Robust Adaptive Method Research Articles

A Robust Adaptive Rank-Reduction Method for 3D Diffraction Separation and Imaging

A Robust Adaptive Rank-Reduction Method for 3D Diffraction Separation and Imaging

Tracking control of uncertain Euler–Lagrange systems with fading and saturating actuations: A low‐cost neuroadaptive proportional‐integral‐derivative approach

AbstractMany important engineering systems can be classified as Euler–Lagrange (EL) systems. In this work we develop a new Proportional‐Integral‐Derivative (PID) ‐based tracking control solution for uncertain EL systems subject to actuation failures and saturation. Two set of control algorithms are developed using robust adaptive and neuroadaptive methods, which are shown to exhibit several salient features: (1) the control schemes are of PID form, hence is characterized with simplicity in structure and intuition in concept; (2) the PID gains in the scheme are automatically updated by analytic algorithms with no need for manual tuning, rendering the control scheme more user‐friendly; and (3) the developed control algorithms are robust against nonvanishing disturbances, adaptive to unknown virtual parameter and immune to partial actuation effectiveness faults.

Udwadia-Kalaba Equation Based Adaptive Robust Control for Two-wheeled Inverted Pendulum System: Underactuation and Uncertainty

A new adaptive robust control method based on Udwadia-Kalaba(U-K) approach which can be applied to the underactuated system is designed and used to two-wheeled inverted pendulum system in this paper. We separate this typical underactuated system into two subsystems(forward subsystem and yaw subsystem), which are fully underactuated and actuated. For these different subsystems, we use different control methods. We apply an adaptive robust control method which has been proved many times to the fully actuated subsystem. Based on this adaptive robust method, a new control strategy can be redesigned and applied to the underactuated subsystem by modifying the adaptive law and other things. This adaptive robust control with a leakage-type adaptive law could guarantee the uniform boundedness and uniform ultimate boundedness of the system. Finally, the simulation is executed to demonstrate the advantage and simplicity of the proposed method.

A New Robust Adaptive Fusion Method for Double-Modality Medical Image PET/CT

A new robust adaptive fusion method for double-modality medical image PET/CT is proposed according to the Piella framework. The algorithm consists of the following three steps. Firstly, the registered PET and CT images are decomposed using the nonsubsampled contourlet transform (NSCT). Secondly, in order to highlight the lesions of the low-frequency image, low-frequency components are fused by pulse-coupled neural network (PCNN) that has a higher sensitivity to featured area with low intensities. With regard to high-frequency subbands, the Gauss random matrix is used for compression measurements, histogram distance between the every two corresponding subblocks of high coefficient is employed as match measure, and regional energy is used as activity measure. The fusion factor d is then calculated by using the match measure and the activity measure. The high-frequency measurement value is fused according to the fusion factor, and high-frequency fusion image is reconstructed by using the orthogonal matching pursuit algorithm of the high-frequency measurement after fusion. Thirdly, the final image is acquired through the NSCT inverse transformation of the low-frequency fusion image and the reconstructed high-frequency fusion image. To validate the proposed algorithm, four comparative experiments were performed: comparative experiment with other image fusion algorithms, comparison of different activity measures, different match measures, and PET/CT fusion results of lung cancer (20 groups). The experimental results showed that the proposed algorithm could better retain and show the lesion information, and is superior to other fusion algorithms based on both the subjective and objective evaluations.

Multi-objective two-stage adaptive robust planning method for an integrated energy system considering load uncertainty

An integrated energy system (IES) is considered as an effective approach to reduce carbon emissions, lower energy supply costs and increase system flexibility. As numerous energy conversion and storage devices with various features have been developed, determining their types and capacity size and optimizing the synergic action of all selected energy devices under load uncertainty have become challenging issues during the planning and designing of a new IES. To address these issues, a load uncertainty model is constructed by integrating the regional equivalent standard building-integrated load prediction method, the robust uncertainty set method and the fuzzy C-means clustering algorithm. Based on this uncertainty model, this paper proposes a planning method that incorporates the fuzzy multi-objective decision and two-stage adaptive robust optimization methods for handling these challenging issues. The multi-objective decision method enables comprehensive optimization based on the system's economic performance, carbon emissions and grid integration level. The two-stage adaptive robust optimization method aims to optimize the single-objective problem transformed by the multi-objective decision method. The planning method is implemented in an industrial park in the Baoding City of China. The influence of load budget uncertainty, annual load growth rate and energy purchase price is then investigated. The results show that the load budget uncertainty and annual load growth rate only affect the optimal capacity size of the selected devices, whereas the energy purchase price influences both the optimal device types and their capacity size. The optimal capacity size remains constant when the load budget uncertainty exceeds 3. This study provides a methodology to select the suitable device types and determine their capacity size for IES under load uncertainty, which will benefit the design of a new IES.

