Critic-observer-based decentralized force/position approximate optimal control for modular and reconfigurable manipulators with uncertain environmental constraints

Abstract A critic-observer decentralized force/position approximate optimal control method is presented to address the joint trajectory and contacted force tracking problem of modular reconfigurable manipulators (MRMs) with uncertain environmental constraints. The dynamic model MRM systems formulated as an integration subsystems via extensive state observer (ESO) associated effect interconnected coupling (IDC). radial basis function neural network (RBF-NN) developed deal IDC effects among independent subsystems. Based on adaptive programming (ADP) approach policy iteration (PI) algorithm, Hamilton–Jacobi–Bellman (HJB) equation approximately solved by establishing critic NN structure then approximated can be derived. closed-loop manipulator system proved asymptotic stable using Lyapunov theory. Finally, simulation results are provided demonstrate effectiveness advantages proposed method.