Robust Adaptive Control of a Flexible Transmission System Using Multiple Models

An application of the multiple models adaptive control based on switching and tuning to a flexible transmission system is presented. This approach has been considered in order to assure high control performance in the presence of large load variation on the system. The advantages of the multiple models adaptive control system with respect to the classical adaptive control is illustrated via experimental results. It is also shown that the robustness of the adaptive control system can be improved with the appropriate shaping of a sensitivity function. The use of a parameter estimation algorithm based on the minimization of the closed-loop output error in the multiple models scheme also improves the performance of the system in the tuning phase and makes the adaptation algorithm insensitive to unmodeled output disturbances

A Data-Driven Fixed-Structure Control Design Method with Application to a 2-DOF Gyroscope

This paper presents the practical aspects and application of a novel data-driven, fixed-structure, robust control design method. Only the frequency response data of the system is needed for the design, and no parametric model is required. The method can be used to design fully parametrized continuous- or discrete-time matrix transfer function controllers. The control performance is specified as constraints on the $H_{\infty}$ or $H_2$ norm of weighted sensitivity functions, and a convex formulation of the robust design problem is proposed. An application of the presented method is explored on an experimental setup, where a multivariable controller for a gyroscope is designed based only on the measured frequency response of the system

Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers

Most polyp segmentation methods use CNNs as their backbone, leading to two key issues when exchanging information between the encoder and decoder: 1) taking into account the differences in contribution between different-level features; and 2) designing an effective mechanism for fusing these features. Different from existing CNN-based methods, we adopt a transformer encoder, which learns more powerful and robust representations. In addition, considering the image acquisition influence and elusive properties of polyps, we introduce three novel modules, including a cascaded fusion module (CFM), a camouflage identification module (CIM), and a similarity aggregation module (SAM). Among these, the CFM is used to collect the semantic and location information of polyps from high-level features, while the CIM is applied to capture polyp information disguised in low-level features. With the help of the SAM, we extend the pixel features of the polyp area with high-level semantic position information to the entire polyp area, thereby effectively fusing cross-level features. The proposed model, named Polyp-PVT, effectively suppresses noises in the features and significantly improves their expressive capabilities. Extensive experiments on five widely adopted datasets show that the proposed model is more robust to various challenging situations (e.g., appearance changes, small objects) than existing methods, and achieves the new state-of-the-art performance. The proposed model is available at https://github.com/DengPingFan/Polyp-PVT.Comment: Technical Repor

Dynamical Downscaling Projections of Twenty-First-Century Atlantic Hurricane Activity: CMIP3 and CMIP5 Model-Based Scenarios

Twenty-first-century projections of Atlantic climate change are downscaled to explore the robustness of potential changes in hurricane activity. Multimodel ensembles using the phase 3 of the Coupled Model Intercomparison Project (CMIP3)/Special Report on Emissions Scenarios A1B (SRES A1B; late-twenty-first century) and phase 5 of the Coupled Model Intercomparison Project (CMIP5)/representative concentration pathway 4.5 (RCP4.5; early- and late-twenty-first century) scenarios are examined. Ten individual CMIP3 models are downscaled to assess the spread of results among the CMIP3 (but not the CMIP5) models. Downscaling simulations are compared for 18-km grid regional and 50-km grid global models. Storm cases from the regional model are further downscaled into the Geophysical Fluid Dynamics Laboratory (GFDL) hurricane model (9-km inner grid spacing, with ocean coupling) to simulate intense hurricanes at a finer resolution.A significant reduction in tropical storm frequency is projected for the CMIP3 (-27%), CMIP5-early (-20%) and CMIP5-late (-23%) ensembles and for 5 of the 10 individual CMIP3 models. Lifetime maximum hurricane intensity increases significantly in the high-resolution experimentsby 4%-6% for CMIP3 and CMIP5 ensembles. A significant increase (+87%) in the frequency of very intense (categories 4 and 5) hurricanes (winds 59 m s(-1)) is projected using CMIP3, but smaller, only marginally significant increases are projected (+45% and +39%) for the CMIP5-early and CMIP5-late scenarios. Hurricane rainfall rates increase robustly for the CMIP3 and CMIP5 scenarios. For the late-twenty-first century, this increase amounts to +20% to +30% in the model hurricane\u27s inner core, with a smaller increase (similar to 10%) for averaging radii of 200 km or larger. The fractional increase in precipitation at large radii (200-400 km) approximates that expected from environmental water vapor content scaling, while increases for the inner core exceed this level

Sliding Mode Control

The main objective of this monograph is to present a broad range of well worked out, recent application studies as well as theoretical contributions in the field of sliding mode control system analysis and design. The contributions presented here include new theoretical developments as well as successful applications of variable structure controllers primarily in the field of power electronics, electric drives and motion steering systems. They enrich the current state of the art, and motivate and encourage new ideas and solutions in the sliding mode control area

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available