The performance of the model-based controller is always affected by the uncertainty and nonlinearity of the model parameters in the vehicle path tracking process. To address this issue, a novel path tracking controller based on model-free adaptive dynamic programming (ADP) is proposed for autonomous vehicles in this paper. To be specific, the proposed controller obtains information from the online state and front-wheel angle input data which are repeatedly used to calculate the controller gain iteratively. So, this controller features not requiring accurate knowledge of vehicle model parameters for controller development. Meanwhile, the path tracking performance of the autonomous vehicle will be inevitably disturbed by unknown nonlinear external disturbance. To approximate this disturbance, the learning characteristics of Radial Basis Function Neural Network (RBFNN) are applied to generate compensation for the front-wheel angle. Afterward, the weight updating law of RBFNN is derived by Lyapunov function to ensure the stability and convergence of the whole system. Finally, Hardware in the loop (HIL) test results demonstrate that the proposed ADP-RBF controller can improve the comprehensive performance of the vehicle path tracking control system and achieve the balance between path tracking accuracy and minimum sideslip angle.

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For 4MIDEV with differential steering, the trajectory tracking multi-objective control method is studied. The dynamics model of the four-in-wheel motor independent-drive vehicle (4MIDEV) is established comprehensively considering the differential steering dynamics characteristics, longitudinal dynamics characteristics, and lateral dynamics characteristics. Takagi Sugeno (T-S) fuzzy method is adopted to deal with the nonlinear and time-varying characteristics of the model parameters such as vehicle longitudinal speed and the front steering wheel angle. The trajectory tracking control targets are divided into the kinematic control target considering the trajectory tracking accuracy and the dynamics control target considering longitudinal speed tracking as well as lateral stability performance. The generalized 2 norm and ∞ norm are selected to constrain the kinematic control target and the dynamics control target respectively. The generalized 2 / ∞ trajectory tracking controller is designed to improve the trajectory tracking accuracy and longitudinal speed tracking as well as lateral stability performance. The hardware in the loop test (HIL) verified that the designed trajectory tracking multi-objective controller can improve the comprehensive performance of vehicle and balance the trajectory tracking accuracy between the longitudinal and lateral dynamics performance.

Background: This article provides an overview of the current status and research progress of mesothelioma. Methods: A total of 2638 documents published from 1 January 2004 to 30 November 2022 were retrieved from the Web of Science Core Collection and analyzed via Microsoft Office Excel 2019, VOSviewer 1.6.18, and Tableau 2022.2. Results: There was an obvious increase in the number of publications regarding mesothelioma in the last 18 years, with the United States dominating the research field with 715 publications and 23,882 citations, while the University of Turin contributed the most (118). Occupational & Environmental Medicine was the most popular journal (80), with Corrado Magnani being the most prolific author (52) and Michele Carbone obtaining the most citations (4472). “Oncology” and “Health Science of Environment & Occupation” were the two main subjects, while the keywords “asbestos”, “lung cancer”, “gene expression”, “apoptosis”, “survival”, and “cisplatin” were the most popular. Conclusions: The containment of mesothelioma calls for more participation from low- and middle-income countries, and further attention needs to be paid to clinical research.