This paper proposes an adaptive optimal control strategy of finite-time control for high-order uncertain strict-feedback nonlinear systems. Firstly, a reinforcement learning (rl) based an optimal control scheme is emp...
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This paper proposes an adaptive optimal control strategy of finite-time control for high-order uncertain strict-feedback nonlinear systems. Firstly, a reinforcement learning (rl) based an optimal control scheme is employed to design a optimal controller, to achieve global optimisation. Additionally, considering the unmeasurable states, we construct a fuzzy observer and utilise fuzzy logic systems to approximate the unknown functions. Meanwhile, the inclusion of command filtering and time-based control simplifies the controller design and enhances the system's response rapidity. Finally, the effectiveness and feasibility of the proposed approach are validated through a numerical simulation and a single link-robot system simulation.
In confocal scanning fluorescence microscopy, the effective modulation transfer function with Gaussian plane wave illumination covers very few high-frequency components, which prohibits further improvement of the spat...
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In confocal scanning fluorescence microscopy, the effective modulation transfer function with Gaussian plane wave illumination covers very few high-frequency components, which prohibits further improvement of the spatial resolution. In this study, we propose saturated confocal scanning fluorescence microscopy with linear polarization to achieve super-resolution imaging. In saturated confocal scanning fluorescence microscopy with linear polarization, the effective modulation transfer function in the Fourier domain is extended in comparison with that of Gaussian plane wave illumination. The digital algorithm is adapted to retrieve the super-resolved image from the modulated recordings. The simulation results demonstrated that saturated confocal scanning fluorescence microscopy with linear polarization could be used to increase the resolution in confocal scanning fluorescence microscopy.
Considering the fact that it is very difficult to fully model an autonomous underwater vehicle (AUV) in the complex water environment, this paper presents a model-free tracking control strategy for an AUV in the prese...
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Considering the fact that it is very difficult to fully model an autonomous underwater vehicle (AUV) in the complex water environment, this paper presents a model-free tracking control strategy for an AUV in the presence of unknown disturbances. We first formulate an optimized control problem by defining a track -ing Hamilton-Jacobi-Isaac (HJI) equation. Then, we present a reinforcement learning (rl) algorithm to compute an optimized solution by learning from the HJI equation online. It is noted that during the learn-ing period, no information about the AUV's dynamics is needed. In order to demonstrate the efficiency of the proposed strategy, numerical simulation is considered, results are validated and discussed. (c) 2021 Elsevier B.V. All rights reserved.
Aiming at studying the vibration characteristics and active control of a coupling system with four flexible beams connected by springs, an experimental platform is built. The dynamic equation of the system is solved b...
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Aiming at studying the vibration characteristics and active control of a coupling system with four flexible beams connected by springs, an experimental platform is built. The dynamic equation of the system is solved by finite element method (FEM), and the parameter model based on state space equation is deduced. In order to ensure the accuracy of the parameter model, an experimental identification method based on wavelet transform and optimization algorithm is adopted. The state matrix, observation matrix and control force coefficient matrix in the parameterized model are solved in turn. A multi-agent based Heterogeneous-Agent Trust Region Policy Optimization (HATRPO) reinforcement learning (rl) algorithm is designed. The HATRPO rl algorithm interacts with the identified parameter model. After several rounds of training, the HATRPO rl vibration controller is finally obtained. The simulation and experimental results show that the HATRPO rl controller can well compensate for the nonlinearity and uncertainty in the multi-flexible beam coupling system. In addition, the nonlinear characteristics of the HATRPO rl algorithm effectively solve the problem of insufficient control power of traditional linear controller in small vibration amplitude, and realize faster vibration suppression.
An active vibration control algorithm based on reinforcement learning (rl) is applied to suppress the coupling vibration of a multi-flexible beam coupling system. The experimental setup of four-flexible beam coupling ...
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An active vibration control algorithm based on reinforcement learning (rl) is applied to suppress the coupling vibration of a multi-flexible beam coupling system. The experimental setup of four-flexible beam coupling system is constructed. Piezoelectric sensors/actuators are used to detect vibration signals and suppress vibration. The finite element method (FEM) is used to establish the system dynamics model, and the model is modified by identifying parameters using the experimental data to obtain an accurate system model. The identified model is used as the simulation environment of rl algorithm. The multi-agent twin delayed deep deterministic policy gradient (MATD3) algorithm is designed to train the rl vibration controller through interaction with the simulation environment. The trained rl vibration controller is used to suppress the vibration of the four-flexible beam coupling system in simulation and experimental environment. Simulation and experimental results show that compared with proportional and derivative (PD) controller, the rl controller trained by the MATD3 algorithm has better control effect, especially for small amplitude vibration. (C) 2022 Elsevier Masson SAS. All rights reserved.
