Driven by practical applications, the achievement of distributed observers for nonlinear systems has emerged as a crucial advancement in recent years. However, existing theoretical advancements face certain limitation...
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Driven by practical applications, the achievement of distributed observers for nonlinear systems has emerged as a crucial advancement in recent years. However, existing theoretical advancements face certain limitations: They either fail to address more complex nonlinear phenomena, rely on hard-to-verify assumptions, or encounter difficulties in solving system ***, this paper aims to address these challenges by investigating distributed observers for nonlinear systems through the full-measured canonical form(FMCF), which is inspired by full-measured system(FMS) theory. To begin with, this study addresses the fact that the FMCF can only be obtained through the observable canonical form(OCF) in existing FMS *** paper demonstrates that a class of nonlinear systems can directly obtain FMCF through state space equations, independent of OCF. Also, a general method for solving FMCF in such systems is provided. Furthermore, based on the FMCF, A distributed observer is developed for nonlinear systems under two scenarios: Lipschitz conditions and open-loop bounded *** paper establishes their asymptotic omniscience and demonstrates that the designed distributed observer in this study has fewer design parameters and is more convenient to construct than existing approaches. Finally, the effectiveness of the proposed methods is validated through simulation results on Van der Pol oscillators and microgrid systems.
In the field, unsupervised learning-based fault diagnosis is necessary due to the lack of fault data. However, conventional auto-encoder models face challenges in fault classification. To address this issue, this stud...
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A hysteresis motor is a motor characterized by an increase in the efficiency of the output to input ratio during an over-excitation in which a higher voltage is applied at startup than when the rated voltage is applie...
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The hydrogen energy storage system(HESS)integrated with renewable energy power generation exhibits low reliability and flexibility under source-load *** address the above issues,a two-stage optimal scheduling model co...
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The hydrogen energy storage system(HESS)integrated with renewable energy power generation exhibits low reliability and flexibility under source-load *** address the above issues,a two-stage optimal scheduling model considering the operation sequences of HESSs is proposed for commercial community integrated energy systems(CIESs)with power to hydrogen and heat(P2HH)*** aims to optimize the energy flow of HESS and improve the flexibility of hydrogen production and the reliability of energy supply for ***,the refined operation model of HESS is established,and its operation model is linearized according to the operation domain of HESS,which simplifies the difficulty in solving the optimization problem under the premise of maintaining high approximate ***,considering the flexible start-stop of alkaline electrolyzer(AEL)and the avoidance of multiple energy conversions,the operation sequences of HESS are ***,a two-stage optimal scheduling model combining day-ahead economic optimization and intra-day rolling optimization is established,and the model is simulated and verified using the source-load prediction data of typical days in each *** simulation results show that the two-stage optimal scheduling reduces the total load offset by about 14%while maintaining similar operating cost to the day-ahead economic optimal ***,by formulating the operation sequences of HESS,the operating cost of CIES is reduced by up to about 4.4%.
As battery deployments in electric vehicles and energy storage systems grow, ensuring homogeneous performance across units is crucial. We propose a multi-derivative imaging fusion(MDIF) model, employing advanced imagi...
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As battery deployments in electric vehicles and energy storage systems grow, ensuring homogeneous performance across units is crucial. We propose a multi-derivative imaging fusion(MDIF) model, employing advanced imaging and machine learning to predict battery aging trajectories from minimal initial data, thus facilitating effective performance grouping before deployment. Utilizing a derivative strategy and Gramian Angular Difference Field for dimensional enhancement, the MDIF model uncovers subtle predictive features from discharge curve data after only ten cycles. The architecture includes a parallel convolutional neural network with lateral connections to enhance feature integration and *** on a self-developed dataset, the model achieves an average root-mean-square error of 0.047 Ah and an average mean absolute percentage error of 1.60%, demonstrating high precision and *** robustness is further validated through transfer learning on two publicly available datasets, adapting with minimal retraining. This approach significantly reduces the testing cycles required, lowering both time and costs associated with battery testing. By enabling precise battery behavior predictions with limited data, the MDIF model optimizes battery utilization and deployment strategies, enhancing system efficiency and sustainability.