A robust adaptive indirect in-motion coarse alignment method for GPS/SINS integrated navigation system

Strap-down inertial navigation system (SINS) is an autonomous sensor that can be used for car-mount in-motion coarse alignment. To overcome the shortcoming of high computational burden for the existing in-motion coarse alignment method, we propose a novel fast indirect in-motion coarse alignment method. In the proposed method, the measurement model is constructed by using the output of a global positioning system (GPS) and a SINS, and the rotation vector is used as the attitude parameterization to establish the process model. Moreover, considering that existing linear state estimators cannot always yield a satisfactory performance when the distribution of the measurement noise deviates from the assumed Gaussian distribution and inaccurate noise covariance matrix exists in the filtering model. We propose a novel adaptive Student's t-based Kalman filter (STKF) to overcome these challenges. Car-mounted simulation and experiments show the correctness of the proposed in-motion alignment method and adaptive STKF.

Parallel hybrid mesh adaptation by refinement and coarsening

In this paper we propose a robust adaptation method for hybrid mesh including both refinement and coarsening. Elements of different types are refined through isotropic subdivision in a similar way. Their unrefined neighbours are converted to polyhedrons, which benefits to localize mesh refinement. Mesh elements are coarsened by reversing refinement based on a multi-levels data structure. Elements subdivision can be conducted in entirely parallel mode by introducing a non-overlap number range for each processor. Then feature data is devised to recover the uniqueness of new ghost points and elements through parallel communication. A repartition-migration method is adopted to rebalance the computing load. We illustrate our method by an application of flow simulation. With mesh adaptation, the structures of shocks and vortices are resolved better.

Robust Adaptive Control for Nonlinear Aircraft System with Uncertainties

Model reference adaptive control (MRAC) schemes are known as an effective method to deal with system uncertainties. High adaptive gains are usually needed in order to achieve fast adaptation. However, this leads to high-frequency oscillation in the control signal and may even make the system unstable. A robust adaptive control architecture was designed in this paper for nonlinear aircraft dynamics facing the challenges of input uncertainty, matched uncertainty, and unmatched uncertainty. By introducing a robust compensator to the MRAC framework, the high-frequency components in the control response were eliminated. The proposed control method was applied to the longitudinal-direction motion control of a nonlinear aircraft system. Flight simulation results demonstrated that the proposed robust adaptive method was able to achieve fast adaptation without high-frequency oscillations, and guaranteed transient performance.

Unsteady flow simulation with mesh adaptation

Mesh adaptation is a reliable and effective method to improve the precision of flow simulation with computational fluid dynamics. Mesh refinement is a common technique to simulate steady flows. In order to dynamically optimize the mesh for transient flows, mesh coarsening is also required to be involved in an iterative procedure. In this paper, we propose a robust mesh adaptation method, both refinement and coarsening included. A data structure of [Formula: see text]-way tree is adopted to save and access the parent–children relationship of mesh elements. Local element subdivision is employed to refine mesh, and element mergence is devised to coarsen mesh. The unrefined elements adjacent to a refined element are converted to polyhedrons to eliminate suspending points, which can also prevent refinement diffusing from one refined element to its neighbors. Based on an adaptation detector for vortices recognizing, the mesh adaptation was integrated to simulate the unsteady flow around a tri-wedges. The numerical results show that the mesh zones where vortices located are refined in real time and the vortices are resolved better with mesh adaptation.

Active Ordinal Querying for Tuplewise Similarity Learning

Many machine learning tasks such as clustering, classification, and dataset search benefit from embedding data points in a space where distances reflect notions of relative similarity as perceived by humans. A common way to construct such an embedding is to request triplet similarity queries to an oracle, comparing two objects with respect to a reference. This work generalizes triplet queries to tuple queries of arbitrary size that ask an oracle to rank multiple objects against a reference, and introduces an efficient and robust adaptive selection method called InfoTuple that uses a novel approach to mutual information maximization. We show that the performance of InfoTuple at various tuple sizes exceeds that of the state-of-the-art adaptive triplet selection method on synthetic tests and new human response datasets, and empirically demonstrate the significant gains in efficiency and query consistency achieved by querying larger tuples instead of triplets.

