A Lyapunov-based model predictive control strategy with a disturbances compensation mechanism for dual-arm manipulators

Abstract

This paper proposes a Lyapunov-based nonlinear model predictive control (LMPC) - based on adaptive Lyapunov to solve existing problems in nonlinear dual-arm systems such as system constraints and unknown external disturbances. In practice, the constraints tend to adversely affect the system’s performance and stability. The nonlinear model predictive control NMPC is considered a promising candidate for handling system constraints while enhancing the robustness of the system. However, the rigour of the modeling procedure has a significant influence on the execution of the NMPC, system convergence cannot be assured in the face of modeling uncertainty. To solve this problem, the proposed controller takes into account external disturbances and unidentified parameters by using an adaptive mechanism constructed via the Radial Basis Function Neural Network (RBFNN). Furthermore, the dominant problem of the NMPC algorithm is the system stability which is considered by the Lyapunov theory backbones by a nonlinear Sliding Mode Control (SMC). The numerical simulations are carried out based on a pseudo-physical model to show the efficiency of the proposed control method.