This paper investigates a novel set-membership global estimation method for a class of discrete time-varying systems with unknown-but-bounded noises. Firstly, in order to improve the accuracy of the state estimation, a multi-sensor network structure is deployed for the considered system, in which the adjacent sensors can communicate with each other. Considering the different sampling rates of multiple sensors, a resampling strategy is proposed to transform the asynchronous sampling system into a synchronous one. Additionally, the unknown-but-bounded noises and system parameter variations are considered. A novel distributed set-membership filter with parameter variation is designed, and the optimal local state estimation ellipsoid is obtained by developing a convex optimization method. Subsequently, a soft actor-critic algorithm based on reinforcement learning is proposed, which fuses all local estimation ellipsoids from each sensor to obtain global estimation results, providing a solution for the considered system. Finally, a port crane system is employed for performance analysis to verify the feasibility and effectiveness of the proposed method.

This paper proposes a multi threshold attack detection method based on interval estimation methods for networked control systems with false data injection attacks and unknown-but-bounded noises. First, a two-step interval estimation method and the set membership estimation method are used to design the state predictor and estimator for the considered system in order to obtain the corresponding prediction sets and estimation sets. In addition, the zonotopes and ellipsoids are used to describe their outer boundaries. Second, the Monte Carlo method is adopted to calculate the intersection area of the two outer boundaries. Then, based on a large amount of area data obtained from prior experiments, cluster analysis is performed to obtain multiple thresholds for determining different attack scenarios. Finally, the effectiveness and accuracy of the proposed algorithm for detecting false data injection attacks are verified through a simulation example of an automated guided vehicle system model.

This paper proposes a multi threshold attack detection method based on interval estimation methods for networked control systems with false data injection attacks and unknown-but-bounded noises. First, a two-step interval estimation method and the set membership estimation method are used to design the state predictor and estimator for the considered system in order to obtain the corresponding prediction sets and estimation sets. In addition, the zonotopes and ellipsoids are used to describe their outer boundaries. Second, the Monte Carlo method is adopted to calculate the intersection area of the two outer boundaries. Then, based on a large amount of area data obtained from prior experiments, cluster analysis is performed to obtain multiple thresholds for determining different attack scenarios. Finally, the effectiveness and accuracy of the proposed algorithm for detecting false data injection attacks are verified through a simulation example of an automated guided vehicle system model.

This paper investigates a distributed set-membership estimation method which is adaptable to the network topology in the presence of unknown-but-bounded noises. The estimation performance is optimized by adaptively adjusting the neighboring node sets for each sensor. First, a group of set-membership estimators is deployed to obtain individual ellipsoid estimates, and error metrics are introduced to evaluate estimation error for different neighborhood topology configurations. Second, a greedy search algorithm is developed to identify suboptimal neighboring node sets to reduce computational complexity. Finally, an intersection-based strategy is proposed to integrate the estimates from neighboring nodes to obtain the final estimates. Simulation results demonstrate that the proposed algorithm effectively enhances accuracy and robustness while reducing computational complexity.

This paper aims to address the limitations of the conventional modular multilevel converter (MMC) control strategy in flexible middle-voltage direct current (MVDC) transmission technology with regard to voltage utilization and power transfer. In this paper, a novel control strategy is proposed for the MMC topology in DC transmission systems. This strategy is based on the use of a carrier phase-shifted sinusoidal wave modulation (CPS-SWM) system. The efficacy of this control strategy is evaluated through the implementation of simulation and analytical methods, in terms of technical feasibility, economic benefits, and system performance enhancement.

This article investigates the neural-network-based (NN-based) set-membership filtering issue for nonlinear systems. In order to lighten the network transmission burden and avoid data collisions, the weighted try-once-discard (WTOD) protocol is employed to regulate the signal transmission process, which provides higher transmission priority to the most needed data. Considering the data discarding problem of the WTOD protocol, a novel event-triggered compensation mechanism is proposed to compensate the measurement output processed by the WTOD protocol, thereby improving the filtering performance. Next, considering the nonlinear dynamics of the system and the unknown-but-bounded (UBB) noise interference, an NN-based set-membership filter is designed to solve the state estimation problem. In a unified set-membership framework, an neural-network (NN) weight adaptive tuning law and a state estimation algorithm are designed. Sufficient conditions are derived for the existence of the adaptive NN parameters and the NN-based set-membership filter, and two optimization problems are put forward to seek the optimal NN parameters and filtering parameters that make the filter performance optimal. Finally, illustrative examples demonstrate the effectiveness of the proposed compensation mechanism and filtering algorithm.

