In process industries, it is crucial to maintain operational parameters within a designated steady-state operating point to ensure product quality and operational efficiency. However,steady-state drift(a gradual shift...
In process industries, it is crucial to maintain operational parameters within a designated steady-state operating point to ensure product quality and operational efficiency. However,steady-state drift(a gradual shift in key parameters occurs over time even when the system is intended to be under stable conditions) can lead to significant production losses,safety risks, and increased operational costs. Therefore, accurately detecting steady-state drift is essential for maintaining stable, safe, and optimized operating conditions.
In order to solve the problem of broken colliders caused by the fact that the object models for interaction are not constructed in one piece, but are built in pieces in the Unity3D macro virtual scene, this paper prop...
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Data-driven soft sensing has become quite popular in recent years, which can provide real-time estimations of key variables in industrial processes. While the introduction of deep learning does improve the prediction ...
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This paper focuses on bridge inspections performed by intelligent unmanned aerial vehicles (UAV). For this, small data loggers are placed by the UAVs at the bridge, which have to be removed later. In our previous work...
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Recently, a reference derived some new higher-order output tracking properties for direct model reference adaptive control（MRAC） of linear time-invariant（LTI） systems: limt→∞ e（i）（t） = 0, i = 1,..., n*-1, wh...
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Recently, a reference derived some new higher-order output tracking properties for direct model reference adaptive control（MRAC） of linear time-invariant（LTI） systems: limt→∞ e（i）（t） = 0, i = 1,..., n*-1, where n*and e（i）（t） denote the relative degree of the system and the i-th derivative of the output tracking error, respectively. However, a naturally arising question involves whether indirect adaptive control（including indirect MRAC and indirect adaptive pole placement control） of LTI systems still has higher-order tracking properties. Such properties have not been reported in the literature. Therefore, this paper provides an affirmative answer to this question. Such higher-order tracking properties are new discoveries since they hold without any additional design conditions and, in particular, without the persistent excitation condition. Given the higher-order properties, a new adaptive control system is developed with stronger tracking features.（1） It can track a reference signal with any order derivatives being unknown.（2） It has higher-order exponential or practical output tracking properties.（3） Finally, it is different from the usual MRAC system, whose reference signal's derivatives up to the n*order are assumed to be known. Finally, two simulation examples are provided to verify the theoretical results obtained in this paper.
The development of a dynamic model for a popular implemented solar power plant is a critical task for power engineers aiming to enhance the plant’s performance and reliability. In this study, we utilized the predicti...
DC-DC converter-based multi-bus DC microgrids(MGs) in series have received much attention, where the conflict between voltage recovery and current balancing has been a hot topic. The lack of models that accurately por...
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DC-DC converter-based multi-bus DC microgrids(MGs) in series have received much attention, where the conflict between voltage recovery and current balancing has been a hot topic. The lack of models that accurately portray the electrical characteristics of actual MGs while is controller design-friendly has kept the issue active. To this end, this paper establishes a large-signal model containing the comprehensive dynamical behavior of the DC MGs based on the theory of high-order fully actuated systems, and proposes distributed optimal control based on this. The proposed secondary control method can achieve the two goals of voltage recovery and current sharing for multi-bus DC MGs. Additionally, the simple structure of the proposed approach is similar to one based on droop control, which allows this control technique to be easily implemented in a variety of modern microgrids with different configurations. In contrast to existing studies, the process of controller design in this paper is closely tied to the actual dynamics of the MGs. It is a prominent feature that enables engineers to customize the performance metrics of the system. In addition, the analysis of the stability of the closed-loop DC microgrid system, as well as the optimality and consensus of current sharing are given. Finally, a scaled-down solar and battery-based microgrid prototype with maximum power point tracking controller is developed in the laboratory to experimentally test the efficacy of the proposed control method.
Large language model in the field of text generation shows excellent language skills, combined with the retrieval of generated algorithm is enhanced, the language model is more accurate and timely in a professional fi...
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We present a simple and effective way to account for non-convex costs and constraints in state feedback synthesis, and an interpretation for the variables in which state feedback synthesis is typically convex. We achi...
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This paper proposes a data-driven learning-based approach to predictive control for switched nonlinear systems subject to state and control constraints and external stochastic disturbances. A switched Koopman modeling...
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This paper proposes a data-driven learning-based approach to predictive control for switched nonlinear systems subject to state and control constraints and external stochastic disturbances. A switched Koopman modeling framework is developed, where a multi-mode neural network for state lifting is trained simultaneously with Koopman operators and state reconstruction matrices for all *** framework facilitates the construction of the switched linear Koopman model in a transformed space and effectively captures the dynamics of the original nonlinear system. A switched predictive control strategy is then designed to regulate the switched Koopman model with constrained states and control inputs against both the stochastic disturbances and the uncertainties introduced by the lifting neural network. The proposed control scheme ensures mean-square stability and guarantees boundedness during the online phase. Furthermore, boundedness analysis is performed to determine the bounded set of the original system state across all admissible switching sequences. The effectiveness of the proposed methodology is demonstrated through a case study of a gene regulatory network.
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