This paper proposes an event-triggered stochastic model predictive control for discrete-time linear time-invariant(LTI) systems under additive stochastic disturbances. It first constructs a probabilistic invariant set...
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This paper proposes an event-triggered stochastic model predictive control for discrete-time linear time-invariant(LTI) systems under additive stochastic disturbances. It first constructs a probabilistic invariant set and a probabilistic reachable set based on the priori knowledge of system *** with enhanced robust tubes, the chance constraints are then formulated into a deterministic form. To alleviate the online computational burden, a novel event-triggered stochastic model predictive control is developed, where the triggering condition is designed based on the past and future optimal trajectory tracking errors in order to achieve a good trade-off between system resource utilization and control performance. Two triggering parameters σ and γ are used to adjust the frequency of solving the optimization problem. The probabilistic feasibility and stability of the system under the event-triggered mechanism are also examined. Finally, numerical studies on the control of a heating, ventilation, and air conditioning(HVAC) system confirm the efficacy of the proposed control.
In recent years, the need for surface life-saving is gradually increasing as a response to accidents such as ship collisions in the marine environment. To solve this problem, there were approaches that launched life-s...
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The railway point machine plays an important part in railway systems. It is closely related to the safe operation of trains. Considering the advantages of vibration signals on anti-interference, this paper develops a ...
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The railway point machine plays an important part in railway systems. It is closely related to the safe operation of trains. Considering the advantages of vibration signals on anti-interference, this paper develops a novel vibration signal-based diagnosis approach for railway point machines. First, variational mode decomposition(VMD)is adopted for data preprocessing, which is verified more effective than empirical mode decomposition. Next, multiscale permutation entropy is extracted to characterize the fault features from multiple scales. Then ReliefF is utilized for feature selection, which can greatly decrease the feature dimension and improve the diagnosis accuracy. By experiment comparisons, the proposed approach performs best on diagnosis for railway point machines. The diagnosis accuracies on reverse-normal and normal-reverse processes are respectively 100% and 98.29%.
Single-electron transistor（SET） is considered as one of the promising candidates for future electronic devices due to its advantages of low power consumption and high integration. The comparative analysis of SET-bas...
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Single-electron transistor（SET） is considered as one of the promising candidates for future electronic devices due to its advantages of low power consumption and high integration. The comparative analysis of SET-based inverters, especially the noise margin, is carried out. Pure SET-SET and hybrid SET with p type metal oxide semiconductor（SET-PMOS） inverters are designed for investigation. The effects of SET supply voltage, junction resistance and junction capacitance on noise tolerance and power consumption of inverters are studied. For hybrid SET-PMOS inverters, the noise margin for a logic high（NMH） is less than 60 mV under various conditions, which may become the bottleneck of its application. For pure SET-SET inverters, both NMH and the noise margin for a logic low（NML） could reach 300 mV at a supply voltage of 0.8 V. The minimum power consumption of pure SET-SET and hybrid SET-PMOS inverters is 2.85 nW and 58 nW, respectively. The pure SET-SET inverters have greater noise tolerance and lower power consumption, which is more conducive to large-scale integration. When junction capacitance CJ= 0.0273 aF and junction resistance RT≥1 MΩ in SET-SET inverters at a supply voltage of 0.8 V, the NMH and NML are not significantly affected by the junction resistance and the noise margin fluctuates at 300 mV.
Dear Editor,This letter presents a class of saturated sliding mode control (SMC)strategy for linear systems subject to impulsive disturbance and input saturation. To ensure the feasibility of proposed SMC under satura...
Dear Editor,This letter presents a class of saturated sliding mode control (SMC)strategy for linear systems subject to impulsive disturbance and input saturation. To ensure the feasibility of proposed SMC under saturation, a relationship is established among attraction domain, saturation structure and control gain.
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