Condition-based maintenance (CBM) involves taking decisions on maintenance or repair based on the actual deterioration conditions of the components. The long-run average cost is minimised by choosing the right mainten...
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Condition-based maintenance (CBM) involves taking decisions on maintenance or repair based on the actual deterioration conditions of the components. The long-run average cost is minimised by choosing the right maintenance action at the right time. In this study, the CBM decision-making problem is modelled as a continuous semi-Markov decision process (CSMDP). It consists of a chain of states representing various stages of deterioration, a set of maintenance actions, their costs and scheduled inspection policy. The application of a reinforcement learning (rl) algorithm based on the average reward for CSMDPs in CBM is described. The rl algorithm is used to learn the optimal maintenance decisions and inspection schedule based on the current health state of the component.
In a passive millimeter wave (PMMW) imaging system, the resolution of the acquired image is limited by the antenna size. The Richardson-Lucy (rl) algorithm is a simple and nonlinear method, which can improve the resol...
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ISBN:
(纸本)9783037858646
In a passive millimeter wave (PMMW) imaging system, the resolution of the acquired image is limited by the antenna size. The Richardson-Lucy (rl) algorithm is a simple and nonlinear method, which can improve the resolution of the image. However, when the noise can not be neglected, it is difficult for rl algorithm to get good restoration of the corrupted image. To the best of our knowledge, the block-matching with 3D transform domain collaborative filtering (BM3D) algorithm achieves very good performance in image de-noising. In order to improve the resolution of passive millimeter wave images, a rl imaging algorithm for passive millimeter wave based on BM3D is proposed in this paper. The modified algorithm effectively reduces the influence of noise on rl algorithm by using de-noise algorithm based on BM3D. Experimental results demonstrate that the proposed algorithm improves the performance of rl algorithm. Furthermore, the algorithm can be easily implemented for passive millimeter wave imaging.
Control by interconnection (CbI) is a dynamic output-feedback approach used to control port-Hamiltonian (PH) systems. Here, both the plant and the controller are modelled in PH form, in terms of their own Hamiltonians...
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ISBN:
(纸本)9781479977888
Control by interconnection (CbI) is a dynamic output-feedback approach used to control port-Hamiltonian (PH) systems. Here, both the plant and the controller are modelled in PH form, in terms of their own Hamiltonians. However, obtaining an appropriate controller Hamiltonian is generally difficult. In this paper, we address this issue by using reinforcement learning (rl). Additionally due to the semi-supervised optimization nature of the rl algorithms, a performance criterion can be readily included in CbI. We demonstrate the usefulness of the proposed learning algorithm for stabilization of a manipulator arm.
Ringing is one of the most common disturbing artifacts in image deconvolution. With a totally known kernel, the standard Richardson-Lucy (rl) algorithm succeeds in many motion deblurring processes, but the resulting i...
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Ringing is one of the most common disturbing artifacts in image deconvolution. With a totally known kernel, the standard Richardson-Lucy (rl) algorithm succeeds in many motion deblurring processes, but the resulting images still contain visible ringing. When the estimated kernel is different from the real one, the result of the standard rl iterative algorithm will be worse. To suppress the ringing artifacts caused by failures in the blur kernel estimation, this paper improves the rl algorithm based on the local prior. Firstly, the standard deviation of pixels in the local window is computed to find the smooth region and the image gradient in the region is constrained to make its distribution consistent with the deblurring image gradient. Secondly, in order to suppress the ringing near the edge of a rigid body in the image, a new mask was obtained by computing the sharp edge of the image produced using the first step. If the kernel is large-scale, where the foreground is rigid and the background is smoothing, this step could produce a significant inhibitory effect on ringing artifacts. Thirdly, the boundary constraint is strengthened if the boundary is relatively smooth. As a result of the steps above, high-quality deblurred images can be obtained even when the estimated kernels are not perfectly accurate. On the basis of blurred images and the related kernel information taken by the additional hardware, our approach proved to be effective. (C) 2010 Elsevier Ltd. All rights reserved.
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