Mo Yan’s \u3cem\u3eRadish\u3c/em\u3e: Between the Real and the Surreal

In their article “Mo Yan’s Radish: Between the Real and the Surreal” Xiaoyue Li and Xuefang Feng argue that Radish, a novella written by Nobel Prize winner Mo Yan skillfully combines realism with surrealism, the flexible swing between which is made possible by the choice of children’s perspective, and the effect is too significant to be ignored. Their analysis concludes that the transparent golden radish symbolizes humanity, the lack of which projects the protagonist’ sufferings, and that the surrealist description of the radish reveals the magic function of humanity in brightening up life in times of miseries

ON ITERATIVE LEARNING CONTROL FOR SOLVING NEW CONTROL PROBLEMS

Ph.DDOCTOR OF PHILOSOPH

Weighted endpoint estimates for commutators of multilinear fractional integral operators

summary:Let $m$ be a positive integer, $0<\alpha <mn$, $\vec {b}=(b_{1},\cdots ,b_{m})\in {\rm BMO}^m$. We give sufficient conditions on weights for the commutators of multilinear fractional integral operators \Cal {I}^{\vec {b}}_{\alpha } to satisfy a weighted endpoint inequality which extends the result in D. Cruz-Uribe, A. Fiorenza: Weighted endpoint estimates for commutators of fractional integrals, Czech. Math. J. 57 (2007), 153–160. We also give a weighted strong type inequality which improves the result in X. Chen, Q. Xue: Weighted estimates for a class of multilinear fractional type operators, J. Math. Anal. Appl., 362, (2010), 355–373

Robust Adaptive Learning-based Path Tracking Control of Autonomous Vehicles under Uncertain Driving Environments

This paper investigates the path tracking control problem of autonomous vehicles subject to modelling uncertainties and external disturbances. The problem is approached by employing a 2-degree of freedom vehicle model, which is reformulated into a newly defined parametric form with the system uncertainties being lumped into an unknown parametric vector. On top of the parametric system representation, a novel robust adaptive learning control (RALC) approach is then developed, which estimates the system uncertainties through iterative learning while treating the external disturbances by adopting a robust term. It is shown that the proposed approach is able to improve the lateral tracking performance gradually through learning from previous control experiences, despite only partial knowledge of the vehicle dynamics being available. It is noteworthy that a novel technique targeting at the non-square input distribution matrix is employed so as to deal with the under-actuation property of the vehicle dynamics, which extends the adaptive learning control theory from square systems to non-square systems. Moreover, the convergence properties of the RALC algorithm are analysed under the framework of Lyapunov-like theory by virtue of the composite energy function and the λ-norm. The effectiveness of the proposed control scheme is verified by representative simulation examples and comparisons with existing methods