Model Mismatch Research Articles

Enhanced BNC Approach for Non-Circular Signals Direction Finding with Sparse Array in the Scenario of Impulsive Noise

Sparse array is popular in the field of array signal processing. However, in direction of arrival (DOA) estimation, most research on sparse array assumes Gaussian noise, resulting in model mismatch in the practical scenarios of impulsive noise. We investigate the estimation performance of the bounded non-linear covariance (BNC) matrix based methods, which can be used to replace the original data covariance matrix of the received signal and achieve good robustness to the impulsive interference. We propose an enhanced BNC (EBNC) matrix with non-circular signal for nested array in the scenario of impulsive noise. The proposed EBNC matrix can fight against the impulsive noise outliers and its boundedness and convergence are shown. The proposed method's performance is assessed with simulations. Simulation results indicate that the proposed method provides better performance in DOA estimation than the classical BNC, correntropy-based covariance matrix and Gaussian covariance based methods, especially in highly impulsive noise scenario.

Cubic Hermite Interpolation and Lobatto Collocation for Nonlinear Sampled-Data Control

Nonlinear controls designed in continuous time suffer from an increasing model mismatch and performance degradation when implemented in sampled-data control loops at relatively low sampling rates. The model mismatch is reduced when higher order numerical integration is used as modeling basis to describe the open-loop and the desired closed-loop dynamics. We recently presented a modular and minimally invasive way to implement continuously designed nonlinear controls with high accuracy in discrete time based on Gauss-Legendre collocation. In this paper, we show how the main drawback of the latter approach, highly discontinuous control signals, is removed by using piecewise cubic spline interpolation, or equivalently, Lobatto collocation. We present the rationale behind our approach, set up the nonlinear systems of equations for the required one-step predictions of the target dynamics, and discuss several relations between Hermite, Lobatto and Gauss collocation. On a benchmark nonlinear simulation example, we illustrate and discuss the performance of the approach with increasing sampling times.

Linear Fault Estimators for Nonlinear Systems: An Ultra-Local Model Design

This paper addresses the problem of robust process and sensor fault reconstruction for nonlinear systems. The proposed method augments the system dynamics with an approximated internal linear model of the combined contribution of known nonlinearities and unknown faults - leading to an approximated linear model in the augmented state. We exploit the broad modeling power of ultra-local models to characterize this internal dynamics. We use a linear filter to reconstruct the augmented state (simultaneously estimating the state of the original system and the sum of nonlinearities and faults). Having this combined estimate, we can simply subtract the analytic expression of nonlinearities from that of the corresponding estimate to reconstruct the fault vector. Because the nonlinearity does not play a role in the filter dynamics (it is only used as a static nonlinear output to estimate the fault), we can avoid standard restrictive assumptions like globally (one-sided) Lipschitz nonlinearities and/or the need for Lipschitz constants to carry out the filter design. The filter synthesis is posed as a mixed H2/H∞ optimization problem where the effect of disturbances and model mismatches is minimized in the H∞ sense, for an acceptable H2 performance with respect to measurement noise.

Integrated Beam Tracking and Communication for (Sub-)mmWave Links With Stochastic Mobility

We consider the problem of active sensing and sequential beam tracking at mmWave frequencies and above. We focus on the setting of aerial communications between a quasi-stationary receiver and mobile transmitter, for example, a gateway array tracking a small agile drone, where we formulate the problem to be equivalent to actively sensing and tracking an optimal beamforming vector along the single dominant (often line-of-sight) path. In this setting, an ideal beam points in the direction of the angle of arrival (AoA) with sufficiently high resolution to ensure high beamforming gain. However, narrow beams are inherently sensitive to stochastic mobility. Without active sensing, narrow beam alignment can only be maintained in the case of highly predictive mobility with low prediction error. We pose the problem of active beam tracking and communication as a partially observed Markov decision problem (POMDP) with an expected average cost constraint. We establish the existence of a solution to the dynamic programming equation under reasonable technical assumptions. Drawing on the insight obtained from this solution, we propose an active joint sensing and communication algorithm for tracking the AoA through evolving a Bayesian posterior probability belief which is utilized for a sequential beamforming selection. Our algorithm relies on an integrated strategy of adaptive allocation of pilot versus data symbols as well as an active selection of beamforming vectors that trades off mutual information between the AoA and measurements (sensing) against spectral efficiency (communication). Through extensive numerical simulations, we analyze the performance of our proposed algorithm under various stochastic mobility models and demonstrate significant improvements over existing strategies. We also consider the impact of model mismatch on the performance of our algorithm which shows a good degree of robustness to model mismatch.