Three‐dimensional impact angle‐constrained adaptive guidance law considering autopilot lag and input saturation

AbstractThis work investigates three‐dimensional accurate guidance problem in the presence of impact angle constraint, input saturation, autopilot lag, and external disturbance, and presents a robust adaptive guidance method for maneuvering targets. More specifically, based on integral Lyapunov control algorithm, a robust guidance law, which can drive both terminal line‐of‐sight angle error and its rate to a small region around zero, while resisting the terrible influence caused by external disturbance, is proposed in this work. To deal with input saturation, guidance command is separated into two parts: real input and saturation error, and an adaptive control technique is employed to compensate the influence resulting from external disturbance and saturation error. Moreover, regarding autopilot lag as a first‐order dynamics, a backstepping designed controller with an adaptive term is proposed. Numerical simulations are carried out and their results demonstrate the proposed properties.

Kinetic behavior evaluation of electromagnetic valve train subject to exhaust gas force

In recent years, great efforts have been put into fully variable electromagnetic valve train (EMVT) technologies in order to improve the power and economic performance of IC engines. However, the application of EMVT to exhaust system faces a more severe operating condition compared with intake system: the sizeable cycle-to-cycle combustion gas force makes serious disturbance to the movement of valve train, and a complicated kinetic behavior is generated because of the interplay of gas flow and valve opening motion. As a result, the exhaust efficiency and engine performance are negatively affected. Therefore, this paper analyzes the evolution mechanism of the gas force acting on exhaust valve, and explores the disturbance model in consideration of the flow stagnation effect. Based on this, a control-oriented model of EMVT is proposed, and the adaptive robust method is used for the motion control. The dynamic characteristics of EMVT are verified by the simulation and experimental results, which show that the control method has a strong anti-interference capability against the exhaust gas force. With the increase of gas force, the opening motion of EMVT would be delayed, but the opening transition time remains basically unchanged.

Compressor surge control using a new robust adaptive method in the presence of uncertainty and unmatched disturbance

The surge is the most common and important instability associated with compressors and its control is very important due to its damaging effects on the compressor system. This paper presents a new method of surge control using a robust adaptive method in which the stability of the system is guaranteed according to the Lyapunov method. In the used model, the unmatched disturbance is introduced into the compressor system, and the compressor characteristic curve is assumed to be unknown. The characteristic curve of the throttle valve is also considered unknown. The comparative results in the simulation section illustrate the capabilities of the proposed method.

A robust adaptive grid method for singularly perturbed Burger-Huxley equations

<p style='text-indent:20px;'>In this paper, an adaptive grid method is proposed to solve one-dimensional unsteady singularly perturbed Burger-Huxley equation with appropriate initial and boundary conditions. Firstly, we use the classical backward-Euler scheme on a uniform mesh to approximate time derivative. The resulting nonlinear singularly perturbed semi-discrete problem is linearized by using Newton-Raphson-Kantorovich approximation method which is quadratically convergent. Then, an upwind finite difference scheme on an adaptive nonuniform grid is used for space derivative. The nonuniform grid is generated by equidistribution of a positive monitor function, which is similar to the arc-length function. It is shown that the presented adaptive grid method is first order uniform convergent in the time and spatial directions, respectively. Finally, numerical results are given to validate the theoretical results.</p>

A New Robust Identification Method for Transmission Line Parameters Based on ADALINE and IGG Method

Accurate transmission line parameters are the basis of power system calculations. Aiming at obtaining accuracy online transmission line parameters in the case of large random noises even bad data in Phasor Measurement Unit (PMU) measurements, which occurs frequently in the practice, a new adaptive robust identification method combining adaptive linear neuron (ADALINE) and traditional robust IGG (Institute of Geodesy & Geophysics, Chinese Academy of Sciences) method is proposed. In detail, first, the identification model of transmission line parameters is presented based on the multi-period PMU measurements at both ends of the transmission line. Then, a parameter solving model based on ADALINE is established. Furthermore, to fully use measurement information, the adaptive robust ADALINE (ARA) are proposed, which applies the robust IGG weight function (Scheme I) to ADALINE to realize “three segments” robust identification. In addition, to improve the robustness, the expectation and variance of the equation residual sequence are estimated adaptively with the median principle to adjust the threshold for the IGG function to assure robustness (TAR), which is independent of the known information for the error of the measurement equipment. The cases based on PSCAD simulated data and measured data show the effectiveness and engineering practicality of the proposed method.

