Stability Analysis of HJB-Based Optimal Control for Hybrid Motion/Force Control of Robot Manipulators Using RBF Neural Network

This paper presents intelligent optimal control approach based on Hamilton–Jacobi–Bellman (HJB) optimization for hybrid motion/force control problem of constrained robot manipulators. For designing of control scheme, first of all a state-space form of error dynamics is derived for quadratic optimization describing the constrained and unconstrained motion separately. Then, the explicit solution of HJB equation for optimal control is obtained by Riccati equation. The uncertainties of the system are compensated using radial basis function neural network (RBFNN) and adaptive compensator. Thus, the proposed control scheme is combination of the linear optimal control, neural network, and adaptive bound. The asymptotic stability of the system is demonstrated using Lyapunov stability analysis, and the simulated results are produced with two-link constrained manipulator.