This article investigates the localization problem of the automatic guided vehicle (AGV) system. In order to improve the reliability and flexibility of the localization process, a distributed sensor network structure is introduced to realize the localization of the AGV. Moreover, considering the influence of unknown-but-bounded noise and the accuracy requirements of the localization, a novel global set-membership filtering approach is proposed to obtain accurate localization results including a distributed set-membership filtering (DSMF) strategy and a circumscribed rectangle method. First, a DSMF strategy is designed to obtain local state estimation ellipsoids. Sufficient conditions for the existence of the state estimation ellipsoids are derived and a convex optimization process is developed to obtain the optimal local estimation ellipsoids. Then, a circumscribed rectangle method is proposed to fuse all local state estimation ellipsoids and obtain global set-membership filtering results. The proposed fusion method does not have a complicated optimization process, and can obtain more accurate estimation ellipsoids than local estimation results. Performance analysis verifies the effectiveness of the proposed global set-membership filtering approach.

As the successor to Sunway TaihuLight, the new Sunway supercomputer has ultra-high computing capacity, but the unique heterogeneous architecture presents performance optimization challenges for High Performance Computing (HPC) applications. Performance modeling is an effective way to discover the performance bottlenecks and then improve the performance of HPC applications. Existing performance modeling techniques do not work well on large-scale HPC applications due to high overhead and low accuracy, and are not suitable for the heterogeneous architecture due to a lack of support for multi-resource parameters. To address the above challenges, we propose an automatic multi-parameter performance modeling method for HPC applications on the new Sunway supercomputer. First, a lightweight performance profiling method is proposed to achieve low overhead performance profiling. Then, performance models with multiple resource parameters based on the Fourier neural operator are built, achieving high prediction accuracy and generalization ability. Finally, the Fourier neural operator is extended on the new Sunway supercomputer to realize the performance modeling automatically. Experimental results show that the average prediction error is less than 10% and the average overhead is less than 4%, and the results are superior to the baselines.

The use of ensemble approaches in the single-objective evolutionary algorithms is ubiquitous, but ensembles of multiobjective evolutionary algorithms (MOEAs) have achieved relatively little attention. On the other hand, manually selecting a suitable MOEA to solve an actual multiobjective optimization problem (MOP) is time-consuming and challenging. Therefore, developing a multiobjective hyperheuristic to allocate computational resources for multiple MOEAs in an intelligent approach is beneficial. In this work, an autoselection strategy of MOEAs based on the performance indicator (MOEAS-PI) is introduced to alleviate the abovementioned problem. In the MOEAS-PI, the performance of each constituent MOEA in the pool is assessed according to a real-time and comprehensive performance indicator, which contains both the current and future performances. The MOEAS-PI is able to easily choose the best performing MOEA during the evolutionary process. Also, it can enhance the robustness of MOEAs and reduce the application risk. The effectiveness of the MOEAS-PI is carefully evaluated on 23 MOPs. Simulation results demonstrate that the MOEAS-PI is an effective and efficient method to integrate the advantages of each individual algorithm. Finally, the MOEAS-PI is utilized to solve a translation control problem of an immersed tunnel element under current flow. Experimental results reveal that the MOEAS-PI is a reliable and effective optimization approach to solve actual MOPs.Note to Practitioners—Multiobjective optimization problems (MOPs) have been commonly found in various fields. However, a single MOEA cannot guarantee its sufficient robustness and adaptability in solving MOPs. Therefore, this study aims to propose a multiobjective hyperheuristic algorithm to improve the robustness of MOEAs. The performance of the proposed algorithm is tested on benchmark test functions and an actual MOP. The results show that the proposed approach can select a suitable MOEA to solve a particular type of MOPs during the evolutionary process and provide a solution set for decision-makers to control the translation of an immersed tunnel element under different objectives/operator environments.

