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This paper addresses the distributed tracking control of multiple uncertain high-order nonlinear systems with prescribed performance requirements. By introducing a performance function and a nonlinear transformation, the prescribed fixed-time performance tracking control problem is reformulated as a distributed tracking control problem for multiple special nonlinear systems. With the aid of the universal approximation theorem for continuous functions and algebraic graph theory, distributed robust adaptive controllers are designed using the backstepping technique. Simulation results are presented to demonstrate the effectiveness of the proposed algorithms. 

This paper considers the optimal control of a second-order nonlinear system with unknown dynamics. A new reinforcement learning based approach is proposed with the aid of direct adaptive control. By the new approach actor-critic reinforcement learning algorithms are proposed with three neural network approximation. Simulation results are presented to show the effectiveness of the proposed algorithms. 

This paper considers the formation flying of multiple quadrotors with a desired orientation and a leader. In the formation flying control, it is assumed that the desired formation is time-varying and there are the system uncertainty and the information uncertainty. In order to deal with different uncertainties, a backstepping-based approach is proposed for the controller design. In the proposed approach, different types of uncertainties are considered in different steps. By integrating adaptive/robust control results and Laplacian algebraic theory, distributed robust adaptive control laws are proposed such that the formation errors exponentially converge to zero and the attitude of each quadrotor exponentially converges to the desired value. Simulation results show the effectiveness of the proposed algorithms. 

This paper considers the formation flying of multiple quadrotors with a desired orientation and a leader. In the formation flying control, it is assumed that the desired formation is time-varying and there are the system uncertainty and the information uncertainty. In order to deal with different uncertainties, a backstepping-based approach is proposed for the controller design. In the proposed approach, different types of uncertainties are considered in different steps. By integrating adaptive/robust control results and Laplacian algebraic theory, distributed robust adaptive control laws are proposed such that the formation errors exponentially converge to zero and the attitude of each quadrotor exponentially converges to the desired value. Simulation results show the effectiveness of the proposed algorithms. Keywords: Quadrotor · Distributed control · Cooperative control · Leader–follower control · Formation control 

This paper considers the position and attitude tracking control problem of a vertical take-off and landing unmanned aerial vehicle with uncertainty and input constraints. Considering the parametric and non-parametric uncertainties in the dynamics of systems, a robust adaptive tracking controller is proposed with the aid of the special structure of the dynamics of the system. Considering the uncertainty and input constraints, a robust adaptive saturation controller is proposed with the aid of an auxiliary compensated system. Simulation results show the effectiveness of the proposed algorithms. 

This paper considers the position and attitude tracking control problem of a vertical take-off and landing unmanned aerial vehicle with uncertainty and input constraints. Considering the parametric and non-parametric uncertainties in the dynamics of systems, a robust adaptive tracking controller is proposed with the aid of the special structure of the dynamics of the system. Considering the uncertainty and input constraints, a robust adaptive saturation controller is proposed with the aid of an auxiliary compensated system. Simulation results show the effectiveness of the proposed algorithms. 

Abstract Accurate prediction of global land monsoon rainfall on a sub-seasonal (2–8 weeks) time scale has become a worldwide demand. Current forecasts of weekly-mean rainfall in most monsoon regions, however, have limited skills beyond two weeks, calling for a more profound understanding of monsoon intraseasonal variability (ISV). We show that the high-frequency (HF; 8–20 days) ISV, crucial for the Week 2 and Week 3 predictions, accounts for about 53–70% of the total (8–70 days) ISV, generally dominating the sub-seasonal predictability of various land monsoons, while the low-frequency (LF; 20–70 days)’s contribution is comparable to HF only over Australia (AU; 47%), South Asia (SA; 43%), and South America (SAM; 40%). The leading modes of HFISVs in Northern Hemisphere (NH) monsoons primarily originate from different convectively coupled equatorial waves, while from mid-latitude wave trains for Southern Hemisphere (SH) monsoons and East Asian (EA) monsoon. The Madden-Julian Oscillation (MJO) directly regulates LFISVs in Asian-Australian monsoon and affects American and African monsoons by exciting Kelvin waves and mid-latitude teleconnections. During the past four decades, the HF (LF) ISVs have considerably intensified over Asian (Asian-Australian) monsoon but weakened over American (SAM) monsoon. Sub-seasonal to seasonal (S2S) prediction models exhibit higher sub-seasonal prediction skills over AU, SA, and SAM monsoons that have larger LFISV contributions than other monsoons. These results suggest an urgent need to improve the simulation of convectively coupled equatorial waves and two-way interactions between regional monsoon ISVs and mid-latitude processes and between MJO and regional monsoons, especially under the global warming scenarios. 

The immunohistochemical (IHC) staining of the human epidermal growth factor receptor 2 (HER2) biomarker is widely practiced in breast tissue analysis, preclinical studies, and diagnostic decisions, guiding cancer treatment and investigation of pathogenesis. HER2 staining demands laborious tissue treatment and chemical processing performed by a histotechnologist, which typically takes one day to prepare in a laboratory, increasing analysis time and associated costs. Here, we describe a deep learning-based virtual HER2 IHC staining method using a conditional generative adversarial network that is trained to rapidly transform autofluorescence microscopic images of unlabeled/label-free breast tissue sections into bright-field equivalent microscopic images, matching the standard HER2 IHC staining that is chemically performed on the same tissue sections. The efficacy of this virtual HER2 staining framework was demonstrated by quantitative analysis, in which three board-certified breast pathologists blindly graded the HER2 scores of virtually stained and immunohistochemically stained HER2 whole slide images (WSIs) to reveal that the HER2 scores determined by inspecting virtual IHC images are as accurate as their immunohistochemically stained counterparts. A second quantitative blinded study performed by the same diagnosticians further revealed that the virtually stained HER2 images exhibit a comparable staining quality in the level of nuclear detail, membrane clearness, and absence of staining artifacts with respect to their immunohistochemically stained counterparts. This virtual HER2 staining framework bypasses the costly, laborious, and time-consuming IHC staining procedures in laboratory and can be extended to other types of biomarkers to accelerate the IHC tissue staining used in life sciences and biomedical workflow.