a robust adaptive method based on Goodness-of-Fit (GoF) test to calculate the characteristics of a voltage dip. First, a calculation method for the transition segment of voltage dip is proposed based on the GoF test. Then, four methods are presented for calculating four different characteristics of the dip based on the detected transition segment. The proposed method is subsequently unified and thus reduces the computation of monitor devices. Moreover, this method can adaptively adjust the critical value according to the sampling rate, avoiding the influence of noise. Finally, this study applies 516 sets of measured voltage dips to verify the performance of the proposed method, comparing the results with that of the existing methods.

Locality Robust Domain Adaptation for cross-scene hyperspectral image classification

Domain adaptation (DA) has become a widely used technique for cross-scene hyperspectral image (HSI) classification. Most DA methods aim to learn a domain invariant subspace that reduce the domain discrepancy between source and target domains. However, some of them fail to explore the local manifold structure between different domains, while also neglecting the negative influences of abnormal features. To solve these problems, we propose a Locality Robust Domain Adaptation (LRDA) method for cross-domain data recognition. In LRDA, the statistical alignment is applied to reduce the domain-shift between the source and target domains. Then, LRDA combines the row-sparsity constraint and the discriminant regularization term to learn a robust projection matrix, while maintaining the discriminative capability of the matrix. Furthermore, a manifold regularization term is proposed to automatically learn the nearest neighbors and weights between two domains. The proposed LRDA not only reduces the discrepancy between two domains but also explores the local neighbor information between them. Experiment results on three HSI datasets illustrate that the proposed LRDA has better performance than other related methods.

Robust Steganography for High Quality Images

With the prosperity of online social networks (OSNs), people usually share photos taken with their mobile phones to OSN, which is suitable for covert communication. However, these images have high quality factors (QFs) and will be JPEG recompressed with low QFs by OSNs, which precludes existing robust steganography methods using low QF images. Therefore, we propose a Postprocessing and precise dither Modulation based robust Adaptive Steganography method (PMAS) for high quality images. Precise dither modulation ensures the robustness of the modified coefficients and improves security by reducing the magnitude of modifications. Postprocessing amends the coefficients that changed after recompression to ensure the robustness of unmodified coefficients. Additionally, we devise heuristics to mitigate post-processing and explore the relationship between the modification magnitude and distortion assignment with novel scaling functions, which all contribute to security. The experimental results demonstrate that PMAS is competent for high quality images, and the effect of every module in PMAS is verified.

A Novel Optimal Robust Adaptive Scheme for Accurate GNSS RTK/INS Tightly Coupled Integration in Urban Environments

Modern navigation systems are inseparable from an integrated solution consisting of a global navigation satellite system (GNSS) and an inertial navigation system (INS) since they serve as an important cornerstone of national comprehensive positioning, navigation, and timing (PNT) technology and can provide position, velocity, and attitude information at higher accuracy and better reliability. A robust adaptive method utilizes the observation information of both systems to optimize the filtering system, overcoming the shortcomings of the Kalman filter (KF) in complex urban environments. We propose a novel robust adaptive scheme based on a multi-condition decision model suitable for tightly coupled real-time kinematic (RTK)/INS architecture, which can reasonably determine whether the filtering system performs robust estimation (TCRKF) or adaptive filtering (TCAKF), improving the robust estimation method of two factors considering ambiguity variance for RTK-related observations. The performance of the proposed robust adaptive algorithm was evaluated through two sets of real vehicle tests. Compared with the TCAKF and TCRKF algorithms, the new robust adaptive scheme improves the average three-dimensional (3D) position root mean square (RMS) by 31% and 18.88%, respectively. It provides better accuracy and reliability for position, velocity, and attitude simultaneously.

