An Enhanced Low-Computational-Complexity Predefined-Time Convergent Zeroing Neural Network for Constrained...

Time-varying quadratic programming (TVQP) problems can be regarded as a challenging issue in a wide range of engineering applications, frequently incorporating equality, inequality, and bound constraints. By integrating a nonlinear complementarity problem (NCP) formulation, zeroing neural networks (ZNNs) can be generalized to solve TVQP problems effectively with such a full set of constraints. However, three issues may limit its computational efficiency and practical applications: 1) the inherent formulation of NCP functions for handling equality/inequality and boundary constraints substantially expands dimensions of coefficient matrices/vectors and solution-space variables; 2) the conventional ZNN (CZNN) framework inevitably necessitates matrix inversion operations for real-time solutions; and 3) insufficient robustness against noise interference compromises solution accuracy in practical implementations. To overcome these issues and promote solution performance, this article develops an enhanced lower dimension NCP low-computational-complexity (LCC) ZNN (ELNCP-LCCZNN) model for solving TVQP problems with time-varying equality, inequality, and variable boundary constraints. An ELNCP function is designed to reduce the matrix/vector coefficients of the model, and the LCCZNN model is utilized to construct a new dynamic model that eliminates the necessity for matrix inversion during solution. Furthermore, a nonlinear activation function is involved to guarantee predefined-time convergence and strengthen robustness against noise. The theoretical properties of the proposed ELNCP-LCCZNN model are validated through numerical simulations and experiments on robotic manipulator kinematic control. The results corroborate the analysis and demonstrate improved computational efficiency, enhanced noise robustness, and practical implementability compared with existing approaches.