Background error covariance iterative updating with invariant observation measures for data assimilation

In order to leverage the information embedded in the background state and observations, covariance matrices modelling is a pivotal point in data assimilation algorithms. These matrices are often estimated from an ensemble of observations or forecast differences. Nevertheless, for many industrial applications the modelling still remains empirical based on some form of expertise and physical constraints enforcement in the absence of historical observations or predictions. We have developed two novel robust adaptive assimilation methods named CUTE (Covariance Updating iTerativE) and PUB (Partially Updating BLUE). These two non-parametric methods are based on different optimization objectives, both capable of sequentially adapting background error covariance matrices in order to improve assimilation results under the assumption of a good knowledge of the observation error covariances. We have compared these two methods with the standard approach using a misspecified background matrix in a shallow water twin experiments framework with a linear observation operator. Numerical experiments have shown that the proposed methods bear a real advantage both in terms of posterior error correlation identification and assimilation accuracy.

Adaptive Robust Efficient Methods for Periodic Signal Processing Observed with Colours Noises

In this paper, we consider the problem of robust adaptive efficient estimating a periodic signal observed in the transmission channel with the dependent noise defined by non-Gaussian Ornstein-Uhlenbeck processes with unknown correlation properties. Adaptive model selection procedures, based on the shrinkage weighted least squares estimates, are proposed. The comparison between shrinkage and least squares methods is studied and the advantages of the shrinkage methods are analyzed. Estimation properties for proposed statistical algorithms are studied on the basis of the robust mean square accuracy defined as the maximum mean square estimation error over all possible values of unknown noise parameters. Sharp oracle inequalities for the robust risks have been obtained. The robust efficiency of the model selection procedure has been established.

Bio-Inspired Speed Curve Optimization and Sliding Mode Tracking Control for Subway Trains

Operation optimization for modern subway trains usually requires the speed curve optimization and speed curve tracking simultaneously. For the speed curve optimization, a multi-objective seeking issue should be addressed by considering the requirements of energy saving, punctuality, accurate parking, and comfortableness at the same time. But most traditional searching methods lack in efficiency or tend to fall into the local optimum. For the speed curve tracking, the widely applied proportional integral differential (PID) and fuzzy controllers rely on complicated parameter tuning, whereas robust adaptive methods can hardly ensure the finite-time convergence strictly, and thus are not suitable for applications in fixed time intervals of trains. To address the above-mentioned two problems, this paper presents a novel approach for speed curve seeking and tracking control. First, we present the random reinforcement genetic algorithm (GA) algorithm to avoid the local optimum efficiently. Then, a sliding mode controller is developed for speed curve tracking with bounded disturbance. The Lyapunov theory is adopted to prove that the system can be stabilized in the finite time. Finally, using the real datasets of Yizhuang Line in Beijing Subway, the proposed approach is validated, demonstrating its effectiveness and superiorities for the operation optimization.

An efficient and robust adaptive sampling method for polynomial chaos expansion in sparse Bayesian learning framework

Sparse polynomial chaos expansion has been widely used to tackle problems of function approximation in the field of uncertain quantification. The accuracy of PCE depends on how to construct the experimental design. Therefore, adaptive sampling methods of designs of experiment are raised. Classic designs of experiment for PCE are based on least-square minimization techniques, where the design space is only defined by the inputs without involving the responses of the system. To overcome this limitation, a novel adaptive sampling method is introduced in sparse Bayesian learning framework. The design point is enriched sequentially by maximizing a generalized expectation of loss function criterion which allows an effective use of all the information available, on which two adaptive strategies are derived to get a balance between the global exploration and the local exposition via the error information from the previous iteration. The numerical results show that the proposed method is superior to classic design of experiment in terms of efficiency and robustness.

A robust adaptive grid method for a nonlinear singularly perturbed differential equation with integral boundary condition

In this paper, the numerical solution of a nonlinear first-order singularly perturbed differential equation with integral boundary condition is considered. The discrete method is generated by a backward Euler formula and the grid is obtained by equidistributing a monitor function based on arc-length. We first give a rigorous error analysis for the numerical method of this problem on a grid that is constructed adaptively from a knowledge of the exact solution. A first-order rate of convergence, independent of the perturbation parameter, is established. Then, an a posteriori error bound and the corresponding convergence result are derived for the presented numerical scheme on an adaptive grid, which is constructed adaptively from a discrete approximation of the exact solution. At last, numerical experiments are given to illustrate our theoretical results.