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Your search keyword '"Li, Xiao-bin"' showing total 552 results
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552 results on '"Li, Xiao-bin"'

40. Integral reinforcement learning‐based optimal tracking control for uncertain nonlinear systems under input constraint and specified performance constraints.

Author

Chang, Ru, Liu, Zhi‐Meng, Li, Xiao‐Bin, and Sun, Chang‐Yin

Subjects
MACHINE learning, REINFORCEMENT learning, COST functions, NONLINEAR systems, UNCERTAIN systems, HAMILTON-Jacobi equations
Abstract

This article addresses the optimal tracking control problem with prescribed performance for uncertain nonlinear systems subject to input constraint and unknown disturbances. First, a fixed‐time monotonic convergence function is introduced to restrain tracking error, and a nonlinear mapping technique is employed to transform the constrained error into an unconstrained variable, then the fixed‐time output tracking issue is boiled down to the boundedness problem of the transformed variable. With the aid of a nonquadratic cost function, the input constraint is encoded into the optimization problem. To solve the unknown disturbances, an auxiliary system and an auxiliary disturbance policy are constructed, and the optimal control problem is formulated as a two‐player zero‐sum game. Moreover, a Hamilton–Jacobi–Isaacs (HJI) equation associated with this nonquadratic zero‐sum game is established to give the optimal control and the worst‐case disturbance policy solution. Subsequently, to avoid using knowledge of the system dynamics, three neural network approximators, namely, actor, critic, and disturbance, which are tuned online and simultaneously for approximating the solution of HJI, are constructed based on the integral reinforcement learning algorithm. Theoretical analysis shows that the reconstructed error system states and the weight estimation errors are semi‐globally uniformly ultimately bounded. Finally, the simulation study further tests the availability of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Published
2024
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46. Effects of random heterogeneity of soil on VH failure envelopes of the torpedo anchor.

Author

Xu, Shi Jie, Yi, Jiang Tao, Yi, Jin Wen, Li, Xiao Bin, Tian, Ye, Xie, Qiang, and Liu, Fei

Subjects
MONTE Carlo method, HETEROGENEITY, SOIL mechanics, SOILS, ANCHORS
Abstract

Torpedo anchor is one offshore anchoring which has been gaining popularity recently for floating structures. The torpedo anchor was expected to resist the environmental loading transferred from the floating platform. Thus, the understanding of the holding capacity of torpedo anchors under vertical and horizontal combined loading is the key concern of its design. Nonetheless, most of the previous studies are deterministic without considering the common existing random heterogeneity nature of marine soil. In light of this situation, this paper reports a large deformation random finite element study under the framework of the Monte Carlo method. VH failure envelopes of torpedo anchors in heterogeneous soil were obtained by conducting simulations of continuous extraction procedures along different directions. Furthermore, the strain-softening behaviour of the soil was also accounted for considering the server deformation of the soil. The results show that both the size and shape of the VH failure envelope are significantly affected by random heterogeneity of soil. A series of VH failure envelopes of various probabilities of failure was thus given to guide the design. Results in this paper may be constructive for the safety design of the torpedo anchor. [ABSTRACT FROM AUTHOR]

Published
2024
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