This paper recalls a novel data-driven model-free adaptive control (MFAC) method for a class of interconnected discrete-time nonlinear systems, whose model is unavailable and interactions between each subsystem are me...
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ISBN:
(纸本)9781479978632
This paper recalls a novel data-driven model-free adaptive control (MFAC) method for a class of interconnected discrete-time nonlinear systems, whose model is unavailable and interactions between each subsystem are measurable. Then, under some mild conditions, stability of the closed-loop system is analyzed theoretically. Compared with original MFAC, the proposed MFAC for interconnected systems belongs to decentralized control method, and makes full use of the interacted data to achieve better performance. The effectiveness and superiority are verified by simulation result.
Brain-computer interface (BCI) plays an important role in helping the people with severe motor disability. In event-related potential (ERP) based BCIs, subjects were asked to count the target stimulus in the offline e...
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Brain-computer interface (BCI) plays an important role in helping the people with severe motor disability. In event-related potential (ERP) based BCIs, subjects were asked to count the target stimulus in the offline experiment, the recorded electroencephalogram (EEG) data was used to train the classification mode. However, subjects may make mistakes in counting the target stimulus or be affected by the non-target stimulus. The target trials may not contain expected ERPs and the non-target trials may contain unexpected ERPs, which was called error samples. This paper intends to survey whether the classification accuracy could be improved after removing these error samples from offline training data. The result showed that the online performance of BCI system could be improved after selecting the offline samples for training the classification mode.
This study investigates an event-triggered model predictive control for wireless networked control system with packet losses in the sensor-to-controller channel. Based on a predictive control compensation strategy, th...
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ISBN:
(纸本)9781479987313
This study investigates an event-triggered model predictive control for wireless networked control system with packet losses in the sensor-to-controller channel. Based on a predictive control compensation strategy, the closed-loop model with packet losses is established. The event-triggered conditions are derived by choosing the performance objective function of MPC as a Lyapunov function. Further, the maximal allowable number of successive packet losses is presented. Under the proposed mechanism, the energy consumption of the wireless network is alleviated and closed-loop stability is guaranteed. Finally, simulation results are shown to illustrate the effectiveness of the proposed method.
This paper presents a brain-computer interface (BCI) in which the face paradigm was optimized for the visual mismatch negativity (MMN). There were 12 cells in a LCD monitor. A single letter was at the bottom of each c...
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ISBN:
(纸本)9781467368513
This paper presents a brain-computer interface (BCI) in which the face paradigm was optimized for the visual mismatch negativity (MMN). There were 12 cells in a LCD monitor. A single letter was at the bottom of each cell. In the new paradigm, a color face appeared above each of the 12 cells randomly while the gray faces appeared in others 11 cells. A traditional face paradigm with single character pattern was compared. Three healthy subjects participated in the experiment. Results showed that the new paradigm elicited larger N200 and N400 components than traditional face paradigm and had better performance in online session. The results demonstrated the advantages of the new paradigm in our P300 speller system.
With new dynamics and uncertainties in today's power grids, traditional fixed-interval State Estimation (SE) may be unable to track the variability and monitor the power grid effectively. This paper presents a new...
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ISBN:
(纸本)9781479958306
With new dynamics and uncertainties in today's power grids, traditional fixed-interval State Estimation (SE) may be unable to track the variability and monitor the power grid effectively. This paper presents a new architecture to transfer data and execute SE on demand. A list of situations are summarized to direct the SE-demand generator in system control center. As SCADA and PMU measurements are co-exist in realistic power systems, time skew problem is inevitable. To mitigate the influence of time skew, a state estimator based on time skew oriented weight adaptation is considered. In each SE circle, the weights assigned to the measurements not only correspond to their noise, but also the time offsets relative to the SE-demand point. Numerical examples demonstrate the improved accuracy of our estimator compared with the conventional hybrid SE when measurements time skew is present.
Catalytic naphtha reforming is one of the most important processes for high octane gasoline manufacture and aromatic hydrocarbons production. In this article, a modified differential evolution (DE) algorithm is propos...
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Catalytic naphtha reforming is one of the most important processes for high octane gasoline manufacture and aromatic hydrocarbons production. In this article, a modified differential evolution (DE) algorithm is proposed to optimize an actual continuous catalytic naphtha reforming (CCR) process. The optimization problem considers to minimize the energy consumption and maximize the aromatics yield. The CCR process model is established by adopting the 27-lumped kinetics reaction network, and all parameters are adjusted based on the actual process data. The DE algorithm is modified to maintain the diversity of the population. In this mechanism, individuals further from the best individual have larger possibilities to be selected in the mutation operator. The modified DE is evaluated by solving 6 benchmark functions, and the performance is compared with classic DEs. The results demonstrate that the modified DE has better global search ability and higher computation efficiency. Furthermore, the optimization results of catalytic naphtha reforming process indicate that the proposed algorithm has the ability of locating the optimal operating points, in which the aromatics yield is improved, while energy consumption is reduced. Meanwhile, the optimal operating points and results are discussed at the end of the article.
control performance monitoring has attracted great attention in both academia and industry over the past two decades. However, most research efforts have been devoted to the performance monitoring of linear control sy...
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control performance monitoring has attracted great attention in both academia and industry over the past two decades. However, most research efforts have been devoted to the performance monitoring of linear control systems, without considering the pervasive nonlinearities(e.g. valve stiction) present in most industrial control systems. In this work, a novel probability distribution distance based index is proposed to monitor the performance of non-linear control systems. The proposed method uses Hellinger distance to evaluate change of control system performance. Several simulation examples are given to illustrate the effectiveness of the proposed method.
This paper presents that the majorization theory plays an essential role in a class of sensor scheduling problems, whose solutions all have periodic or uniformly distributed patterns. This paper revisits the problem o...
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ISBN:
(纸本)9781479978878
This paper presents that the majorization theory plays an essential role in a class of sensor scheduling problems, whose solutions all have periodic or uniformly distributed patterns. This paper revisits the problem of communication time scheduling for a single sensor with local computation capability, and strengthens its original result by the majorization theory. The scheduling for a single normal sensor in a general-order system is also studied, and the optimal schedules for minimizing the upper bound of the objective function is provided. Examples are provided at the end.
This paper investigates the uniformly ultimate boundedness (UUB) of an identifier-based adaptive dynamic programming (ADP) algorithm proposed in [7]. It is demonstrated that the estimation errors of weights in both cr...
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This paper investigates the uniformly ultimate boundedness (UUB) of an identifier-based adaptive dynamic programming (ADP) algorithm proposed in [7]. It is demonstrated that the estimation errors of weights in both critic and action networks are UUB during iteration learning. Moreover, a selection method on learning rates is also given.
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