Cheng Han^1,*, Zhengguang Xu^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 515-536, 2024, DOI:10.32604/cmes.2023.043464

Abstract A prediction framework based on the evolution of pattern motion probability density is proposed for the output prediction and estimation problem of non-Newtonian mechanical systems, assuming that the system satisfies the generalized Lipschitz condition. As a complex nonlinear system primarily governed by statistical laws rather than Newtonian mechanics, the output of non-Newtonian mechanics systems is difficult to describe through deterministic variables such as state variables, which poses difficulties in predicting and estimating the system’s output. In this article, the temporal variation of the system is described by constructing pattern category variables, which are non-deterministic variables. Since pattern category variables have… More >

Zhiguo Wang^*, Fangqing Gao, Fei Liu

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 343-355, 2023, DOI:10.32604/cmes.2022.020569

Abstract In this paper, an improved high-order model-free adaptive iterative control (IHOMFAILC) method for a class of nonlinear discrete-time systems is proposed based on the compact format dynamic linearization method. This method adds the differential of tracking error in the criteria function to compensate for the effect of the random disturbance. Meanwhile, a high-order estimation algorithm is used to estimate the value of pseudo partial derivative (PPD), that is, the current value of PPD is updated by that of previous iterations. Thus the rapid convergence of the maximum tracking error is not limited by the initial value of PPD. The convergence… More >