The steady-state security region(SSR)offers ro-bust support for the security assessment and control of new power systems with high uncertainty and ***,accurately solving the steady-state security region boundary(SS-RB...
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The steady-state security region(SSR)offers ro-bust support for the security assessment and control of new power systems with high uncertainty and ***,accurately solving the steady-state security region boundary(SS-RB),which is high-dimensional,non-convex,and non-linear,presents a significant *** address this problem,this paper proposes a method for approximating the SSRB in power systems using the feature non-linear converter and improved oblique decision ***,to better characterize the SSRB,boundary samples are generated using the proposed sampling *** samples are distributed within a limited distance near the ***,to handle the high-dimensionality,non-convexity and non-linearity of the SSRB,boundary samples are converted from the original power injection space to a new fea-ture space using the designed feature non-linear ***-sequently,in this feature space,boundary samples are linearly separated using the proposed information gain rate based weighted oblique decision ***,the effectiveness and generality of the proposed sampling method are verified on the WECC 3-machine 9-bus system and IEEE 118-bus system.
This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance ru...
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This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system's control input and improve the robots' tracking ***, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots' run jitter. Then, Lyapunov's theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.
This study investigates the controllability of a general heterogeneous networked sampled-data system(HNSS) consisting of nonidentical node systems, where the inner coupling between any pair of nodes can be described b...
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This study investigates the controllability of a general heterogeneous networked sampled-data system(HNSS) consisting of nonidentical node systems, where the inner coupling between any pair of nodes can be described by a unique *** signals on control and transmission channels are sampled and held by zero-order holders, and the control sampling period of each node can be different. Necessary and sufficient controllability conditions are developed for the general HNSS, using the Smith normal form and matrix equations, respectively. The HNSS in specific topology or dynamic settings is discussed subsequently with easier-to-verify conditions derived. These heterogeneous factors have been determined to independently or jointly affect the controllability of networked sampled-data systems. Notably, heterogeneous sampling periods have the potential to enhance the overall controllability, but not for systems with some special dynamics. When the node dynamics are heterogeneous,the overall system can be controllable even if it is topologically uncontrollable. In addition, in several typical heterogeneous sampled-data multi-agent systems, pathological sampling of single-node systems will necessarily cause overall uncontrollability.
Industrial cyber-physical systems closely integrate physical processes with cyberspace, enabling real-time exchange of various information about system dynamics, sensor outputs, and control decisions. The connection b...
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Industrial cyber-physical systems closely integrate physical processes with cyberspace, enabling real-time exchange of various information about system dynamics, sensor outputs, and control decisions. The connection between cyberspace and physical processes results in the exposure of industrial production information to unprecedented security risks. It is imperative to develop suitable strategies to ensure cyber security while meeting basic performance *** the perspective of controlengineering, this review presents the most up-to-date results for privacy-preserving filtering,control, and optimization in industrial cyber-physical systems. Fashionable privacy-preserving strategies and mainstream evaluation metrics are first presented in a systematic manner for performance evaluation and engineering *** discussion discloses the impact of typical filtering algorithms on filtering performance, specifically for privacy-preserving Kalman filtering. Then, the latest development of industrial control is systematically investigated from consensus control of multi-agent systems, platoon control of autonomous vehicles as well as hierarchical control of power systems. The focus thereafter is on the latest privacy-preserving optimization algorithms in the framework of consensus and their applications in distributed economic dispatch issues and energy management of networked power systems. In the end, several topics for potential future research are highlighted.
In this paper, problem of secure message (signal and image) transmission is studied. The message is encrypted by masking it with a chaotic system state and then transmitted to receiver-side via a communication channel...
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This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking pe...
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This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.
The increasing prevalence of drones has raised significant concerns regarding their potential for misuse in activities such as smuggling, terrorism, and unauthorized access to restricted airspace. Consequently, the de...
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Numerous studies have been conducted on microfluidic mixers in various microanalysis systems, which elucidated the manipulation and control of small fluid volumes within microfluidic chips. These studies have demonstr...
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Numerous studies have been conducted on microfluidic mixers in various microanalysis systems, which elucidated the manipulation and control of small fluid volumes within microfluidic chips. These studies have demonstrated the ability to control fluids and samples precisely at the microscale. Microfluidic mixers provide high sensitivity for biochemical analysis due to their small volumes and high surface-to-volume ratios. A promising approach in drug delivery is the rapid microfluidic mixer-based extraction of elemental iodine at the micro level, demonstrating the versatility and the potential to enhance diagnostic imaging and accuracy in targeted drug delivery. Micro-mixing inside microfluidic chips plays a key role in biochemical analysis. The experimental study describes a microfluidic mixer for extraction of elemental iodine using carbon tetrachloride with a gas bubble mixing process. Gas bubbles are generated inside the microcavity to create turbulence and micro-vortices resulting in uniform mixing of samples. The bubble mixing of biochemical samples is analyzed at various pressure levels to validate the simulated results in computational fluid dynamics(CFD). The experimental setup includes a high-resolution camera and an air pump to observe the mixing process and volume at different pressure levels with time. The bubble formation is controlled by adjusting the inert gas flow inside the microfluidic chip. Microfluidic chip-based gas bubble mixing effects have been elaborated at various supplied pressures.
This paper proposes a coordinated frequency control scheme for emergency frequency regulation of isolated power systems with a high penetration of wind *** proposed frequency control strategy is based on the novel non...
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This paper proposes a coordinated frequency control scheme for emergency frequency regulation of isolated power systems with a high penetration of wind *** proposed frequency control strategy is based on the novel nonlinear regulator theory,which takes advantage of nonlinearity of doubly fed induction generators(DFIGs)and generators to regulate the frequency of the power *** deviations and power imbalances are used to design nonlinear feedback controllers that achieve the reserve power distribution between generators and DFIGs,in various wind speed *** effectiveness and dynamic performance of the proposed nonlinear coordinated frequency control method are validated through simulations in an actual isolated power grid.
Dear Editor,This letter presents a new transfer learning framework for the deep multi-agent reinforcement learning(DMARL) to reduce the convergence difficulty and training time when applying DMARL to a new scenario [1...
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Dear Editor,This letter presents a new transfer learning framework for the deep multi-agent reinforcement learning(DMARL) to reduce the convergence difficulty and training time when applying DMARL to a new scenario [1], [2].
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