Performance analysis of LVI-based PDNN applied to real-time solution of time-varying quadratic programming

This paper illustrates theoretical analysis and simulative verification on the performance of the linear-variatlonal inequality based primal-dual neural network (LVI-PDNN), which was designed originally for static quadratic programming (QP) problem solving but Is now applied to time-varying QP problem solving. It Is theoretically proved that the LVI-PDNN for solving the time-varying QP problem subject to equality, Inequality and bound constraints simultaneously could only approximately approach the time-varying theoretical solution, Instead of converging exactly. In other words, the steady-state error of the realtime solution can not decrease to zero. In order to better evaluate the time-varying situation, we Investigate the upper bound of such an error and the global exponential convergence rate for the LVI-PDNN approaching Its loose error bound. Computer simulations further substantiate the performance analysis of the LVI-PDNN exploited for real-time solution of the time-varying QP problem.