Time-varying Weights Research Articles

Online Inverse Optimal Control for Time-Varying Cost Weights.

Inverse optimal control is a method for recovering the cost function used in an optimal control problem in expert demonstrations. Most studies on inverse optimal control have focused on building the unknown cost function through the linear combination of given features with unknown cost weights, which are generally considered to be constant. However, in many real-world applications, the cost weights may vary over time. In this study, we propose an adaptive online inverse optimal control approach based on a neural-network approximation to address the challenge of recovering time-varying cost weights. We conduct a well-posedness analysis of the problem and suggest a condition for the adaptive goal, under which the weights of the neural network generated to achieve this adaptive goal are unique to the corresponding inverse optimal control problem. Furthermore, we propose an updating law for the weights of the neural network to ensure the stability of the convergence of the solutions. Finally, simulation results for an example linear system are presented to demonstrate the effectiveness of the proposed strategy. The proposed method is applicable to a wide range of problems requiring real-time inverse optimal control calculations.

Fuzzy adaptive resilient decentralized control of nonlinear interconnected cyber-physical systems under false data injection attacks

In this paper, a new fuzzy adaptive resilient decentralized control method is presented for the nonlinear interconnected cyber-physical systems under false data injection (FDI) attacks. Fuzzy logic systems (FLS) are used to model unknown nonlinear dynamics and the FDI attacks are represented by the sum of the system states and unknown time-varying bounded functions. To deal with the problem of the system states are unavailable under FDI attacks, a new coordinate transformation is proposed for control design. By using the backstepping control design theory and Nussbaum function property, a fuzzy adaptive resilient decentralized control scheme is developed, where the Nussbaum functions are employed to deal with the unknown time-varying weight. Then, the stability of the controlled systems is proved by constructing a appropriate Lyapunov function. Finally, the effectiveness of the proposed fuzzy adaptive resilient decentralized control scheme is verified by the simulation and comparison results.

Coopetition-dependent controller design for bipartite synchronization of cooperation–competition delayed memristive neural networks

In this paper, the bipartite synchronization problem for cooperation–competition delayed memristive neural networks (CCDMNNs) is addressed via designing a coopetition-dependent controller. Based on the coordinate transformation technique, an easy-to-analyze error system is constructed in the presence of cooperation–competition interactions. Two control strategies are proposed respectively to realize bipartite synchronization. First of all, a coopetition-dependent controller that can eliminate the impact of cooperation–competition interactions as well as the time-varying state-dependent memristive connection weights is designed. In combination with inequality techniques, the Lyapunov theory and the interval matrix method, an LMI-form criterion is established to ensure the bipartite synchronization of CCDMNNs. Moreover, an adaptive controller that can automatically adjusts the control parameters to achieve appropriate control gains is proposed to further improve the control performance. Finally, a numerical example is given to verify the correctness and superiority of the proposed control schemes.

Stability analysis for a Rao-Nakra sandwich beam equation with time-varying weights and frictional dampings

<abstract><p>In this paper, we studied the asymptotic behavior of solutions for a Rao-Nakra sandwich beam equation with time-varying weights and frictional damping terms acting complementarily in the domain. We studied the effect of the three damping on the asymptotic behavior of the energy function. Under nonrestrictive on the growth assumption on the frictional damping terms, we established exponential and general energy decay rates for this system by using the multiplier approach. The results generalized some earlier decay results on the Rao-Nakra sandwich beam equation.</p></abstract>

Fuzzy Adaptive Finite-Time Resilient Control Against Unknown False Data Injection Attacks for MIMO Nonlinear Switched Systems With Unknown Dead Zone.

This article studies the finite-time adaptive resilient control problem for MIMO nonlinear switched systems with the unknown dead zone. The sensors of the controlled systems suffer from unknown false data injection (FDI) attacks so that all states cannot be directly applied to the design process of the controller. To address this negative impact of FDI attacks, a new coordinate transformation is designed in control design. Moreover, the Nussbaum gain technique is introduced to deal with the difficulty of unknown time-varying weights caused by FDI attacks. Based on the common Lyapunov function method, a finite-time resilient control algorithm is designed by employing compromised state variables, which ensures that all signals of the closed-loop systems are bounded under arbitrary switching rules even in the presence of unknown FDI attacks. Compared with the existing results, the proposed control algorithm not only enables the controlled systems to reach an equilibrium state in a finite time but also removes the assumption that the sign of the attack weights is positive. In the end, a practical simulation example proves that the designed control method is valid.

