A Unified Predefined-Time Convergent and Robust ZNN Model for Constrained Quadratic Programming

Hu, Zeshan; Xiao, Lin; Dai, Jianhua; Xu, Yang; Zuo, Qiuyue; Liu, Chubo

doi:10.1109/tii.2020.2996215

Cited by 41 publications

(3 citation statements)

References 28 publications

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“…A S a fundamental optimization problem, the QP problem is pervasive in the realm of science and technology, driven by the compelling need for robust and precise optimization. For example, investment portfolio [1], robotics [2]- [7], predictive control [8], scheduling controller design [9], and image processing [2], [10], [11]. Moreover, numerous engineering problems find resolution through the transformation of the original problem into a QP formulation.…”

Section: Introductionmentioning

confidence: 99%

A High Order Integration Enhanced Zeroing Neural Network for Time-Varying Convex Quadratic Program Against Nonlinear Noise

Li,

Rong,

et al. 2024

IEEE Access

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The quadratic programming (QP) problem constitutes a distinctive class within mathematical optimization, prevalent in scientific computations and engineering applications. In practical scenarios, noise interference has the potential to adversely impact the solution accuracy of time-varying QP (TVQP) problems, thereby compromising the performance of established neural network models, including the original zeroing neural network (OZNN). Consequently, certain variants of the Zeroing Neural Network (ZNN) model exhibit notable tolerance to constant or linear noise; however, it is imperative to acknowledge the prevalence of more intricate forms of noise, particularly nonlinear noise. To ameliorate compromised solution accuracy in the presence of nonlinear noise, this study introduces a novel Zeroing Neural Network (ZNN) model, denoted as Triple Integral Enhancement Zeroing Neural Network (TIEZNN). The model incorporates a novel error processing mechanism strategically enhanced through triple integral augmentation, designed specifically to address TVQP perturbed by nonlinear noise. Theoretical analysis rigorously establishes the convergence properties and commendable noise suppression capabilities intrinsic to the proposed TIEZNN model. Compared with the traditional gradient-based neural network and the previous ZNN model and its variants, the TIEZNN model proposed in this paper is still highly robust and accurate in the face of nonlinear noise. Simulation results unequivocally demonstrate the efficiency and superior performance of the TIEZNN model in addressing TVQP challenges, particularly in scenarios featuring equality and inequality constraints influenced by nonlinear noise. INDEX TERMS Triple-integration-enhanced zeroing neural network (TIEZNN), quadratic programming, time-varying, gradient-based neural network (GNN).

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Section: Introductionmentioning

confidence: 99%

A High Order Integration Enhanced Zeroing Neural Network for Time-Varying Convex Quadratic Program Against Nonlinear Noise

Li,

Rong,

et al. 2024

IEEE Access

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“…Therefore, the researchers have proposed a corresponding novel activation function in order to achieve the desired experimental results. For example [38], proposed zero-tuned neural networks (ZTNN) to solve the Stein matrix equation based on several new activation functions. In [39], it is proposed that the PSAF-based ZNN model is applied to the secondary programming problem.…”

Section: Introductionmentioning

confidence: 99%

Towards non-linearly activated ZNN model for constrained manipulator trajectory tracking

Lan

Liu²

2023

Front. Phys.

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As a powerful method for time-varying problems solving, the zeroing neural network (ZNN) is widely applied in many practical applications that can be modeled as time-varying linear matrix equations (TVLME). Generally, existing ZNN models solve these TVLME problems in the ideal no noise situation without inequality constraints, but the TVLME with noises and inequality constraints are rarely considered. Therefore, a non-linear activation function is designed, and based on the non-linear activation function, a non-linearly activated ZNN (NAZNN) model is proposed for solving constrained TVLME (CTVLME) problems. The convergence and robustness of the proposed NAZNN model are verified theoretically, and simulation results further demonstrate the effectiveness and superiority of the NAZNN model in dealing with CTVLME and the constrained robot manipulator trajectory tracking problems. In addition, the wheeled robot trajectory tracking fault problems with physical constraints are also analyzed theoretically, and the proposed NAZNN model is also applied to the manipulator trajectory tracking fault problem, and the experimental results prove that the NAZNN model also deal with the manipulator trajectory tracking fault problem effectively.

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“…Besides, Xiao et al [28] present a unified ZNN, which simultaneously has both remarkable finite time convergence performance and inherent noises tolerance. In [29], a unified predefined-time convergent and robust ZNN model is put forward to obtain an exact solution in a specified finite time and accuracy can not be affected in the face of high amplitude perturbations. Moreover, by using special activation functions including the well-known sign-bi-power function in ZNN, Xiao et al [30] recommend a finite-time Zhang neural network to make the convergence performance have a remarkable improvement.…”

Section: Introductionmentioning

confidence: 99%

Solving Perturbed Time-Varying Linear Equation and Inequality Problem With Adaptive Enhanced and Noise Suppressing Zeroing Neural Network

Huang

et al. 2022

IEEE Access

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Solving time-varying linear equation and inequality (TVLEI) problem has attracted extensive attention in numerous scientific and engineered fields. In this article, it is basically considered that the commonly used dynamics neural network in the virtual environment is inevitably interfered with by the variable measurement noises while dealing with the TVLEI problem. An adaptive enhanced and noisesuppressing zeroing neural network (AENSZNN) model is proposed as an improved algorithm for solving the TVLEI problem. An adaptive scale factor based on the residual error norm is designed to make the proposed AENSZNN model converge to the theoretical solution faster. Furthermore, the momentum enhancement terms added to the model enables the AENSZNN model to effectively solve the TVLEI problem in real-time under the obstruction of different measurement noises. Besides, theoretical results and numerical experiments indicate that the AENSZNN model has advantages in convergence accuracy and robustness to noises compared with the existing algorithms. Note that, the proposed AENSZNN model is successfully exploited for the estimation of mobile object localization.

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A Unified Predefined-Time Convergent and Robust ZNN Model for Constrained Quadratic Programming

Cited by 41 publications

References 28 publications

A High Order Integration Enhanced Zeroing Neural Network for Time-Varying Convex Quadratic Program Against Nonlinear Noise

A High Order Integration Enhanced Zeroing Neural Network for Time-Varying Convex Quadratic Program Against Nonlinear Noise

Towards non-linearly activated ZNN model for constrained manipulator trajectory tracking

Solving Perturbed Time-Varying Linear Equation and Inequality Problem With Adaptive Enhanced and Noise Suppressing Zeroing Neural Network

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