Distributionally robust unsupervised domain adaptation

Obtaining ground-truth label information from real-world data along with uncertainty quantification can be difficult or even infeasible. In the absence of labeled data for a certain task, unsupervised domain adaptation (UDA) techniques have shown great accomplishment by learning transferable knowledge from labeled source domain data and applying it to unlabeled target domain data, yet scarce studies consider addressing uncertainties under domain shifts to improve the model robustness. Distributionally robust learning (DRL) is emerging as a high-potential technique for building reliable learning systems that are robust to distribution shifts. In this study, we propose a distributionally robust unsupervised domain adaptation (DRUDA) method to enhance the machine learning model generalization ability under input space perturbations. The DRL-based UDA learning scheme is formulated as a min–max optimization problem by optimizing worst-case perturbations of the training source data. Our Wasserstein distributionally robust framework can reduce the shifts in the joint distributions across domains. The proposed DRUDA has been tested on digit datasets and the Office-31 dataset, compared with other state-of-the-art domain adaptation techniques. Our experimental results show that the proposed DRUDA leads to improvements in domain adaptation accuracy performance on target domains.

A robust adaptive grid method for first-order nonlinear singularly perturbed Fredholm integro-differential equations

<abstract><p>In this paper, a robust adaptive grid method is developed for solving first-order nonlinear singularly perturbed Fredholm integro-differential equations (SPFIDEs). Firstly such SPFIDEs are discretized by the backward Euler formula for differential part and the composite numerical quadrature rule for integral part. Then both a prior and an a posterior error analysis in the maximum norm are derived. Based on the prior error bound and the mesh equidistribution principle, it is proved that there exists a mesh gives optimal first-order convergence which is robust with respect to the perturbation parameter. Finally, the posterior error bound is used to choose a suitable monitor function and design a corresponding adaptive grid generation algorithm. Numerical results are given to illustrate our theoretical result.</p></abstract>

Detection-based active defense of biased injection attack based on robust adaptive controller

As the promising technology, the cooperative cyber-physical system can enhance the operating efficiency and reliability of smart grids. Meanwhile, the characteristics that deep integration of cyber-physical system can make smart grid face new security problems caused by false data injection attack. To maintain a safe and stable operation of smart grids, timely detection and defense of the emerging false data injection attacks, such as biased injection attack, is crucial. For this reason, this paper aims at developing a detection-based active defense mechanisms against biased injection attacks via robust adaptive controller. Through the established physical dynamic power model, an improved adaptive observer-based detection algorithm is proposed. Through the design of observer parameters, the proposed adaptive observer can enhance the accuracy of estimation state. In contrast to well-known attack detection methods for smart grids, the performance of attack detection under the developed detection algorithm can be effectively improved, such as the accuracy of state estimation and false positive rate. Through the above results provided by the attack detection, a robust adaptive controller-based active defense method is further developed. The proposed method can offset the impact of biased injection attack to maintain the stable running of power system. Simulation studies demonstrate the reliable response of the developed active defense method against biased injection attacks.

RISE-based adaptive control of electro-hydraulic servo system with uncertain compensation.

Electro-hydraulic servo system (EHSS) plays an important role in many industrial and military applications. However, its high-performance tracking control is still a challenging mission due to its nonlinear system dynamics and model uncertainties. In this paper, a novel adaptive robust integral method of the sign of the error (ARISE) with extended state observer (ESO) is proposed. Firstly, the nonlinear mathematical model of typical EHSS with modeling uncurtains and uncertain nonlinear is established. Then, ESO is used to estimate the state and lumped disturbance, of which the unknown parameter estimations can be updated by the novel adaptive law. Results shows that the novel controller achieves better tracking performance in maximum tracking error, average tracking error and standard deviation of the tracking error.

Deformed dose restoration to account for tumor deformation and position changes for adaptive proton therapy.

