This investigation introduces a sophisticated robust fuzzy distributed protocol, which synergistically merges the strengths of robust control and fuzzy control to confront the global consensus conundrum in unknown multi-agent systems. The technological ingenuity of this protocol lies in its integration of a seamless switching function, which ensures the robust and effective functionality of the fuzzy protocol across a broad global spectrum. Furthermore, the study delves into the global consensus dilemma in both first-order and second-order stochastic unknown multi-agent systems, outlining the specific design framework for robust fuzzy controllers. To uphold the stability of the closed-loop systems, the investigation innovatively formulates a novel type of Lyapunov function, inspired by the tenets of Lyapunov quadratic form design. Conclusively, through a series of simulation experiments, the investigation substantiates the practical effectiveness of the proposed algorithms. Note to Practitioners—Practitioners in automation, robotics, and distributed decision-making face a significant challenge in achieving global consensus in multi-agent systems amidst uncertainties and disturbances. This research introduces a sophisticated robust fuzzy distributed protocol that integrates robust control and fuzzy control, leveraging a switching function to ensure effectiveness across various scenarios. The study provides a detailed design framework for robust fuzzy controllers in first- and second-order stochastic unknown multi-agent systems, crucial for developing resilient strategies to maintain system stability and performance. Innovatively, a novel Lyapunov function, inspired by Lyapunov quadratic form design, upholds closed-loop system stability, offering a theoretical foundation for control strategies. Simulation experiments confirm the protocol’s practical effectiveness, achieving high-efficiency and reliable global consensus in unknown MASs. Preliminary results suggest promising practical implementation, benefiting practitioners across various fields.

This article presents a novel distributed robust fuzzy control scheme to address the global consensus problem of unknown nonlinear multiagent systems (MASs). By replacing the nonlinear dynamic model constrained by the global Lipschitz condition with a more general system model, the proposed approach enhances applicability. A robust fuzzy control scheme based on a smooth switching function is introduced, effectively resolving the global consensus problem for unknown nonlinear systems. Furthermore, time-varying σ-modification terms are incorporated into the adaptive parameter design, replacing constant terms to avoid asymptotically uniform ultimate boundedness and ensuring global asymptotic consensus of the closed-loop systems. The efficacy of the proposed scheme is demonstrated through simulation results.

Reconstructing CSG trees from CAD models is a critical subject in reverse engineering. While there have been notable advancements in CSG reconstruction, challenges persist in capturing geometric details and achieving efficiency. Additionally, since non-axis-aligned volumetric primitives cannot maintain coplanar characteristics due to discretization errors, existing Boolean operations often lead to zero-volume surfaces and suffer from topological errors during the CSG modeling process. To address these issues, we propose a novel workflow to achieve fast CSG reconstruction and reliable forward modeling. First, we employ feature removal and model subdivision techniques to decompose models into sub-components. This significantly expedites the reconstruction by simplifying the complexity of the models. Then, we introduce a more reasonable method for primitive generation and filtering, and utilize a size-related optimization approach to reconstruct CSG trees. By re-adding features as additional nodes in the CSG trees, our method not only preserves intricate details but also ensures the conciseness, semantic integrity, and editability of the resulting CSG tree. Finally, we develop a coplanar primitive discretization method that represents primitives as large planes and extracts the original triangles after intersection. We extend the classification of triangles and incorporate a coplanar-aware Boolean tree assessment technique, allowing us to achieve manifold and watertight modeling results without zero-volume surfaces, even in extreme degenerate cases. We demonstrate the superiority of our method over state-of-the-art approaches. Moreover, the reconstructed CSG trees generated by our method contain extensive semantic information, enabling diverse model editing tasks.

This article studies the human-in-the-loop fuzzy iterative learning control of leader-following consensus for unknown mixed-order nonlinear multi-agent systems. The human operator participates in the cooperative control of multi-agent systems, which indirectly affects the followers by directly controlling the leader. Moreover, the leader's input is unknown to all followers. The mixed-order multi-agent systems contain both first- and second-order agents, which include the special case of the second-order multi-agent systems. By using fuzzy logic systems to approximate unknown nonlinear dynamics, a fully distributed fuzzy iterative learning controller with time-varying coupling gain is designed. In the estimation parameters, a$\sigma$-modification related to the number of iterations is designed to ensure the convergence of the closed-loop systems. Based on the new composite energy function, the exact consensus of the closed-loop systems is proved. Finally, the simulation results verify the effectiveness of the designed control algorithm.