Community Detection With Known, Unknown, or Partially Known Auxiliary Latent Variables

Empirical observations suggest that in practice, community membership does not completely explain the dependency between the edges of an observation graph. The residual dependence of the graph edges are modeled in this paper, to first order, by auxiliary node latent variables that affect the statistics of the graph edges but <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">carry no information about the communities of interest.</i> We then study community detection in graphs obeying the stochastic block model and censored block model with auxiliary latent variables. We analyze the conditions for exact recovery when these auxiliary latent variables are unknown, representing unknown nuisance parameters or model mismatch. We also analyze exact recovery when these secondary latent variables have been either fully or partially revealed. Finally, we propose a semidefinite programming algorithm for recovering the desired labels when the secondary labels are either known or unknown. We show that exact recovery is possible by semidefinite programming down to the respective maximum likelihood exact recovery threshold.

A Distributed Synchronisation-based Predictor for Lur'e-type Nonlinear Systems

This paper proposes a distributed state predictor for nonlinear systems using sampled measured data. In particular, we attempt to develop a distributed state predictor for Lur'e systems based on anticipating synchronisation. Anticipating synchronisation is a leader-follower synchronisation such that the follower system synchronises with the future values of the leader system by using delayed feedback. Since the prediction scheme has a closed-loop structure, it is expected to have robustness for disturbance and model mismatch. In this paper, we develop a distributed predictor that consists of networks of multiple predictors based on anticipatory synchronisation to achieve the state prediction for sampled-data nonlinear systems. First, we consider a distributed state predictor in the case where all measurements are sampled synchronously. After that, we develop a distributed state predictor based on asynchronously acquired measurements. The validity of the obtained results is illustrated in a numerical example.

An End-to-End Approach for Rigid-Body Target Micro-Doppler Analysis Based on the Asymmetrical Autoencoding Network

Micro-Doppler analysis of rigid-body target is significant for attitude estimation and recognition of space objects. The traditional micro-Doppler analysis method for rigid-body targets includes two sequential steps. First, the time-frequency analysis is performed on the radar echo data of the target, and then the micro-Doppler curve of each scattering center is separated and extracted from the time-frequency map. The second step depends on the micro motion model of the target. In the micro-Doppler analysis of real rigid-body target, there are some problems such as the mismatch of the micro motion model, the incidence angle dependence of the scattering center position, and the partial occlusion of the scattering center. Therefore, it is very difficult to correctly extract the micro-Doppler curves of multiple scatterers. In this paper, an end-to-end micro-Doppler analysis method for rigid-body target based on deep learning network is proposed, which can directly separate and extract the micro-Doppler curves of multiple scatterers from the target echo data. Specifically, an Asymmetrical AutoEncoding (A2E) network equipped with a Data Pre-Processing (DP2) module is developed to extract Time-Frequency Curves (TFCs) from radar echos. Considering the sparseness of Time-Frequency Distribution (TFDs), we then develop a novel Energy-Concentration Objective (ECO) function based on Min-Max game to enhance curves energy while suppress the background energy. In practice, measured TFDs are rarely annotated, it restricts the generalization capability of the A2E from the simulation to the measurement. To bridge the gap, two-fold modifications are finally constructed: i) we insert a partial-shared branch of decoder to reconstruct the TFD from the DP2 module; ii) we regularize the ECO function with a knowledge preservation based reconstruction bound to further capture the characteristics of measured echos in a semi-supervised way at test time to relieve the domain-shift problem.