Online adaptation during intensity-modulated proton therapy (IMPT) can minimize the effect of inter-fractional anatomical changes, but remains challenging because of the complex workflow. One approach for fast and automated online IMPT adaptation is dose restoration, which restores the initial dose distribution on the updated anatomy. However, this method may fail in cases where tumor deformation or position changes occur. To develop a fast and robust IMPT online adaptation method named "deformed dose restoration (DDR)" that can adjust for inter-fractional tumor deformation and position changes. THE DDR METHOD COMPRISES TWO STEPS: : (1) calculation of the deformed dose distribution, and (2) restoration of the deformed dose distribution. First, the deformable image registration (DIR) between the initial clinical target volume (CTV) and the new CTV were performed to calculate the vector field. To ensure robustness for setup and range uncertainty and the ability to restore the deformed dose distribution, an expanded CTV-based registration to maintain the dose gradient outside the CTV was developed. The deformed dose distribution was obtained by applying the vector field to the initial dose distribution. Then, the voxel-by-voxel dose difference optimization was performed to calculate beam parameters that restore the deformed dose distribution on the updated anatomy. The optimization function was the sum of total dose differences and dose differences of each field to restore the initial dose overlap of each field. This method only requires target contouring, which eliminates the need for organs at risk (OARs) contouring. Six clinical cases wherein the tumor deformation and/or position changed on repeated CTs were selected. DDR feasibility was evaluated by comparing the results with those from three other strategies, namely, not adapted (continuing the initial plan), adapted by previous dose restoration, and fully optimized. In all cases, continuing the initial plan was largely distorted on the repeated CTs and the dose-volume histogram (DVH) metrics for the target were reduced due to the tumor deformation or position changes. On the other hand, DDR improved DVH metrics for the target to the same level as the initial dose distribution. Dose increase was seen for some OARs because tumor growth had reduced the relative distance between CTVs and OARs. Robustness evaluation for setup and range uncertainty (3 mm/3.5%) showed that deviation in DVH-bandwidth for CTV D95% from the initial plan was 0.4 ± 0.5% (Mean ± S.D.) for DDR. The calculation time was 8.1 ± 6.4 min. An online adaptation algorithm was developed that improved the treatment quality for inter-fractional anatomical changes and retained robustness for intra-fractional setup and range uncertainty. The main advantage of this method is that it only requires target contouring alone and saves the time for OARs contouring. The fast and robust adaptation method for tumor deformation and position changes described here can reduce the need for offline adaptation and improve treatment efficiency. This article is protected by copyright. All rights reserved.

A robust and accurate adaptive approximation method for a diffuse-interface model of binary-fluid flows

We present an adaptive simulation framework for binary-fluid flows, based on the Abels–Garcke–Grün Navier–Stokes–Cahn–Hilliard (AGG NSCH) diffuse-interface model. The adaptive-refinement procedure is guided by a two-level hierarchical a-posteriori error estimate, and it effectively resolves the spatial multiscale behavior of the diffuse-interface model. To improve the robustness of the solution procedure and avoid severe time-step restrictions for small-interface thicknesses, we introduce an ɛ-continuation procedure, in which the diffuse interface thickness (ɛ) are enlarged on coarse meshes, and the mobility is scaled accordingly. To further accelerate the computations and improve robustness, we apply a modified Backward Euler scheme in the initial stages of the adaptive-refinement procedure in each time step, and a Crank–Nicolson scheme in the final stages of the refinement procedure. To enhance the robustness of the nonlinear solution procedure, we introduce a partitioned solution procedure for the linear tangent problems in Newton’s method, based on a decomposition of the NSCH system into its NS and CH subsystems. We conduct a systematic investigation of the conditioning of the monolithic NSCH tangent matrix and of its NS and CH subsystems for a representative 2D model problem. To illustrate the properties of the presented adaptive simulation framework, we present numerical results for a 2D oscillating water droplet suspended in air, and we validate the obtained results versus those of a corresponding sharp-interface model.

