Model predictive control has become a widely accepted strategy in industrial applications in the recent years. Often mentioned reasons for the success are the optimization based on a system model, consideration of con...
Model predictive control has become a widely accepted strategy in industrial applications in the recent years. Often mentioned reasons for the success are the optimization based on a system model, consideration of constraints and an intuitive tuning process. However, as soon as unknown disturbances or model plant mismatch have to be taken into account the tuning effort to achieve offset-free tracking increases. In this work a novel approach for offset-free MPC is presented, which divides the tuning in two steps, the setup of a nominal MPC loop and an external reference adaptation. The inner nominal loop addresses the performance targets in the nominal case, decouples the system and essentially leads to a first order response. The second outer loop enables offset-free tracking in case of unknown disturbances and consists of feedback controllers adapting the reference. Due to the mentioned properties these controllers can be tuned separate and by known guidelines. To address conditions with active input constraints, additionally a conditional reference adaptation scheme is introduced. The tuning strategy is evaluated on a simulated linear Wood-Berry binary distillation column example.
In this paper, we explained the current matrix completion theory and proposed a new matrix completion framework called Feature Vector and Function Approximating Based Matrix Completion (FVFABMC) which extended low-ran...
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In this paper, we explained the current matrix completion theory and proposed a new matrix completion framework called Feature Vector and Function Approximating Based Matrix Completion (FVFABMC) which extended low-rank matrix completion theory. The new matrix completion problem can be decomposed into two learning problems, feature vector learning problem and synthetic function learning problem based on the feature vector matrix. The global optimal solution for feature vectors can be obtained by only assuming synthetic function is smoothing locally which makes first-order approximation of feature vector learning problem as convex semi-definite programming problem. To solve the large-scale feature vector learning problem, we also proposed a stochastic parallel gradient descent blocks algorithm. For the matrix synthetic function learning problem, according to local linear hypothesis, the problem can be formalized in to an unconstrained least squares problem based on local neighboring coefficients which avoid model selection and parameter learning difficulties. Numerical experiments show that the feasibility of FVFABMC method in learning feature vectors and had a good prediction performance on missing elements of utility matrix.
Hysteretic noisy chaotic neural network (HNCNN) has been proven to be a powerful tool in solving combinatorial optimization problems, which can increase the effective convergence toward optimal or near-optimal solutio...
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ISBN:
(纸本)9781479969906
Hysteretic noisy chaotic neural network (HNCNN) has been proven to be a powerful tool in solving combinatorial optimization problems, which can increase the effective convergence toward optimal or near-optimal solutions by using both stochastic chaotic simulated annealing (SCSA) and hysteretic dynamics. Considering the excellent optimization performance of HNCNN, we apply HNCNN to better resolve economic load dispatch (ELD) of power system in this paper. In addition, the system loss and valve point effect are also involved in the simulation. Simulation results and analyses compared with other approaches are presented to illustrate efficiency of the HNCNN.
In this paper, mathematical model of relationship between clutch engaging speed and engine output torque is firstly established to solve the problem of clutch engaging speed in AMT vehicle starting process, basing on ...
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All currently available Network-based Intrusion Detection Systems (NIDS) rely upon passive protocol analysis which is fundamentally flawed as an attack can evade detection by exploiting ambiguities in the traffic stre...
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After the TMI accident,many alarm reduction systems and diagnostic systems have been studied to reduce nuisance alarms and to detect the causes of an abnormal *** systems provide an operator with information on signif...
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After the TMI accident,many alarm reduction systems and diagnostic systems have been studied to reduce nuisance alarms and to detect the causes of an abnormal *** systems provide an operator with information on significant alarms or causes of an abnormal state for an operator to identify that *** this paper,an operator-aid system,Logic Alarm Cause Tracking System(LogACTs),is proposed for tracking the logics of an alarm,finding the causes of an alarm,displaying the highlighted alarm procedure related to the causes,and suppressing and filtering nuisance alarms due to the physical or logical connections between components or systems in an abnormal *** system can be used by an operator to identify the detailed causes of an alarm without checking all the causes of the candidates by *** proposed system will be applied to a Korean Standard Nuclear Power Plant of a PWR,ShinHanul 1&2 Nuclear Power Plant.
Clustering analysis is an important tool of data mining. The study on efficient clustering has great significance, especially in improving a clustering algorithm's adaptability and usefulness. Clustering ensemble ...
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Clustering analysis is an important tool of data mining. The study on efficient clustering has great significance, especially in improving a clustering algorithm's adaptability and usefulness. Clustering ensemble (CE) integrates several clustering algorithms such that the clustering results can be effectively improved. This work investigates similarity-based methods and proposes a new method called weight- incorporated similarity-based clustering ensemble (WSCE). Six classic data sets are used to test single clustering algorithms, similarity-based one, and the proposed one via simulation. The results prove the validity and performance advantage of the proposed method.
For most practical nonlinear state estimation problems, the conventional nonlinear filters do not usually work well for some cases, such as inaccurate system model, sudden change of state-interested and unknown varian...
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For most practical nonlinear state estimation problems, the conventional nonlinear filters do not usually work well for some cases, such as inaccurate system model, sudden change of state-interested and unknown variance of measurement noise. In this paper, an adaptive cubature strong tracking information filter using variational Bayesian (VB) method is proposed to cope with these complex cases. Firstly, the strong tracking filtering (STF) technology is used to integrally improve performance of cubature information filter (CIF) aiming at the first two cases and an iterative scheme is presented to effectively evaluate a strong tracking fading factor. Secondly, the VB method is introduced to iteratively evaluate the unknown variance of measurement noise. Finally, the novel adaptive cubature information filter is obtained by perfectly combining the STF technology with the VB method, where the variance estimation provided by the VB method guarantees normal running of the strong tracking functionality.
In this paper we deal with container assignment optimization on an intermodal network. We propose a linear programming model, following a frequency based approach, addressing both the maritime and the inland component...
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In this paper we deal with container assignment optimization on an intermodal network. We propose a linear programming model, following a frequency based approach, addressing both the maritime and the inland component, and taking into account custom times at ports and service frequencies. The proposed arc-based formulation, in which only variables related to arcs which actually exist are explicitly created, is particularly suitable for very large but sparse networks, typical in maritime long distance transport, because it allows strongly reducing the number of variables involved. Finally, we discuss computational results obtained on a real size instance.
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