Mixed mismatch model predicted interfacial thermal conductance of metal/semiconductor interface

The acoustic mismatch model and diffuse mismatch model are widely used to calculate interfacial thermal conductance. These two models are respectively based on the assumption of extremely smooth and rough interfaces. Owing to the great difference between the actual interface structure and the two hypotheses, the predictions of these two models deviate greatly from the actual interfacial thermal conductance. The recently proposed mixed mismatch model considers the effect of interface structure on the ratio of phonon specular transmission to diffuse scattering transmission, and the prediction accuracy is improved. However, this model requires molecular dynamics simulation to obtain phonon information at the interface. In this work, the mixed mismatch model is simplified by introducing the measured roughness value, and the influence of interface structure on the contact area is taken into account to achieve a simple, fast and accurate prediction of interface thermal conductance. Based on this model, the interfacial thermal conductances of metals (aluminum, copper, gold) and semiconductors (silicon, silicon carbide, gallium arsenide, gallium nitride) are calculated and predicted. The results of Al/Si interface are in good agreement with the experimental results. This model is helpful not only in understanding the mechanism of interface heat conduction, but also in comparing with the measurement results.

Superresolution Composite SAR Imaging Method by Hierarchical Pattern With Attributed Scattering Priors

Compared with the ideal point scattering model, the attributed scattering center (ASC) model provides concise and physically relevant description of the man-made targets, such as the vehicles and ships, giving an efficient way to interpret the measurements of high-frequency and wide-angle synthetic aperture radar (SAR). Since the ASC model is complicated, it gives rise to a high-dimensional model parameter estimation problem, leading to a heavy computational burden. Aimed at this problem, in this article, a novel superresolution composite SAR imaging algorithm is proposed based on sparse representation, in which the ASC attributes are estimated by a hierarchical pattern for reducing the computational cost. In the proposed algorithm, the ASC model parameters, classified into three categories, are estimated coarsely via minimum variance pursuit (MVP). Then these coarse estimates are refined jointly using the consensus alternating direction method of multipliers (CADMM) optimization for polarimetric measurements. The spectrum extrapolation and replacement are performed for superresolution and reducing the impact of model mismatch, followed by a fast Fourier transform (FFT)-based operation to efficiently obtain the final superresolution image. The proposed approach not only possesses the ability of superresolution reconstruction with the target nonideal scattering features’ enhancement but also offers the physically relevant attributes on the premise of estimation accuracy improvement and calculation capacity reduction. Extensive experiments are conducted to corroborate the effectiveness of the proposed algorithm.

Persistently Feasible Robust Safe Control by Safety Index Synthesis and Convex Semi-Infinite Programming

Model mismatches prevail in real-world applications. Ensuring safety for systems with uncertain dynamic models is critical. However, existing robust safe controllers may not be realizable when control limits exist. And existing methods use loose over-approximation of uncertainties, leading to conservative safe controls. To address these challenges, we propose a control-limits aware robust safe control framework for bounded state-dependent uncertainties. We propose safety index synthesis to find a robust safe controller guaranteed to be realizable under control limits. And we solve for robust safe control via Convex Semi-Infinite Programming, which is the tightest formulation for convex bounded uncertainties and leads to the least conservative control. In addition, we analyze when and how safety can be preserved under unmodeled uncertainties. Experiment results show that our robust safe controller is always realizable under control limits and is much less conservative than strong baselines.