Observer-Based Adaptive Consensus Protocol Design for Multi-Agent Systems with One-Sided Lipschitz Nonlinearity

This paper considers the observer-based leader–follower consensus problem of multi-agent systems with one-sided Lipschitz conditions and quadratic inner-boundedness nonlinearity. Based on the relative outputs of neighboring agents, an adaptive protocol over a directed graph is designed via assigning a time-varying coupling weight to each node. Compared with the existing protocols, the proposed protocol can not only be utilized in the unidirectional flow of information, but it does not require any global graph information for its design. It has been shown that the established criteria can not only make the observer error tendency zero, but one can also achieve the leader–follower consensus of such nonlinear multi-agent systems. Furthermore, the gains of observer and protocol parameters can be constructed by solving the linear matrix inequalities (LMIs), which are conveniently checked. The results are illustrated by numerical simulations.

The GNAR-edge model: a network autoregressive model for networks with time-varying edge weights

Abstract In economic and financial applications, there is often the need for analysing multivariate time series, comprising of time series for a range of quantities. In some applications, such complex systems can be associated with some underlying network describing pairwise relationships among the quantities. Accounting for the underlying network structure for the analysis of this type of multivariate time series is required for assessing estimation error and can be particularly informative for forecasting. Our work is motivated by a dataset consisting of time series of industry-to-industry transactions. In this example, pairwise relationships between Standard Industrial Classification (SIC) codes can be represented using a network, with SIC codes as nodes and pairwise transactions between SIC codes as edges, while the observed time series of the amounts of the transactions for each pair of SIC codes can be regarded as time-varying weights on the edges. Inspired by Knight et al. (2020, J. Stat. Softw., 96, 1–36), we introduce the GNAR-edge model which allows modelling of multiple time series utilizing the network structure, assuming that each edge weight depends not only on its past values, but also on past values of its neighbouring edges, for a range of neighbourhood stages. The method is validated through simulations. Results from the implementation of the GNAR-edge model on the real industry-to-industry data show good fitting and predictive performance of the model. The predictive performance is improved when sparsifying the network using a lead–lag analysis and thresholding edges according to a lead–lag score.

A fusion method for capacity estimation of fast-charging lithium-ion battery under varied degradation mode

Recently, lithium-ion batteries with fast-charging capability have been gradually equipped in electric products. Capacity estimation specially developed for fast-charging batteries is still an open question, and most of the existing works are proposed for batteries charged under the 1C rate. However, incorrect fast-charging strategies may result in Li plating leading to battery capacity aging rapidly and degradation mode variation. Moreover, unprecise sampling by BMS increases the difficulty of accurately estimating battery capacity in such scenarios. Thus, a fusion prognostic method based on ensemble learning is proposed for the above issues in fast-charging batteries. Firstly, measurement-based health features are extracted from the portion charging phase. Then, the accuracy of the validation dataset-based strategy is proposed to achieve time-varying weight allocation. Finally, a fusion prognostic model is constructed based on the above weight allocation strategy and health features. The effectiveness of the proposed fusion method is verified by a self-design fast-charging battery dataset that maintains stable and reliable performance under sparse sampling and degradation mode variation conditions. In addition, the robustness and adaptability are validated by the comparison experiments with traditional ensemble learning-based methods, in which accuracy is improved by 27.5% minimally.

Real-time evacuation route optimization in the fire scenarios of cruise ships

The optimization of evacuation routes for evacuees in fire scenarios on cruise ships directly affects evacuation efficiency. A time-varying equivalent weight Dijkstra algorithm (TVEWDA) is proposed, combining a fire dynamics simulator (FDS) to find optimal evacuation routes in case of a sudden cruise ship fire. First, the FDS software is used to simulate a cruise ship's single and multi-source fire scenarios. Second, the Dijkstra algorithm using only distance between nodes is improved by considering the effects of CO concentration, visibility, and temperature. The evacuees’ speed is taken into consideration within the TVEWDA. The effects of congestion are considered in the evacuees’ moving speed. Finally, the TVEWDA and a cruise ship evacuation network model are integrated to develop a real-time evacuation route optimization model. Simulation results show that the evacuation time achieved by the TVEWDA is 22% shorter than that of the Dijkstra algorithm using only distance between nodes and 11% shorter than that of an improved social force model in the multi-source fire scenario. This approach is well-suited to guide the evacuation of cruise ship evacuees in the event of single-source or multi-source fires, effectively ensuring the safety of passengers and the preservation of their property.

Statin therapy and the incidence of atherosclerotic cardiovascular events after kidney transplantation.