Variational Bayesian approximation based robust adaptive single beacon navigation method

For the practical application of single beacon navigation system, effective sound velocity (ESV) setting error, measurement outliers and unknown noise statistical parameters are the major sources of positioning error. Although the unknown ESV of single beacon navigation system has been well studied, the other two sources are less concerned. The performance of existing state-of-art navigation methods will deteriorate when there are measurement outliers or noise statistical parameters setting error. This paper aims to fill the gap. We model the process and measurement noise of each state and measurement component based on their own stochastic properties, so that, the influence of measurement outliers can be incorporated into stochastic model. The unknown noise statistical parameters are regarded as random variables and the variational Bayesian approximation method is used to estimate them along with the navigation state. The corresponding robust adaptive single beacon navigation method is proposed. Moreover, the simulation and field data demonstrate that the proposed method is superior to the existing state-of-art navigation methods in the presence of measurement outliers and unknown noise statistical parameters.

Robust-adaptive method of power unit control based on the operational assessment of fuel quality indicators

The article develops systems for the operational assessment of fuel quality indicators, schematic diagrams of sensors, evaluation criteria that allow: to provide the desired values of indicators for the operational assessment and implementation of operational properties of the types of fuel used that determine the quality of the power unit, such as fuel efficiency, emissions of harmful substances, noise level; to identify the main factors at all stages of the transportation process. The article presents the results of a study on the formation of a robust adaptive control method of a power unit based on the developed systems, schemes and evaluation criteria. In order to optimize the control system of the power unit, a three-level information and control model of the fuel quality assessment system on the vehicle has been developed. As a criterion of optimality in terms of the quality of the fuel used, the developed fuel utilization factor of the power unit was used.

TEO-MCRP: Thermal exchange optimization-based clustering routing protocol with a mobile sink for wireless sensor networks

Sensor nodes consume their energy quickly due to their continuous perception, routing, and internal circuit mechanisms in Wireless Sensor Networks (WSNs). Especially, the nodes that are close to the sink node consume their energy more quickly, causing the hot spot problem. In this direction, the development of robust clustering and adaptive mobile routing methods is extremely significant to ensure energy efficiency in WSNs. For this purpose, a new mobile clustering routing protocol based on Thermal Exchange Optimization (TEO) inspired by Newton's cooling law, called TEO-MCRP, is presented for heterogeneous WSNs. In the present protocol, two different algorithms are proposed for Cluster Head (CH) selection and Mobile Sink (MS) path detection with objective functions including independent fitness parameters. With the manner of minimizing these objective functions, effective CH selection and MS trajectory determination have been performed for each mobile round using the temperature equations in the TEO algorithm. In parallel, an MS node gathers data from all CHs and transmits all data to the Base Station (BS). The performance results strikingly illustrate that the proposed protocol is better than other algorithms. Due to minimizing packet conflicts and losses, the packet delivery rate was achieved at 99.784% in the network

Cyberattack-resilient load forecasting with adaptive robust regression

Cyberattacks in power systems that alter the input data of a load forecasting model have serious, potentially devastating consequences. Existing cyberattack-resilient work focuses mainly on enhancing attack detection. Although some outliers can be easily identified, more carefully designed attacks can escape detection and impact load forecasting. Here, a cyberattack-resilient load forecasting approach based on an adaptive robust regression method is proposed, where the observations are trimmed based on their residuals and the proportion of the trim is adaptively determined by an estimation of the contaminated data proportion. An extensive comparison study shows that the proposed method outperforms the standard robust regression in various settings.

Robust adaptive control of uncertain nonlinear systems with unmodeled dynamics using command filter

AbstractThis article proposes a robust adaptive controller design method for strict feedback nonlinear systems with unmodeled dynamics and input saturation. By using command filters, the explosion of complexity is obviated. The influence of filter errors bought in by command filters is eliminated by introducing compensating signals. Moreover, by skillfully constructing annth‐order auxiliary dynamic system, the effect of input saturation is avoided, where the disadvantages of conventional methods based on the hyperbolic tangent function are overcome. To tackle the effect of unmodeled dynamics which can destroy the performance and even destabilize the system, based on input‐to‐state stability theory, a dynamic signal is designed. Finally, the Lyapunov theory‐based stability analysis proves that all the closed‐loop signals are uniformly ultimately bounded. The effectiveness of the proposed controller design scheme is verified in simulation experiments.