Feature Tracking Control of Input-constrained VS Manipulators through ADP

Abstract

An adaptive dynamic programming (ADP) technique is investigated to figure out the problem of image feature tracking control of constrained visual servoing (VS) manipulators system. The complete camera-manipulator dynamic model can be established through the mapping relationship between image feature and manipulator position. The control inputs in the VS manipulators system are taken as a constrained case, and the performance index function is considered to cooperate with sliding mode function. On the basis of ADP technique, a critic neural network (NN) is constructed to derive the Hamilton-Jacobi-Bellman (HJB) equation. Furthermore, the near-optimal control policy is obtained. The Lyapunov theory illustrates that the feature tracking of input-constrained VS manipulators system is ultimately uniformly bounded (UUB). Numerical simulation examples are given to certify the validity of the developed approach.