Statins are recommended in kidney transplant recipients (KTRs)-a population with a high risk of major cardiovascular (CV) events. However, the literature data on the effectiveness of statins in KTRs are sparse and inconclusive. The present study's objective was to evaluate the association between statin exposure and atherosclerotic CV events in KTRs and the biochemical effectiveness of statins on the lipid profile. A total of 318 consecutive KTRs managed at a single center between 2006 and 2019 were retrospectively included. Those exposed to statins after transplantation were incident users. In all users, statins were indicated for primary CV prevention. Lipid profiles, the occurrence of any atherosclerotic CV events (stroke, myocardial infarction, other atherosclerotic CV events and atherosclerotic CV deaths) were documented comprehensively. We applied Cox models that included statin exposure as a time-dependent covariate fitted with time-varying inverse probability treatment weighting (IPTW) to assess the effectiveness of statins on atherosclerotic CV events and on all CV events. We built linear mixed models to assess the biochemical effectiveness of statins. During a median (interquartile range) follow-up period of 6.0 (3.9-10.0) years, 27 atherosclerotic CV events occurred in 26 patients. In the Cox models fitted with time-varying IPTW, exposure to statins was not associated with a decrease in atherosclerotic CV events; the hazard ratio was 1.16 (95% confidence interval 0.53-2.53) (P=.700). In the linear mixed models, statin exposure was associated with significant decrease over time in triglyceride and low-density lipoprotein cholesterol concentrations (P<.001). These results were consistent when stratified for the intensity of statin therapy. Even though the lipid profile improved, statin exposure was not associated with a decrease in CV events in this real-life, single-center, retrospective, long-term follow-up study of a KTR cohort. Larger, controlled studies are needed to confirm or refute these results.

SeaBil: Self-attention-weighted ultrashort-term deep learning prediction of ship maneuvering motion

Accurate prediction of motion dynamics fundamentally promotes the autonomy of intelligent ships, but faces great challenges in modeling mechanism. In this paper, to establish data-driven recurrent mapping within ship motion dynamics, an ultrashort-term deep learning predictor is innovatively developed by elaborately creating a self-attention-weighted bidirectional long short-term memory (Bi-LSTM) network in conjunction with 1-dimensional convolution (Conv-1D), named SeaBil. To be specific, combined with the sliding-window technique, the Conv-1D is devised to convert 5-dimensional-input samples consisting of course angle, yaw rate, roll angle, total speed and rudder angle into 1-D-feature vectors, thereby extracting coupled feature maps within the current data-window. The Bi-LSTM is further deployed to recurrently learn forward and reverse feature maps of ship motion time-series data. Self-attention mechanism cascaded in the serial is employed to adaptively assign time-varying weights of sample instants within the current window such that the ultrashort-term prediction of course angle, yaw rate, roll angle and total speed can be achieved. Eventually, comprehensive comparisons to typical approaches using real-world samples demonstrate the superiority of SeaBil in terms of ultrashort-term prediction accuracy.

Multi-objective fractional-order particle swarm optimization algorithms for data processing of multi-wavelength pyrometer.

This paper focuses the algorithm of the true temperature inversion for high-temperature targets with unknown emissivity by transforming multi-spectral true temperature inversion into multi-objective minimum optimization. Two improved fractional-order particle swarm optimizations (IFOPSO), high-order nonlinear time-varying inertia weight (Hntiw) IFOPSO and global-local best values (Glbest) IFOPSO, are proposed to obtain the true temperature by solving the multi-objective minimum optimization. Combining the inherent advantages of fractional-order calculus to jump out of the local extreme value, the Hntiw IFOPSO algorithm is proposed by replacing the linear time-varying inertia weights with nonlinear functions related to the total number of iterations and the current number of iterations. The Glbest IFOPSO algorithm is designed by using the global local optimal inertia weight and acceleration constant to update the particle velocity and position values, which improves the multi-objective optimization ability and the accuracy of the true temperature inversion. The effectiveness of the proposed methods is verified by the simulation with typical spectral emissivity models and the measured data from rocket tail flame.

