In this paper, a new strategy based on impulsive control model of high speed roller is proposed. To make the roller hit the specified target, the strategy is summarized as an optimal control model calculating required...
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Differential Evolution (DE) is a simple and efficient numerical optimization method. Most DE variants in the literature adopt fixed population size. This paper incorporates into DE the mechanisms of lifetime and extin...
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The agents connected by networks are capable to reach a prescribed state if only a small fraction of them are controlled with feedback information. The present work tries to stabilize groups of non-linear agents in a ...
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The agents connected by networks are capable to reach a prescribed state if only a small fraction of them are controlled with feedback information. The present work tries to stabilize groups of non-linear agents in a directed network onto a stable state with several equilibriums. The agent dynamics can be identical or heterogeneous. But the agents in the same group share a same non-linear model. The conditions to reach multiple consensus states are provided under both non-switching and switching topologies. The maximum number of heterogeneous equilibriums is discussed. It is proved theoretically and by simulations that under certain conditions, pinning control can lead a multi-group network to reach a multi-valued consensus state.
Learning control has been an active topic of research for several decades, and is of theoretical, as well as practical, significance. Current theories and developments in learning control are discussed. Following ...
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Learning control has been an active topic of research for several decades, and is of theoretical, as well as practical, significance. Current theories and developments in learning control are discussed. Following a brief introduction of the state as well as new progress on learning control, we give a detail review on the models and algorithms of the control policies developed recently which proved to be advantageous over previous approaches through experimental results. The related results and properties are presented. Then, several potentially developmental topics that are valuable to be further investigated are suggested. Finally, the conclusion remark is proposed.
An analytic flying model that can well represent the physical behavior is derived, where the ball's self-rotational velocity changes along with the flying velocity. Based on the least square method, a rebound mode...
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An analytic flying model that can well represent the physical behavior is derived, where the ball's self-rotational velocity changes along with the flying velocity. Based on the least square method, a rebound model that represents the relation between the velocities before and after rebound is established. The initial trajectory is fitted to three second order polynomials of the flying time with the measured positions of the ball. The initial velocities of the ball in the analytic flying model, including the flying velocity and the self-rotational velocity, are computed from the polynomials. The ball's landing position and velocity is predicted with the model. The velocities after rebound are determined with the rebound model. By taking the velocities after rebound as new initial ones, the flying trajectory after rebound is described with the model again. In other words, the ball's trajectory is predicted. Experimental results verify the effectiveness of the proposed method.
A novel image deblurring method based on high-order non-local range Markov Random Field (NLR-MRF) prior is proposed in the paper. NLR-MRF provides an effective framework to model the statistical prior of natural image...
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A novel image deblurring method based on high-order non-local range Markov Random Field (NLR-MRF) prior is proposed in the paper. NLR-MRF provides an effective framework to model the statistical prior of natural images and leads to excellent performance in the application of image denoising and inpainting. Moreover, the framework will be extended to image deblurring in our work. Instead of commonly used maximum a-posteriori (MAP) estimation, which has several shortcomings, the high-order NLR-MRF prior is integrated into Bayesian minimum mean squared error (MMSE) estimation framework. Then, an efficient Gibbs sampling algorithm is adopted to compute MMSE estimation. The proposed method frees the user from determining regularization parameter beforehand, which relies on unknown noise level. We perform experiments on synthetic and real-world data to demonstrate the effectiveness of our method. Both quantitatively and qualitatively evaluations show superior or comparable results to the state-of-art deblurring methods.
The far-field intensity is detected from far-field image to estimate the piston distance between two gratings. The image processing algorithm includes projections along the horizontal and vertical directions, sear...
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The far-field intensity is detected from far-field image to estimate the piston distance between two gratings. The image processing algorithm includes projections along the horizontal and vertical directions, search for each focal spot's centre, feature extraction and intensity computation. Each focal spot's centre can be found with the projections. A self-growing method is used for feature extraction, where the threshold value depends on the gray value of each focal spot's centre. For each focal spot, the sum of gray values within the relevant domain is taken to be its energy intensity. Furthermore, the energy ratio of left and main focal spots (or main and right focal spots) is computed. A formula that expresses the piston distance between two gratings as the function of the energy ratio is fitted with several measured points. Based on this formula, the piston distance is obtained for a new energy-ratio. Finally, the proposed method is verified with a series of experiments.
An adaptive sliding mode control scheme for electromechanical actuator has been presented. The adaptive control strategy can estimate the uncertain parameters and adaptively compensate the modeled dynamical uncertaint...
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An adaptive sliding mode control scheme for electromechanical actuator has been presented. The adaptive control strategy can estimate the uncertain parameters and adaptively compensate the modeled dynamical uncertainties, while the sliding mode control method overcomes the unmodelled dynamics. In the adaptive law an equivalent output injection of the sliding mode observer which contains the parameter estimation error is used, and estimates of parameters can approximate the true values without prediction-error that is typically used in compositive adaptive law. Due to the improved estimation of uncertain parameters, the sliding mode law can robustifies the design against model uncertainties with a small swithcing gain. Stability of the system with the proposed approach has been proved and it has also been shown that the system states can reach the sliding mode in finite time. Finally, the effectiveness of the proposed control scheme has been exhibited via simulation examples.
The present work considers a scenario that a multi-actuator-sensor network neutralizes poisonous gas and tracks the pollution sources in a bounded area. A novel algorithm is proposed to minimize the system information...
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The present work considers a scenario that a multi-actuator-sensor network neutralizes poisonous gas and tracks the pollution sources in a bounded area. A novel algorithm is proposed to minimize the system information uncertainty while reaching balance on the workload of actuators. The method combines the centroidal Voronoi tessellations (CVT) with a consensus strategy. The CVT of the region insures a local optimal position configuration of the actuators, thus the sensing uncertainty can be minimized. The consensus algorithm utilizes the connection information among actuators, and helps them to reach a common workload. The consensus component will be terminated or suppressed when the workload is averaged. The consensus component may postpone the realization of CVT configuration. But it could be viewed as a perturbation that helps the actuators jump out of the local optimal CVT configuration. As a result, the information uncertainty may be further reduced. Comparison is drawn between the pure CVT algorithm and the method with consensus strategy. Simulations validated the proposed approach.
Based on the comparison of several common methods of electronic compass error compensation, this paper presents a new error compensation method based on Adaptive Differential Evolution-Fourier Neural Networks (ADE-FNN...
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