Recent years have witnessed a wide adoption of various lossy compression techniques to alleviate the burden on high-performance computing (HPC) systems that run large-scale scientific simulations producing large amounts of data. Peak signal-to-noise ratio (PSNR) is considered one of the most important indicators for measuring the distortion between reconstructed and original data and evaluating the performance of lossy compressors. However, the complex interplay between the error introduced during quantization and PSNR requires in-depth exploration to meet maximum compression potential and user-provided PSNR simultaneously. This paper aims to achieve this goal by exploring a novel quantization process to support a fixed-PSNR mode for a transform-based lossy compressor called DCTZ-F. We evaluate DCTZ-F with six real-world scientific datasets from several solvers in FLASH, a multi-physics application code. Our experimental results show that DCTZ-F can generate up to three times the compression ratio than SZ with fixed-PSNR mode while meeting the user-defined PSNR.

High-performance computing (HPC) systems that run scientific simulations of significance produce a large amount of data during runtime. Transferring or storing such big datasets causes a severe I/O bottleneck and a considerable storage burden. Applying compression techniques, particularly lossy compressors, can reduce the size of the data and mitigate such overheads. Unlike lossless compression algorithms, error-controlled lossy compressors could significantly reduce the data size while respecting the user-defined error bound. DCTZ is one of the transform-based lossy compressors with a highly efficient encoding and purpose-built error control mechanism that accomplishes high compression ratios with high data fidelity. However, since DCTZ quantizes the DCT coefficients in the frequency domain, it may only partially control the relative error bound defined by the user. In this paper, we aim to improve the compression quality of DCTZ. Specifically, we propose a preconditioning method based on level offsetting and scaling to control the magnitude of input of the DCTZ framework, thereby enforcing stricter error bounds. We evaluate the performance of our method in terms of compression ratio and rate distortion with real-world HPC datasets. Our experimental result shows that our method can achieve a higher compression ratio than other state-of-the-art lossy compressors with a tighter error bound while precisely guaranteeing the user-defined error bound.

This article investigates the consensus problem of mixed-order nonlinear multiagent systems (MASs). First, a new research framework of consensus control for MASs with hybrid-order dynamics is established. In this framework, the order of low-order dynamic subsystems is increased to higher-order dynamic subsystems by means of increasing order technology, so that the mixed-order MASs can be changed into the same-order MASs. Thus, the distributed controller of hybrid-order MASs can be designed by using the consensus control method of the same-order MASs. Second, through a case study of a stochastic mixed first- and second-order nonlinear MASs, this article further expounds the design idea of the framework structure and gives the concrete design form of the distributed controller and the stability analysis of the closed-loop system. Finally, simulations are given to verify the effectiveness of the distributed control protocol in this case.

Lossy compression on scientific data is coming into prominence as the scientific workflow is hampered significantly by large amounts of data produced by high-performance computing (HPC) applications. State-of-the-art lossy compressors, such as SZ and ZFP, show promising rate-distortion efficiency. However, as the data storage burden and need for feature-preserving compression continue to grow, relying on unitary or single-stage compression is becoming insufficient for obtaining desirable data reductions and feature preservation. This paper aims to improve the compression ratio by taking advantage of information retrieval (IR), a well-established topic but underexplored in lossy compression for scientific data. We propose our lossy compression technique, called DPZ, based on multistage feature extractions, a commonly employed step in IR. Unlike the prior works where the compression is either done by predicting or bit-plane encoding, this work focuses on preserving the key data content from each stage to the maximum extent, ultimately elevates the compression ratio. With the application of discrete cosine transform, principal component analysis, and quantization, DPZ obtains the dominant features with the least amount of bits possible. Specifically, a knee-point detection and an explained variance variation method are designed for finding optimal tradeoffs. DPZ also employs a sampling strategy to reduce computational overhead and estimate compressibility and parameters before compression. We evaluate the performance of DPZ using real-world scientific datasets. Experiments demonstrate that DPZ achieves superior compression ratios through multi-stage retrievals and outperforms SZ and ZFP at medium to high accuracy on most of the evaluated datasets.

This paper investigates the global consensus problems for the first-order and second-order unknown nonlinear multiagent systems (MASs) with uncertain input disturbance. Fuzzy logic systems are applied to solve the global consensus problem for unknown nonlinear MASs. A fully distributed adaptive fuzzy control is designed to enable followers asymptotically to track the leader without using any dynamics of the leader. The global consensus conditions are also derived for the first-order and second-order unknown MASs, which overcomes the drawback of the semiglobal consensus in existing literature. It is worth mentioning that the proposed approach can greatly alleviate the computation burden because it only needs to update a few parameters. An efficient framework is also given to achieve the global formation control of the second-order unknown nonlinear MAS with an undirected connected graph. Finally, four simulated examples are given to illustrate the effectiveness of the proposed control protocols.