Previous work on process monitoring has shown that process nonlinearity can be overcome by neural networks(NN) model. But it's difficult to train a NN model that has good generalization capabilities in process mon...
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Previous work on process monitoring has shown that process nonlinearity can be overcome by neural networks(NN) model. But it's difficult to train a NN model that has good generalization capabilities in process monitoirng. In this paper, a NN model called Auto-Encoder(AE) is introdued to model the process behavior, then a robust training method is proposed based on the idea of making the learned model robust to the noisy training *** analysis shows that the proposed Robust Auto-Encoder(RAE) is equal to a regularized AE which can capture nonlinear variations in the training data. Based on this, a corresponding statistic is introduced for *** case studies are provided for performance evaluation of the proposed method.
This paper discusses a typical supply chain system based on Auto- Regressive Integrated Moving Average (ARIMA) demand process. Minimum Mean Square Error principle and stochastic optimal control theory are introduced t...
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Time synchronization protocols are widely used in industrialcontrolsystems. As the development of network technology, various types of attacks have occurred. Among them, delay attacks are less studied. In this paper...
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Green supply chain has become a promising and challenging field during the last decade driven by rising environmental conscious business and governmental legislation. In this paper, we develop a Petri-net based model ...
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The problem of generating optimal paths for curvature-constrained unmanned aerial vehicles (UAVs) performing surveillance of multiple ground targets is addressed in this paper. UAVs are modeled as Dubins vehicles so...
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The problem of generating optimal paths for curvature-constrained unmanned aerial vehicles (UAVs) performing surveillance of multiple ground targets is addressed in this paper. UAVs are modeled as Dubins vehicles so that the constraints of UAVs' minimal turning radius can be taken into account. In view of the effective surveillance range of the sensors equipped on UAVs, the problem is formulated as a Dubins traveling salesman problem with neighborhood (DTSPN). Considering its prohibitively high computational complexity, the Dubins paths in the sense of terminal heading relaxation are introduced to simplify the calculation of the Dubins distance, and a boundary-based encoding scheme is proposed to determine the visiting point of every target neighborhood. Then, an evolutionary algorithm is used to derive the optimal Dubins tour. To further enhance the quality of the solutions, a local search strategy based on approximate gradient is employed to improve the visiting points of target neighborhoods. Finally, by a minor modification to the individual encoding, the algorithm is easily extended to deal with other two more sophisticated DTSPN variants (multi-UAV scenario and multiple groups of targets scenario). The performance of the algorithm is demonstrated through comparative experiments with other two state-of-the-art DTSPN algorithms identified in literature. Numerical simulations exhibit that the algorithm proposed in this paper can find high-quality solutions to the DTSPN with lower computational cost and produce significantly improved performance over the other algorithms.
The extended state observer, a crucial part of the active disturbance rejection control, was first proposed in 1995 by Jingqing Han. The discrete-time form of the extended state observer has been extensively used in m...
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ISBN:
(纸本)9781467374439
The extended state observer, a crucial part of the active disturbance rejection control, was first proposed in 1995 by Jingqing Han. The discrete-time form of the extended state observer has been extensively used in many practical applications in engineering such as quad-rotor robot control and non-circular machining control;the convergence property of the discrete-time n-dimensional extended state observer, however, remains unexplored and is investigated in this work. The main idea is to construct the error equations of the discrete-time extended state observer for an n-dimensional single-input-single-output nonlinear system with uncertainty, and to prove the convergence of the extended state observer through induction.
Traditional optimization of production system and utility system is carried out separately which cannot ensure that the entire refinery system is optimized globally. In this paper, a novel integrated approach is propo...
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Ethylene cracking furnace system is the heart of ethylene production. It can process different raw materials in the pipe group of each furnace and can produce the main chemical products like ethylene, propylene and et...
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The studies on the controllability of complex networks popularly existing in natural, social and man-made engineered systems have been a critical and attractive subject for both academic and practical communities. To ...
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