Wen Hongsong, Qu Youran, He Xing, Sun Shiying, Yang Hongjun, Li Tao, Zhou Feihu
Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, 400715, China.
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
ISA Trans. 2024 Mar;146:42-49. doi: 10.1016/j.isatra.2023.12.020. Epub 2023 Dec 18.
Zeroing neural network (ZNN) model, an important class of recurrent neural network, has been widely applied in the field of computation and optimization. In this paper, two ZNN models with predefined-time convergence are proposed for the time-varying quadratic programming (TVQP) problem. First, in the framework of the traditional ZNN model, the first-order predefined-time convergent ZNN (FPTZNN) model is proposed in combination with a predefined-time controller. Compared with the existing ZNN models, the proposed ZNN model is error vector combined with sliding mode control technique. Then, the FPTZNN model is further extended and the second-order predefined-time convergent ZNN (SPTZNN) model is developed. Combined with the Lyapunov method and the concept of predefined-time stability, it is shown that the proposed FPTZNN and SPTZNN models have the properties of predefined-time convergence, and their convergence time can be flexibly adjusted by predefined-time control parameters. Finally, the proposed FPTZNN and SPTZNN models are compared with the existing ZNN models for the TVQP problem in simulation experiment, and the simulation experiment results verify the effectiveness and superior performance of the proposed FPTZNN and SPTZNN models. In addition, the proposed FPTZNN model for robot motion planning problem is applied and successfully implemented to verify the practicality of the model.
归零神经网络（ZNN）模型作为递归神经网络的一个重要类别，已在计算与优化领域得到广泛应用。本文针对时变二次规划（TVQP）问题，提出了两种具有预定义时间收敛性的ZNN模型。首先，在传统ZNN模型框架下，结合预定义时间控制器，提出了一阶预定义时间收敛ZNN（FPTZNN）模型。与现有的ZNN模型相比，所提出的ZNN模型是将误差向量与滑模控制技术相结合。然后，对FPTZNN模型进行进一步扩展，开发出二阶预定义时间收敛ZNN（SPTZNN）模型。结合李雅普诺夫方法和预定义时间稳定性的概念，证明了所提出的FPTZNN和SPTZNN模型具有预定义时间收敛的性质，并且它们的收敛时间可以通过预定义时间控制参数灵活调整。最后，在仿真实验中，将所提出的FPTZNN和SPTZNN模型与现有的针对TVQP问题的ZNN模型进行比较，仿真实验结果验证了所提出的FPTZNN和SPTZNN模型的有效性和优越性能。此外，将所提出的用于机器人运动规划问题的FPTZNN模型进行应用并成功实现，以验证该模型的实用性。
