This paper considers the value iteration algorithms of stochastic zero-sum linear quadratic games with unkown ***-policy and off-policy learning algorithms are developed to solve the stochastic zero-sum games,where th...
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This paper considers the value iteration algorithms of stochastic zero-sum linear quadratic games with unkown ***-policy and off-policy learning algorithms are developed to solve the stochastic zero-sum games,where the system dynamics is not *** analyzing the value function iterations,the convergence of the model-based algorithm is *** equivalence of several types of value iteration algorithms is *** effectiveness of model-free algorithms is demonstrated by a numerical example.
To address nonlinearity, strong coupling, and disturbances in fighter aircraft attitude control, this paper proposes an intelligent control method based on multi-agent deep deterministic policy gradient (MADDPG) and l...
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This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking pe...
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This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.
Thermal modeling and analysis are critical for permanent magnet linear synchronous motors (PMLSMs), particularly in multi-physical analysis and motor design optimization. This paper proposes a new method for thermal s...
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One of the most important goals of theoretical ecologists is to find a strategy for controlling the chaos in ecological models to maintain healthy ecosystems. We investigate the influence of fear and the supply of add...
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Data-driven soft sensing has become quite popular in recent years, which can provide real-time estimations of key variables in industrial processes. While the introduction of deep learning does improve the prediction ...
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This paper proposes a novel data-driven finite-time adaptive control method for the spacecraft attitude tracking control problem with inertial uncertainty. Based on the dynamic regression extension technique, the dist...
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Although deep learning methods have been widely applied in slam visual odometry over the past decade with impressive improvements, the accuracy remains limited in complex dynamic environments. In this paper, a compo...
Although deep learning methods have been widely applied in slam visual odometry over the past decade with impressive improvements, the accuracy remains limited in complex dynamic environments. In this paper, a composite mask-based generative adversarial network is introduced to predict camera motion and binocular depth maps. Specifically, a perceptual generator is constructed to obtain the corresponding parallax map and optical flow from between two neighboring frames. Then, an iterative pose improvement strategy is proposed to improve the accuracy of pose estimation. Finally, a composite mask is embedded in the discriminator to sense structural deformation in the synthesized virtual image, thereby increasing the overall structural constraints of the network model, improving the accuracy of camera pose estimation, and reducing drift issues in the Visual Odometer. Detailed quantitative and qualitative evaluations on the KITTI dataset show that the proposed framework outperforms existing conventional, supervised learning and unsupervised depth VO methods, providing better results in both pose estimation and depth estimation.
In this paper, a novel method combining orthogonal polarization laser self-mixing interference is proposed. The method utilizes a rotating cuvette to measure the refractive index of liquids at different concentration...
In this paper, a novel method combining orthogonal polarization laser self-mixing interference is proposed. The method utilizes a rotating cuvette to measure the refractive index of liquids at different concentrations. The cuvette is filled with the liquid to be measured and rotated by a certain angle. The change in the number of interference fringes, caused by comparing an empty cuvette with a liquid-filled cuvette, is used to calculate the refractive index of the liquid. A four-fold logic subdivision algorithm is then used to improve measurement resolution. The experimental results show that for pure water and different NaCl and glucose solutions concentrations, the average relative errors are 0.47%, 0.59%, and 2.17%, respectively, with the maximum relative error within ±2.54%. The standard deviation of all solutions is less than 3.4%.
In order to identify the tilt direction of the self-mixing interference (SMI) signals under weak feedback regime interfered by noise, a deep learning method is proposed. The one-dimensional U-Net (1D U-Net) neural ne...
In order to identify the tilt direction of the self-mixing interference (SMI) signals under weak feedback regime interfered by noise, a deep learning method is proposed. The one-dimensional U-Net (1D U-Net) neural network can discriminate the direction of self-mixing fringes accurately and quickly. For experimental SMI signals, the 1D U-Net can be used for discriminant direction after a one-step normalization. Simulation and experimental results show that the proposed method is suitable for SMI signals with noise within the whole weak feedback regime, and can maintain a high discrimination accuracy for signals interfered by 5dB noise. Combined with fringe counting method, accurate and rapid SMI signal displacement reconstruction can be realized.
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