Forecast combination using grey prediction with fuzzy integral and time-varying weighting in tourism

PurposeTourism demand forecasting is vital for the airline industry and tourism sector. Combination forecasting has the advantage of fusing several forecasts to reduce the risk of inappropriate model selection for analyzing decisions. This paper investigated the effects of a time-varying weighting strategy on the performance of linear and nonlinear forecast combinations in the context of tourism.Design/methodology/approachThis study used grey prediction models, which did not require that the available data satisfy statistical assumptions, to generate forecasts. A quality-control technique was applied to determine when to change the combination weights to generate combined forecasts by using linear and nonlinear methods.FindingsThe empirical results showed that except for when the Choquet fuzzy integral was used, forecast combination with time-varying weights did not significantly outperform that with fixed weights. The Choquet integral with time-varying weights significantly outperformed that with fixed weights for all model combinations, and had a superior forecasting accuracy to those of other combination methods.Practical implicationsThe tourism sector can benefit from the use of the Choquet integral with time-varying weights, by using it to formulate suitable strategies for tourist destinations.Originality/valueCombining forecasts with time-varying weights may improve the accuracy of the predictions. This study investigated incorporating a time-varying weighting strategy into combination forecasting by using CUSUM. The results verified the effectiveness of the time-varying Choquet integral for tourism forecast combination.

An Intelligent Robust Operator-Based Sliding Mode Control for Trajectory Tracking of Nonlinear Uncertain Systems

This paper investigates the problem of trajectory tracking control in the presence of bounded model uncertainty and external disturbance. To cope with this problem, we propose a novel intelligent operator-based sliding mode control scheme for stability guarantee and control performance improvement in the closed-loop system. Firstly, robust stability is guaranteed by using the operator-based robust right coprime factorization method. Secondly, in order to further achieve the asymptotic tracking and enhance the responsiveness to disturbance, a finite-time integral sliding mode control law is designed for fast convergence and non-zero steady-state error in accordance with Lyapunov stability analysis. Lastly, the controller’s parameters are automatically adjusted by the proved stabilizing particle swarm optimization with the linear time-varying inertia weight, which significantly saves tuning time with a remarkable performance guarantee. The effectiveness and efficiency of the proposed method are verified on a highly nonlinear ionic polymer metal composite application. The extensive numerical simulations are conducted and the results show that the proposed method is superior to the state-of-the-art methods in terms of tracking accuracy and high robustness against disturbances.

Steady-State Performance Analysis of the Arctangent LMS Algorithm With Gaussian Input

The adaptive filtering algorithms based on the arctangent cost function framework have shown robustness against impulsive noise. In this brief, the standard least mean square (LMS) algorithm under this framework is concentrated on, which is called the arctangent LMS (ATLMS) algorithm. The steady-state excess mean square error (EMSE) and mean square deviation (MSD) of the ATLMS algorithm are analyzed using the energy conservation relation. In stationary environment, both Gaussian and non-Gaussian situations are discussed. The closed-form expressions of the steady-state EMSE and MSD are obtained using Taylor’s expansion. In non-stationary environment, a first-order random-walk model is used for modeling the time-varying optimal weight. Theoretical steady-state performance and the optimal step size are derived. Simulation results under different noise environments verify the validity of our theoretical findings.

A Novel Hybrid Algorithm Based on Jellyfish Search and Particle Swarm Optimization

Metaheuristic optimization is considered one of the most efficient and powerful techniques of recent decades as it can deal effectively with complex optimization problems. The performance of the optimization technique relies on two main components: exploration and exploitation. Unfortunately, the performance is limited by a weakness in one of the components. This study aims to tackle the issue with the exploration of the existing jellyfish search optimizer (JSO) by introducing a hybrid jellyfish search and particle swarm optimization (HJSPSO). HJSPSO is mainly based on a JSO structure, but the following ocean current movement operator is replaced with PSO to benefit from its exploration capability. The search process alternates between PSO and JSO operators through a time control mechanism. Furthermore, nonlinear and time-varying inertia weight, cognitive, and social coefficients are added to the PSO and JSO operators to balance between exploration and exploitation. Sixty benchmark test functions, including 10 CEC-C06 2019 large-scale benchmark test functions with various dimensions, are used to showcase the optimization performance. Then, the traveling salesman problem (TSP) is used to validate the performance of HJSPSO for a nonconvex optimization problem. Results demonstrate that compared to existing JSO and PSO techniques, HJSPSO contributes in terms of exploration and exploitation improvements, where it outperforms other well-known metaheuristic optimization techniques that include a hybrid algorithm. In this case, HJSPSO secures the first rank in classical and large-scale benchmark test functions by achieving the highest hit rates of 64% and 30%, respectively. Moreover, HJSPSO demonstrates good applicability in solving an exemplar TSP after attaining the shortest distance with the lowest mean and best fitness at 37.87 and 36.12, respectively. Overall, HJSPSO shows superior performance in solving most benchmark test functions compared to other optimization techniques, including JSO and PSO. As a conclusion, HJSPSO is a robust technique that can be applied to solve most optimization problems with a promising solution.

