Robust path-following control of underactuated AUVs with multiple uncertainties and state constraints

This paper proposes a novel robust controller for horizontal path-following problem of an underactuated AUV subject to multiple uncertainties and state constraints. Firstly, four reduced-order extended state observes (ESOs) are designed to estimate the multiple uncertainties, and the estimated values are adopted in the design of kinematic and dynamic controller. Secondly, to address the state constraints, the barrier Lyapunov function is incorporated with the kinematic controller. To resolve the problem of input saturation, the auxiliary design system is utilized in the dynamic controller. To address the problem of “explosion of complexity” inherent in the conventional back-stepping method, a nonlinear tracking differentiator is utilized to obtain the derivative of the desired yaw speed. Finally, the results of numerical simulation are performed to demonstrate the effectiveness of the proposed controller.

A Heuristic Projection Pursuit Method Based on a Connection Cloud Model and Set Pair Analysis for Evaluation of Slope Stability

Determining the projection direction vector (PDV) is essential to the projection pursuit evaluation method for high-dimensional problems under multiple uncertainties. Although the PP method using a cloud model can facilitate interpretation of the fuzziness and randomness of the PDV, it ignores the asymmetry of the PDV and the fact that indicators are actually distributed over finite intervals; it quickly falls into premature defects. Therefore, a novel PP evaluation method based on the connection cloud model (CCM) is discussed to remedy these drawbacks. In this approach, adaptive numerical characteristics of the CCM are adopted to represent the randomness and fuzziness of the candidate PDV and evaluation indicators. Meanwhile, to avoid complex computing and to accelerate the convergence speed of the optimization procedure, an improved fruit fly optimization algorithm (FOA) is set up to find the rational PDV. Alternatively, candidate PDVs are mutated based on the mechanism “pick the best of the best” using set pair analysis (SPA) and chaos theory. Furthermore, the applicability and reliability are discussed based on an illustrative example of slope stability evaluation and comparisons with the neural network method and the PP evaluation method based on the other FOAs and the genetic algorithm. Results indicate that the proposed method with simpler code and quicker convergence speed has good global ergodicity and local searching capabilities, and can better explore the structure of high-dimensional data with multiple uncertainties and asymmetry of the PDV relative to other methods.

The Interplay between Covid-19 and Spondyloarthritis or Its Treatment

Objective The Covid-19 pandemic has created multiple uncertainties regarding rheumatic diseases or their treatment and susceptibility or severity of the viral disease. Methods To address these questions as they relate to spondyloarthritis, we created a longitudinal survey from April 10, 2020 to April 26, 2021. 4723 world-wide subjects with spondyloarthritis and 450 household contacts participated. 3064 of the respondents were from the US and 70.4% of them provided longitudinal data. To control for the duration of potential risk of Covid-19, the rate of contracting Covid-19 was normalized for person months of exposure. Results In an analysis of US subjects who provided longitudinal data, the incident rate ratio for the 159 (out of 2157) subjects who tested positive for Covid-19 was 1.16 compared to the US population as adjusted for age and sex (range 0.997 to 1.361, p=0.059). A paired evaluation using patients and household members did not show a statistically significant effect to indicate a predisposition to develop Covid-19 as a result of spondyloarthritis or its treatment. Our data failed to show that any class of medication commonly used to treat spondyloarthritis significantly affected the risk to develop Covid-19 or the severity of Covid-19. Conclusion These data do not exclude a small increased risk to develop Covid-19 as a result of spondyloarthritis, but the risk, if it exists, is low and not consistently demonstrated. The data should provide reassurance to patients and to rheumatologists about the risk that Covid-19 poses to patients with spondyloarthritis.