Until now, the canonical correlation analysis(CCA)-based method has been most widely applied to steady-state visual evoked potential(SSVEP). Artificial sine-cosine signals are used as the original references in the CC...
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Until now, the canonical correlation analysis(CCA)-based method has been most widely applied to steady-state visual evoked potential(SSVEP). Artificial sine-cosine signals are used as the original references in the CCA method, which could hardly reflect the real SSVEP features buried in electroencephalogram(EEG). In this study, we use principal component analysis(PCA) to extract EEG features multivariate linear regression(MLR) is implemented on EEG and the specific sample labels. Experimental results show that the proposed MLR method outperformed other two competing methods for SSVEP recognition, especially in short time window.
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 exp...
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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.
Formation control and obstacle avoidance for multi-agent systems have attracted more and more attention. In this paper, the problems of formation control and obstacle avoidance are investigated by means of a consensus...
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Formation control and obstacle avoidance for multi-agent systems have attracted more and more attention. In this paper, the problems of formation control and obstacle avoidance are investigated by means of a consensus algorithm. A novel distributed control model is proposed for the multi-agent system to form the anticipated formation as well as achieve obstacle avoidance. Based on the consensus algorithm, a distributed control function consisting of three terms (formation control term, velocity matching term, and obstacle avoidance term) is presented. By establishing a novel formation control matrix, a formation control term is constructed such that the agents can converge to consensus and reach the anticipated formation. A new obstacle avoidance function is developed by using the modified potential field approach to make sure that obstacle avoidance can be achieved whether the obstacle is in a dynamic state or a stationary state. A velocity matching term is also put forward to guarantee that the velocities of all agents converge to the same value. Furthermore, stability of the control model is proven. Simulation results are provided to demonstrate the effectiveness of the proposed control.
This paper presents a data-driven model free adaptive control method based on full form dynamic linearization *** this framework,the controller structure is designed by the full form dynamic linearization technique on...
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ISBN:
(纸本)9781479970186
This paper presents a data-driven model free adaptive control method based on full form dynamic linearization *** this framework,the controller structure is designed by the full form dynamic linearization technique on the ideal controller,and its parameters are online optimized using input and output data of the plant through the simple projection algorithm,where the accurate plant model is not *** effectiveness of the proposed method is verified by numerical simulations.
In the literatures, to design state feedback controller to partially stabilize Boolean control network is rarely *** by this, this paper studies partial stabilization for Boolean network with state feedback control. A...
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ISBN:
(纸本)9781467374439
In the literatures, to design state feedback controller to partially stabilize Boolean control network is rarely *** by this, this paper studies partial stabilization for Boolean network with state feedback control. A sufficient condition for the existence of a state feedback controller to achieve the partial stabilization is established first. Then, the feedback control law is proposed. Finally, examples are presented to show the proposed design procedure.
This paper presents robust optimization models for a multi-product integrated problem of planning and scheduling(based on the work of Terrazas-Moreno & Grossmann(2011) [1]) under products prices uncertainty. With ...
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This paper presents robust optimization models for a multi-product integrated problem of planning and scheduling(based on the work of Terrazas-Moreno & Grossmann(2011) [1]) under products prices uncertainty. With the objective of maximizing the total profit in planning time horizon, the planning section determines the amount of each product, each product distributed to each market, and the inventory level in each manufacturing site during each scheduling time period;the scheduling section determines the products sequence, start and end time of each product running in each production site during each scheduling time period. The uncertainty sets used in robust optimization model are box set, ellipsoidal set, polyhedral set, combined box and ellipsoidal set, combined box and polyhedral set, combined box, ellipsoidal and polyhedral *** genetic algorithm is utilized to solve the robust optimization models. Case studies show that the solutions obtained from robust optimization models are better than the solutions obtained from the original integrated planning and scheduling when the prices are changed.
In this paper we consider the problem of sensor scheduling for distributed estimation with power constraint. To satisfy the requirement of energy consumption, we assume that each sensor sends data to its adjacent sens...
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ISBN:
(纸本)9781479978878
In this paper we consider the problem of sensor scheduling for distributed estimation with power constraint. To satisfy the requirement of energy consumption, we assume that each sensor sends data to its adjacent sensors with a given probability, where the state of the system is estimated by means of distributed estimators. First, we design optimal estimator gain to minimize the estimation error covariance for each sensor, and then we find an upper bound of the expected state error covariance, provide a sufficient condition to guarantee the stability of the proposed estimator. To find the optimal communication probability, we further formulate the sensor scheduling problem as an optimization problem, which is relaxed to a convex optimization in the form of LMIs.
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.
This paper is concerned with the H∞ fault detection (FD) for a class of networked systems with random packet losses, sensor saturation and multiplicative noises. The network with both output measurement and control p...
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ISBN:
(纸本)9781467391054
This paper is concerned with the H∞ fault detection (FD) for a class of networked systems with random packet losses, sensor saturation and multiplicative noises. The network with both output measurement and control packet losses is modeled with two independent Bernouli distributed white sequences. H∞ filtering theory is adapted to formulate the problem of fault detection for networked systems. The output measurement is affected by sensor saturation which is described by sector-nonlinearities, and the multiplicative noises are modeled as a form of Gaussian white noise. The purpose of the addressed problem is to design a fault detection filter such that, the fault detection dynamic system is exponentially stable in the mean square, and the error between the residual value and fault value is made as small as possible. A sufficient condition for FD filter is derived by solving the linear matrix inequality (LMI). Finally, a numerical example is illustrated to show the effectiveness of the designed method.
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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