Application of microphone arrays to interference cancellation by using null broadening beamforming

Beamforming is an important technology for array systems, the spatial filtering can be achieved by focusing the main beam to the target and null point to the interference. Comparing to the noise suppression method based single-channel, the signal from beamforming output shows lower distortion and higher signal-to-interference plus noise ratio. This study is to investigate the null broadening (bearing, width and depth) control of beamforming, the purpose is to overcome the bearing error between interference and null by producing a broadening null, and to improve the interference signal suppression. Considering the requirements of real-time and broadband in the acoustic system, the fixed beamforming process is chosen and combined with the convex optimization technique. There are several advantages form fixed process that are better robustness due to avoiding the signal self-cancellation from the issue of model mismatch and low-complexity computation contributes to achieving the real-time and broadband spatial filtering in the present process. In the experiment of the chirp signal, the SNR of beamformer output is increased from 2 to 10.5 dB, the correlation coefficient for target is increased from 0.52 to 0.82, and the interference is reduced from 0.54 to 0.07. In the experiment of the broadband voice signal, the SNR of beamformer output is increased from 2 to 6.5 dB, the correlation coefficient for target is increased from 0.37 to 0.68, and the interference is reduced from 0.58 to 0.19. The experimental results verify that the proposed algorithm can effectively controlled the capability of null broadening and have better interference suppression than other algorithms.

URGLQ: An Efficient Covariance Matrix Reconstruction Method for Robust Adaptive Beamforming

The computational complexity of the conventional adaptive beamformer is relatively large, and the performance degrades significantly due to both the model mismatch errors and the unwanted signals in received data. In this paper, an efficient unwanted signal removal and Gauss-Legendre quadra-ture (URGLQ)-based covariance matrix reconstruction method is proposed. Different from the prior covariance matrix recon-struction methods, a projection matrix is constructed to remove the unwanted signal from the received data, which improves the reconstruction accuracy of the covariance matrix. Considering that the computational complexity of most matrix reconstruction algorithms are relatively large due to the integral operation, we proposed a Gauss-Legendre quadrature-based method to approximate the integral operation while maintaining the accu-racy. Moreover, to improve the robustness of the beamformer, the mismatch in the desired steering vector is corrected by maximizing the output power of the beamformer under a constraint that the corrected steering vector cannot converge to any interference steering vector. Simulation results and prototype experiment demonstrate that the performance of the proposed beamformer outperforms the compared methods and is much closer to the optimal beamformer in different scenarios.

A Multi-Subobject Approach to Dynamic Formation Target Tracking Using Random Matrices

Bird flocks are typical group targets with various linear formations and high dynamics due to swarm intelligence. This leads to several problems in traditional multi-subobject group target tracking such as shape model mismatch and false correlations. This paper proposes a multi-subobject approach to dynamic formation target tracking. The algebraic graph theory is introduced to analyze the structure of formation targets, then measurements are clustered and combined into subobjects. Based on the existing random matrix approach, two additional filtering branches, including collective filtering and internal structure filtering, are introduced to achieve the robust tracking performance of formation targets. The KL divergence between the prediction and updated densities of the collective filtering is used to determine the change of formation shape. The correct association between the measurements and the subobjects is realized by the guidance of the internal structure filter. Finally, the effectiveness of the proposed method is verified by the simulation and experimental results.

Learning-Based Nonlinear Model Predictive Controller for Hydraulic Cylinder Control of Ship Steering System

The steering mechanism of ship steering gear is generally driven by a hydraulic system. The precise control of the hydraulic cylinder in the steering mechanism can be achieved by the target rudder angle. However, hydraulic systems are often described as nonlinear systems with uncertainties. Since the system parameters are uncertain and system performances are influenced by disturbances and noises, the robustness cannot be satisfied by approximating the nonlinear theory by a linear theory. In this paper, a learning-based model predictive controller (LB-MPC) is designed for the position control of an electro-hydraulic cylinder system. In order to reduce the influence of uncertainty of the hydraulic system caused by the model mismatch, the Gaussian process (GP) is adopted, and also the real-time input and output data are used to improve the model. A comparative simulation of GP-MPC and MPC is performed assuming that the interference and uncertainty terms are bounded. Consequently, the proposed control strategy can effectively improve the piston position quickly and precisely with multiple constraint conditions.

