This article focuses on investigating the stability issue for recurrent neural networks (RNNs) with interval time-varying delays (TVDs) based on a flexible delay-dividing method with parameters, which are related to the delay derivative. First, an interval of delay is separated into parametric subintervals via the linear combination technique. Then, an establishment of Lyapunov–Krasovskii functional (LKF) is connected to the parameters, and a novel linear technology is suggested to dispose of integral terms in the derivatives of the constructed function. Finally, the validity and advantage of the inferred criteria are interpreted by the comparison of representative simulation examples.

A back propagation neural network prediction method is designed for the predictive control of joint angles in pneumatic artificial muscles in this paper, aimed at improving trajectory tracking accuracy for antagonistic joints. Firstly, a back propagation neural network model of pneumatic muscle antagonistic joints (PMAJ) is constructed based on the joint error angle and the error angular velocity. Secondly, the BPNN model is discretized to derive the back propagation neural network prediction model for antagonistic joints. Subsequently, a back propagation neural network-based prediction controller is designed to control the antagonistic joints, and the system’s stability is demonstrated. Finally, the efficacy of the proposed algorithm is demonstrated through simulations and physical experiments in comparison with other algorithms. The results show that the backpropagation neural network model predictive control (BP NNMPC) can significantly improve trajectory tracking accuracy. The key innovation of this work lies in integrating a data-driven back propagation neural network predictor into the model predictive control framework, thereby enhancing the accuracy of the nonlinear muscle dynamics model. Compared to conventional model predictive control, the overall error rate of the BP NNMPC designed in this paper is reduced by 40.5%.

This paper investigates the imperfect premise matching-based (IPMB) static output feedback (SOF) controller for T–S fuzzy systems with time-varying delays. Firstly, by employing integral inequality techniques, the membership-function-independent fuzzy SOF design methods in terms of LMIs are presented based on imperfect premise matching (IPM) strategy. The obtained stabilization conditions do not contain equality constraints, and the output matrices do not have rank constraints. Secondly, some suitable relaxation variables are employed, and more relaxed membership-function-dependent (MFD) stabilization conditions are obtained by considering the local boundary information of membership functions (MFs). Finally, two simulation examples are given to show the progressiveness of the proposed methods in this paper.