Optimization of Square-shaped Bolted Joints Based on Improved Particle Swarm Optimization Algorithm

The bolted joint is widely used in heavy-duty CNC machine tools, which has huge influence on working precision and overall stiffness of CNC machine. The process parameters of group bolt assembly directly affect the stiffness of the connected parts. The dynamic model of bolted joints is established based on the fractal theory, and the overall stiffness of joint surface is calculated. In order to improve the total stiffness of bolted assembly, an improved particle swarm optimization algorithm with combination of time-varying weights and contraction factor is proposed. The input parameters are preloading of bolts, fractal dimension, roughness, and object thickness. The main goal is to maximize the global rigidity. The optimization results show that improved algorithm has better convergence, faster calculation speed, preferable results, and higher optimization performance than standard particle swarm optimization algorithm. Moreover, the global rigidity optimization is achieved.

The WRF-Based Incremental Analysis Updates and Its Implementation in an Hourly Cycling Data Assimilation System

Abstract An incremental analysis update (IAU) scheme is successfully implemented into a WRF/WRFDA-based hourly cycling data assimilation system with the goal to reduce the imbalance introduced by the high-frequency intermittent data assimilation, especially when radar data are included. With the application of IAU, the analysis increment is smoothly introduced into the model integration over a time window centered at the analysis time. As in digital filter initialization (DFI), the IAU scheme is able to limit large shocks in the early part of a model forecast. Compared to DFI, IAU does better in hydrometeor spinup and produces more continuous precipitation forecasts from cycle to cycle. The run with IAU is shown to improve the precipitation forecast skills (10+% for CSI scores) compared to the regular cycling forecasts without IAU. The data assimilation system with IAU is also able to accept more observations due to balanced first-guess fields. Comparable results are obtained in IAU tests when the time-varying weights are used versus constant weights. Because of its better property, the IAU with the time-varying weights is implemented in the operational system.

Transient response of energy harvesting systems with multi-well potential under Poisson white noise excitations

This paper studies the transient response of vibration-based energy harvesting (VEH) systems characterized by multi-well potential functions exposed to Poisson white noise excitations. With the recently developed radial basis function neural networks (RBFNN) approach, the generalized Fokker–Planck–Kolmogorov (GFPK) equation in three-dimensional state space for the transient probability density function (PDF) is solved. The trial solution is composed of a series of radial basis Gaussian functions, each multiplied by a time-varying weight coefficient. The method of Lagrange multiplier is introduced for the constraint of weight coefficients to normalized the trial solution of the PDF solution. As examples to validate the proposed solution approach, the mono-stable and tri-stable energy harvester systems are studied. Monte Carlo simulations (MCS) have been applied to check the analytical solution. Some theoretical trends of the harvested mean square voltage (MSV) as a function of system parameters are examined. The non-Gaussian excitation and time-varying nature of the system can significantly affect the MSV output. It is noted that this work provides researchers and engineers a tool to optimize energy harvesting systems operated in random environment.

Adaptive linear quadratic regulator for optimal structural control based on wavelet transform and genetic algorithm

PurposeIn real life, excitations are highly non-stationary in frequency and amplitude, which easily induces resonant vibration to structural responses. Conventional control algorithms in this case cannot guarantee cost-effective control effort and efficient structural response alleviation. To this end, this paper proposes a novel adaptive linear quadratic regulator (LQR) by integrating wavelet transform and genetic algorithm (GA).Design/methodology/approachIn each time interval, multiresolution analysis of real-time structural responses returns filtered time signals dominated by different frequency bands. Minimization of cost function in each frequency band obtains control law and gain matrix that depend on temporal-frequency band, so suppressing resonance-induced filtered response signal can be directly achieved by regulating gain matrix in the temporal-frequency band, leading to emphasizing cost-function weights on control and state. To efficiently subdivide gain matrices in resonant and normal frequency bands, the cost-function weights are optimized by a developed procedure associated to genetic algorithm. Single-degree-of-freedom (SDOF) and multi-degree-of-freedom (MDOF) structures subjected to near- and far-fault ground motions are studied.FindingsResonant band requires a larger control force than non-resonant band to decay resonance-induced peak responses. The time-varying cost-function weights generate control force more cost-effective than time-invariant ones. The scheme outperforms existing control algorithms and attains the trade-off between response suppression and control force under non-stationary excitations.Originality/valueProposed control law allocates control force amounts depending upon resonant or non-resonant band in each time interval. Cost-function weights and wavelet decomposition level are formulated in an elegant manner. Genetic algorithm-based optimization cost-efficiently results in minimizing structural responses.