A novel PSO algorithm, the Migrant Particle Swarm Optimization (Migrant PSO), is presented to solve the trajectory optimization problem in the presence of constraints such as dynamic pressure and overload. Imitating t...
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A novel PSO algorithm, the Migrant Particle Swarm Optimization (Migrant PSO), is presented to solve the trajectory optimization problem in the presence of constraints such as dynamic pressure and overload. Imitating the behaviour of a flock of migrant birds, the Migrant PSO algorithm employs stochastic search method and adaptive linear search method respectively for PSO search spaces including both continuous space and discrete space. In the example of the minimum peak heat rate reentry trajectory optimization for X-33 vehicle model with free terminal time, some key problems such as parameterized method are argued in detail. The simulation results indicate that the Migrant PSO algorithm proves to be able to generate a complete and optimal 3DOF reentry trajectory rapidly.
Landing footprint of an entry vehicle provides critical information for mission planning. Conventional methods calculate it through solving a family of multi-constraints optimal control problems. It is difficult to so...
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Landing footprint of an entry vehicle provides critical information for mission planning. Conventional methods calculate it through solving a family of multi-constraints optimal control problems. It is difficult to solve. To generate the boundary of it, we proposed a new scheme based on differential evolution(DE). Utilizing DE's parallelism, each run generates one side instead of one point of the boundary. To get the full boundary, just need run the algorithm two times. The algorithm's merits include accurate, fast, not relying on simplification of the system, all control variables are under consideration and facilitating parallel programming.
The electroencephalogram (EEG) is the most popular form of input for brain computer interfaces (BCIs). However, it can be easily contaminated by various artifacts and noise, e.g., eye blink, muscle activities, powerli...
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It is difficult to guide the entry vehicle to prescribed area due to the disperse of environment and kinematics. Through predicted residual range at the current state based on drag acceleration, we developed a predict...
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It is difficult to guide the entry vehicle to prescribed area due to the disperse of environment and kinematics. Through predicted residual range at the current state based on drag acceleration, we developed a predictor-corrector guidance law which doesn't need iteration to figure out the reference trajectory. Then using extended state observer based controller to track the reference trajectory. We use several missions(under various disperse conditions) to test the guidance law. Simulation results demonstrated that the guidance law is able to achieve the prescribed terminal conditions under various perturbations in the aerodynamic coefficients, the density of the atmosphere and the mass of the vehicle.
The K Nearest Neighbor(KNN) classifier has been widely used in the applications of data mining and machine learning, because of its simple implementation and distinguished performance. However, because the distance be...
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The K Nearest Neighbor(KNN) classifier has been widely used in the applications of data mining and machine learning, because of its simple implementation and distinguished performance. However, because the distance between all training samples and test samples have to be calculated, when there are too many samples or samples have huge features dimensionality, the time complexity and space complexity are high. The paper proposes a KNN algorithm with the minimum intra-class distance and the maximum extra-class distance(MIME-KNN). By finding a transformation matrix, the algorithm minimizes the intra-class distance and maximizes the distance between classes, which can improve the classification performance of traditional KNN algorithm. At the same time, the algorithm will also reduce the dimensionality of the samples to achieve the purpose of reducing time and space complexity. Experimental results show that the MIME-KNN work well in practical.
This paper is concerned with the consensus problem for a class of Lipschitz nonlinear agents with Markov switching topologies and time-varying delays. The distributed event-triggered consensus control with an adaptive...
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ISBN:
(纸本)9781467351942
This paper is concerned with the consensus problem for a class of Lipschitz nonlinear agents with Markov switching topologies and time-varying delays. The distributed event-triggered consensus control with an adaptive law in adjusting the coupling weights between neighboring agents is designed, which can not only guarantee the consensus performance in the mean square sense but also reduce the communication burden since the introduction of the event-triggered communication scheme. Different from the traditional event-triggers in the existing references, the parameter of the event-trigger in this paper is adaptively adjusted by using an adaptive law. A convincing simulation example is given to illustrate the theoretical results.
This paper proposes a rapid trajectory optimization approach using a novel PSO algorithm, the Migrant particle swarm optimization (Migrant PSO). Imitating the behavior of a flock of migrant birds, the Migrant PSO algo...
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ISBN:
(纸本)9781424447374;9781424447541
This paper proposes a rapid trajectory optimization approach using a novel PSO algorithm, the Migrant particle swarm optimization (Migrant PSO). Imitating the behavior of a flock of migrant birds, the Migrant PSO algorithm employs stochastic search method and adaptive linear search method respectively for PSO search spaces including both continuous space and discrete space. In the example of the minimum control energy reentry trajectory optimization for X-33 vehicle model with free terminal time, some key problems such as parameterized method are argued in detail. The simulation results indicate that the Migrant PSO algorithm proves to be able to generate a complete and optimal 3DOF reentry trajectory rapidly.
This paper presents an active disturbance rejection guidance method using quadratic transition for the atmospheric ascent guidance problem. The quadratic transition is designed from the current flight states with a re...
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This paper presents an active disturbance rejection guidance method using quadratic transition for the atmospheric ascent guidance problem. The quadratic transition is designed from the current flight states with a reference trajectory planned off-board. For any states error, the solution of the designed quadratic transition converges to the reference trajectory. The guidance command is obtained from the derivative of the designed quadratic transition. For the unknown modeling error, the guidance command is compensated using the estimated disturbances from the extended state observer. Computer simulation for GHV (Generic Hypersonic Air Vehicle) model, which includes initial states error and modeling error, shows great accuracy and effectiveness of this guidance method.
This paper aims at reducing the calibration effort of EEG-based brain-computer interfaces (BCIs). More specifically, in the context of cross-subject classification, we correct covariate shift of EEG data from differen...
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ISBN:
(纸本)9781728145709
This paper aims at reducing the calibration effort of EEG-based brain-computer interfaces (BCIs). More specifically, in the context of cross-subject classification, we correct covariate shift of EEG data from different subjects, so that a classifier trained on auxiliary subjects can also be applied to a new subject, without any labeled trials from the new subject. Methods: We propose two approaches to enhance the performance of a state-of-the-art Riemannian space transfer learning (TL) algorithm: 1) trials selection, which resamples trials from the auxiliary subjects so that they become more consistent with those of the new subject;and, 2) channel selection, which reduces the number of channels and hence makes the Riemannian space computations more accurate and efficient. Results: We tested the proposed approaches on two motor imagery datasets. The results verified that they can enhance the performance of the state-of-the-art TL algorithm. Conclusion and significance: Our proposed approaches make the state-of-the-art TL algorithm more effective and efficient.
As one of the key degradation mechanisms of Solid oxide electrolysis cell(SOEC),oxygen electrode delamination directly affects the durability of *** order to improve the service life of SOEC stack,optimizing the syste...
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As one of the key degradation mechanisms of Solid oxide electrolysis cell(SOEC),oxygen electrode delamination directly affects the durability of *** order to improve the service life of SOEC stack,optimizing the system regulation range is an effective *** this study,according to the delamination mechanism of SOEC oxygen electrode,based on the existing thermal and electrical characteristics of the SOEC stack,a new gas characteristic of oxygen electrode/electrolyte interface oxygen pressure is added,and a cross-flow SOEC 2-D stack model with the oxygen electrode/electrolyte interface oxygen pressure was ***,the influence of the oxygen electrode/electrolyte interface oxygen pressure characteristics on the safe operating range of the stack is *** simulation results show that the safe operating range of the system was optimized by adding it.
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