Intelligent optimal hybrid motion/force control of constrained robot manipulator

This paper presents a neural network-based optimal control approach for hybrid motion/force control problem of robot manipulators in the presence of structured and unstructured uncertainties. The quadratic optimisation with sliding mode control and neural network is utilised to propose an optimal hybrid motion/force control of robotic manipulators. Firstly, the dynamics of robot manipulator is reduced into state-space form describing the constrained and unconstrained motion separately. Then the optimal hybrid motion/force control scheme is derived utilising the optimisation of Hamilton Jacobi Hellman (HJB) equation and sliding mode control approach. The structured and unstructured uncertainties of the system are compensated using radial basis function neural network and adaptive compensator. The radial basis function neural network approximates the unknown dynamics and adaptive compensator is used to estimate the bounds on neural network approximation error and the unstructured uncertainties of the system. The neural networks are trained in online manner using weight update algorithms derived with Lyapunov approach to guarantee the tracking stability and error convergence with prescribed quadratic performance index. Finally, the proposed control approach is verified through simulation results with two link constrained manipulator in a comparative manner.