This article is concerned with set-membership global estimation for a networked system under unknown-but-bounded process and measurement noises. First, a group of local set-membership estimators is deployed to obtain the local ellipsoidal estimate of the true system state. Each estimator is capable of communicating with its neighbors within its communication range. Second, a global estimation approach is proposed which generates a trace-maximal ellipsoid within the intersection of all the local estimation sets with an aim to improve the difference of the local estimate at each time instant. Sufficient conditions for providing a global estimate under both complete and incomplete measurement transmissions are derived. Third, as an application, a modified distributed photovoltaic grid-connected generation system is provided to verify the effectiveness of the developed set-membership global estimation approach. Furthermore, an islanding fault detection scheme is derived based on the calculated global ellipsoidal estimate. Finally, simulation verification of the obtained theoretical results on the distributed generation system is presented.

The complexity of an optimization problem is determined by its decision and objective spaces. Over the past few decades, a large number of works have focused on the performance improvement of metaheuristic algorithms via the objective space, whereas studies related to the decision space have attracted little attentions. Moreover, metaheuristic algorithms may not obtain satisfactory results within an entire feasible region, even if sufficient computational resources are available. Therefore, reducing the search space (i.e., finding a trust region) may be an effective method to ensure that the convergence is sufficiently close to the global optimal region. However, inappropriate subspace size may also weaken the performance of algorithms except for ones with a sufficiently small search space. To alleviate aforementioned problems, a variable search space (VSS) strategy based on a sequential trust region determination approach is proposed in this paper. In the VSS, the entire optimization process is divided into two stages: the first stage is to use an optimization approach for sequentially finding the trust domain of each variable and then determine the best-matched subspace; the second stage is to employ the optimization method for searching an optimal/near-optimal solution within the found trust region. The effectiveness of the VSS is evaluated using two widely used test suites, that is, IEEE CEC2014 and BBOB2012. Experimental results indicate that improving the algorithm performance is an important method for tackling problems, but locating a trust region is also beneficial for metaheuristic algorithms to improve the solution precision, especially for complex optimization problems.

This paper is concerned with the distributed H∞-consensus filtering problem on attitude tracking over a radar filter network subject to switching topology and random packet dropouts occurring in the data transmission from both the Sun sensor and the filters. Since ground-based radars cannot directly measure the satellite attitude, a Sun sensor is deployed at the satellite side and its measurements are transmitted to radar filters through different network communication channels while suffering from random packet dropouts with different probabilities. In the radar filter network, each radar filter receives data not only from the Sun sensor but also from its local neighboring radar filters in accordance with a switching network topology. A delicate distributed H∞-consensus filtering algorithm, which incorporates the effects of switching network topology and random packet dropouts, is adopted to estimate attitude and attitude-rate. The algorithm guarantees H∞-consensus attenuation performance for the estimation deviations among radar filters, and the robustness against the switching network topology and packet dropouts for the radar filter network. The illustrative examples are given to verify the effectiveness of the proposed distributed H∞-consensus filtering algorithm.

In this paper, a quantized H∞control problem for networked control systems (NCSs) subject to randomly multi-step transmission delays is investigated. A quantizer is used before the measurement signal enters the communication network and the randomly multi-step transmission delays which are described by a mathematical model are considered during the transmission through the network. Sufficient conditions are derived for the considered system to satisfy the H∞norm constraint subject to the randomly multi-step transmission delays. Simulation results demonstrate the effectiveness of the proposed method.

In this paper, a linear unbiased minimum-variance filtering problem is considered for a class of systems with randomly multi-step sensor delays. A new mathematical model is established for the multi-step sensor delays. Different from the augmented method for dealing with delayed systems, a linear unbiased minimum-variance filter design method is proposed without augmenting the state vector, which effectively reduces the filter dimensions. A recursive algorithm for calculating the filter gain matrix is developed. The simulation results illustrate the effectiveness of the proposed method.

In this paper, a novel brushless machine with 12-stator-slot/8-rotor-pole having magnets surface-mounted in the stator instead of rotor is proposed, which can be regarded as an improved flux-reversal permanent magnet (FRPM) machine and is especially suitable for brushless DC operation. The topology, operation principle and static characteristics of the proposed three-phase 12/8 stator surface-mounted permanent magnet (SSPM) prototyped machine are analyzed based on 2-D finite element analysis. A comparison between the FRPM and SSPM machines having the same dimensions is presented including the field distributions, back-EMF, cogging torque and electromagnetic torque. In addition, an optimization on the key dimensions of the SSPM machine is explored to improve the characteristics of PM flux-linkage, back-EMF as well as to reduce cogging torque. The predicted results confirm that the SSPM machines with special stator slots and rotor poles combinations are attractive candidates in brushless DC drives.