Modified ZNN for Time-Varying Quadratic Programming With Inherent Tolerance to Noises and Its Application to Kinematic Redundancy Resolution of Robot Manipulators

For quadratic programming (QP), it is usually assumed that the solving process is free of measurement noises or that the denoising has been conducted before the computation. However, time is precious for time-varying QP (TVQP) in practice. Preprocessing for denoising may consume extra time, and consequently violates real-time requirements. Therefore, a model with inherent noise tolerance is urgently needed to solve TVQP problems in real time. In this paper, we make progress along this direction by proposing a modified Zhang neural network (MZNN) model for the solution of TVQP. The original Zhang neural network model and the gradient neural network model are employed for comparisons with the MZNN model. In addition, theoretical analyses show that, without measurement noise, the proposed MZNN model globally converges to the exact real-time solution of the TVQP problem in an exponential manner and that, in the presence of measurement noises, the proposed MZNN model has a satisfactory performance. Finally, two illustrative simulation examples as well as a physical experiment are provided and analyzed to substantiate the efficacy and superiority of the proposed MZNN model for TVQP problem solving.