This article studies the adaptive optimal output regulation problem for a class of interconnected singularly perturbed systems(SPSs) with unknown dynamics based on reinforcement learning(RL).Taking into account the sl...This article studies the adaptive optimal output regulation problem for a class of interconnected singularly perturbed systems(SPSs) with unknown dynamics based on reinforcement learning(RL).Taking into account the slow and fast characteristics among system states,the interconnected SPS is decomposed into the slow time-scale dynamics and the fast timescale dynamics through singular perturbation theory.For the fast time-scale dynamics with interconnections,we devise a decentralized optimal control strategy by selecting appropriate weight matrices in the cost function.For the slow time-scale dynamics with unknown system parameters,an off-policy RL algorithm with convergence guarantee is given to learn the optimal control strategy in terms of measurement data.By combining the slow and fast controllers,we establish the composite decentralized adaptive optimal output regulator,and rigorously analyze the stability and optimality of the closed-loop system.The proposed decomposition design not only bypasses the numerical stiffness but also alleviates the high-dimensionality.The efficacy of the proposed methodology is validated by a load-frequency control application of a two-area power system.展开更多
In the field of reversible data hiding(RDH),designing a high-precision predictor to reduce the embedding distortion and developing an effective embedding strategy to minimize the distortion caused by embedding informa...In the field of reversible data hiding(RDH),designing a high-precision predictor to reduce the embedding distortion and developing an effective embedding strategy to minimize the distortion caused by embedding information are the two most critical aspects.In this paper,we propose a new RDH method,including a predictor based on a transformer and a novel embedding strategy with multiple embedding rules.In the predictor part,we first design a transformer-based predictor.Then,we propose an image division method to divide the image into four parts,which can use more pixels as context.Compared with other predictors,the transformer-based predictor can extend the range of pixels for prediction from neighboring pixels to global ones,making it more accurate in reducing the embedding distortion.In the embedding strategy part,we first propose a complexity measurement with pixels in the target blocks.Then,we develop an improved prediction error ordering rule.Finally,we provide an embedding strategy including multiple embedding rules for the first time.The proposed RDH method can effectively reduce the distortion and provide satisfactory results in improving the visual quality of data-hidden images,and experimental results show that the performance of our RDH method is leading the field.展开更多
基金supported by the National Natural Science Foundation of China (62073327,62273350)the Natural Science Foundation of Jiangsu Province (BK20221112)。
文摘This article studies the adaptive optimal output regulation problem for a class of interconnected singularly perturbed systems(SPSs) with unknown dynamics based on reinforcement learning(RL).Taking into account the slow and fast characteristics among system states,the interconnected SPS is decomposed into the slow time-scale dynamics and the fast timescale dynamics through singular perturbation theory.For the fast time-scale dynamics with interconnections,we devise a decentralized optimal control strategy by selecting appropriate weight matrices in the cost function.For the slow time-scale dynamics with unknown system parameters,an off-policy RL algorithm with convergence guarantee is given to learn the optimal control strategy in terms of measurement data.By combining the slow and fast controllers,we establish the composite decentralized adaptive optimal output regulator,and rigorously analyze the stability and optimality of the closed-loop system.The proposed decomposition design not only bypasses the numerical stiffness but also alleviates the high-dimensionality.The efficacy of the proposed methodology is validated by a load-frequency control application of a two-area power system.
基金Project supported by the National Natural Science Foundation of China(No.62172053)the National Key Research and Development Program of China(Nos.2021YFC3340701 and 2021YFC3340602)。
文摘In the field of reversible data hiding(RDH),designing a high-precision predictor to reduce the embedding distortion and developing an effective embedding strategy to minimize the distortion caused by embedding information are the two most critical aspects.In this paper,we propose a new RDH method,including a predictor based on a transformer and a novel embedding strategy with multiple embedding rules.In the predictor part,we first design a transformer-based predictor.Then,we propose an image division method to divide the image into four parts,which can use more pixels as context.Compared with other predictors,the transformer-based predictor can extend the range of pixels for prediction from neighboring pixels to global ones,making it more accurate in reducing the embedding distortion.In the embedding strategy part,we first propose a complexity measurement with pixels in the target blocks.Then,we develop an improved prediction error ordering rule.Finally,we provide an embedding strategy including multiple embedding rules for the first time.The proposed RDH method can effectively reduce the distortion and provide satisfactory results in improving the visual quality of data-hidden images,and experimental results show that the performance of our RDH method is leading the field.