An Onboard Adaptive Model for Aero-Engine Performance Fast Estimation

The onboard adaptive model is essential to the model-based control and diagnosis of the engine. However, current methods, such as the Kalman-based and the data-driven ones, cannot meet the demands of performance estimation well. Their self-tuning processes lead to a long period of model mismatch and, thus, degrade the quality of control and diagnosis, even causing engine failures. To overcome this disadvantage, a novel onboard adaptive model with fast estimation capability is proposed. The proposed method employs a component level model as the benchmark and introduces some scaling factors as the model tuners. These tuners are derived from the measurements and defined to quantify the characteristic deviations of the engine components at a certain operating condition. An algorithm with memory function is introduced to store the correlations between the tuners and the operating condition and, thus, predict these tuners according to the operating condition of inputs. By feeding the predicted tuners to the benchmark model, the engine performance can be estimated rapidly. Simulations are implemented to demonstrate the effectiveness of the proposed model. The results show that it has not only a high estimation accuracy at steady operating states, but also a short dynamic response time and the memory ability to avoid repeated self-tuning processes when the operating state of the engine varies.

Uncertainty estimation in wave energy systems with applications in robust energy maximising control

Under control action, wave energy devices typically display nonlinear hydrodynamic behaviour, making the design of energy maximising control somewhat onerous. One solution to approach the optimal performance for nonlinear control problem under model mismatches is to employ a linear control strategy, which can be robust to linear model mismatches. However, accurate characterisation of the uncertainty in the linear model is vital, if the controller is to adequately capture the full extent of the uncertainty, while not being overly conservative due to overestimation of the uncertainty. This paper describes a procedure, employing CFD-based numerical tank experiments, to accurately produce a nominal linear empirical transfer function model, along with an accurate estimate of the uncertainty bounds in that linear model, due to hydrodynamic uncertainty. A robust control case study is provided, illustrating the nominal model estimation process, and its corresponding uncertainty set, including the complete procedure, required to generate the robust controller. Robust control results, on the fully nonlinear CFD model, are provided to demonstrate the efficacy of the modelling and control philosophy.

Shoulder Range of Motion Measurements and Baseball Elbow Injuries: Ambiguity in Scientific Models, Approach, and Execution is Hurting Overhead Athlete Health

Elbow injuries are a significant and increasing issue in baseball. Elbow injuries account for 16% of all injuries sustained at the professional level and collegiate level. Because of the continued rise in injury rates, loss of performance value, and medical burden, sports medicine clinicians have attempted to research the causes underlying this injury epidemic in an attempt to help mitigate baseball elbow injuries. Shoulder range of motion (ROM) is the most researched clinical metric related to elbow injuries in baseball and has the greatest consensus as a viable prognostic factor specifically for medial elbow injury. Shoulder ROM is easy to measure, can be modified through stretching and manual therapy interventions, and can be easily assessed during preseason screening throughout all baseball levels. Despite a large number of studies and the widespread use of shoulder ROM in injury risk screening, current findings are unclear as to whether there is a true cause-effect relation with baseball elbow injuries. We argue that the conflicting findings revolving around the value of shoulder ROM measurements associated with baseball elbow injuries are the result of 4 gaps in the research approaches implemented to date: ambiguous research questions, mixed study populations, statistical models used, and shoulder ROM methodology. Specifically, there is a mismatch of methods, statistical models, and conclusions such as (1) investigating the association (i.e., correlation) between shoulder ROM measurements and injury and (2) investigating the cause-effect relation of shoulder ROM to baseball injuries. The purpose of this article is to detail the required scientific steps to evaluate whether preseason shoulder ROM is a potential causal factor for pitching elbow injury. We also provide recommendations to allow for future causal inferences to be made between shoulder ROM and elbow injury. This information will ultimately assist in informing clinical models of care and decision making for baseball throwers.

