Discrete-time double-integral zeroing neural dynamics for time-varying equality-constrained quadratic programming with application to manipulators

Neural dynamics remains a crucial field of interest for researchers, owing to its extensive applicability in addressing time-varying challenges across diverse domains. This study innovatively integrates discrete-time processing with neural dynamics principles, introducing a discrete-time double-integral zeroing neural dynamics (DTDIZND) method to address real-time-varying equality-constrained quadratic programming (ECQP) problems in diverse noisy environments. The DTDIZND model offers a robust solution. Theoretical analyses reveal that the DTDIZND model excels in real-time calculations with remarkable precision, effectively managing multiple noise sources. For comparative analysis, the existing discrete-time zeroing neural dynamics models are evaluated, addressing the same time-varying problems. Relative numerical experiments have been undertaken, further strengthening the evidence of the DTDIZND model’s efficiency and preponderance in managing diverse noise scenarios. In addition, the DTDIZND method is applied to robot manipulator motion planning, particularly in scenarios where diverse noise sources pose challenges. It is a promising tool for addressing time-varying challenges in various domains given its ability to handle real-time calculations with precision, coupled with its resilience against noise.