Implementation of a Model Predictive Control Strategy to Regulate Temperature Inside Plug-Flow Solar Reactor With Countercurrent Flow

Abstract Solar-driven thermochemical energy storage systems are proven to be promising energy carriers (solar fuels) to utilize solar energy by using reactive solid-state pellets. However, the production of solar fuel requires a quasi-steady-state process temperature, which represents the main challenge due to the transient nature of solar power. In this work, an adaptive model predictive controller (MPC) is presented to regulate the temperature inside a tubular solar reactor to produce solid-state solar fuel for long-term thermal storage systems. The solar reactor system consists of a vertical tube heated circumferentially over a segment of its length by concentrated solar power, and the reactive pellets (MgMn2O4) are fed from the top end and flow downwards through the heated tube. A countercurrent flowing gas supplied from the lower end interacts with flowing pellets to reduce it thermochemically at a temperature range of 1000—1500 °C. A low-order physical model was developed to simulate the dynamics of the solar reactor including the reaction kinetics, and the proposed model was validated numerically by using a 7-kW electric furnace. The numerical model then was utilized to design the MPC controller, where the control system consists of an MPC code linked to an adaptive system identification code that updates system parameters online to ensure system robustness against external disturbances (sudden change in the flow inside the reactor), model mismatches, and uncertainty. The MPC controller parameters are tuned to enhance the system performance with minimum steady-state error and overshoot. The controller is tested to track different temperature ranges between 500 °C and 1400 °C with different particles/gas mass flowrates and ramping temperature profiles. Results show that the MPC controller successfully regulated the reactor temperature within ± 1 °C of its setpoint and maintained robust performance with minimum input effort when subjected to sudden changes in the amount of flowing media and the presence of chemical reaction.

Cloud-Edge Collaborative Method for Industrial Process Monitoring Based on Error-Triggered Dictionary Learning

The development of cloud manufacturing enables data-driven process monitoring methods to reflect the real industrial process states accurately and timely. However, traditional process monitoring methods cannot update learned models once they are deployed to edge devices, which leads to model mismatch when confronted time-varying data. In addition, limited resources on the edge prevent it from deploying complex models. Therefore, this article proposes a novel cloud-edge collaborative process monitoring method. First, historical data of industrial processes are collected to establish a dictionary learning model and train the dictionary and classifier in the cloud. Then, the model is simplified and deployed to the edge. The edge layer monitors the process states, including fault detection and working condition recognition, and determines whether a model mismatch has occurred based on an error-triggered strategy. Both numerical simulation and industrial roasting process results verify the superiority of the proposed method.

Neural-Network-Based Adaptive Model Predictive Control for a Flexure-Based Roll-to-Roll Contact Printing System

High-precision contact force control is essential for continuous roll-to-roll contact printing via mechanical contact on flexible web substrates using stamps. Nonuniformly controlled stamp contact force will cause failures during printing, especially for large-area printing processes. Due to their high precision in positioning and force control, flexure mechanisms have been applied in roll-to-roll contact printing systems; however, conventional physical model-based control systems cannot manage the nonlinear effects that exist in flexure-based roll-to-roll contact printing systems. To achieve precise contact force control, we propose a neural-network-based adaptive model predictive control for a flexure-based roll-to-roll contact printing system. The nonlinearity of the flexure mechanism is learned and modeled by an artificial neural network. To eliminate the steady-state error caused by model mismatches and external disturbances, an online adaptive mechanism is designed via updating the biases of the output layer of the neural network model. Experimental results show that the root-mean-square error of the contact force can be controlled in the range of 0–0.075 N with balances on two ends of the print roller, outperforming a proportional–integral–derivative controller, a neural-network-based standard model predictive control (MPC) controller, and a neural-network-based robust MPC controller. The proposed control algorithm is implemented in a microcontact printing process that prints 45- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</i> m width gold patterns and achieves a variation of 0.3 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</i> m in the average gold line width at different locations on an 88.9-mm width flexible substrate. The uniform microscale printing results have shown the effectiveness of the proposed neural-network-based adaptive model predictive control in the applied printing process.