Robust Model Predictive Control of Position and Orientation of Redundant Manipulators

Redundant manipulators are explored and utilized across a wide range of domains. Investigating effective trajectory tracking of redundant manipulators is pivotal for their practical applications. This paper constructs a model predictive control of position and orientation (MPCPO) scheme, which controls the position and orientation of the end-effector simultaneously while minimizing norms of the tracking error, joint velocity, and joint acceleration. Additionally, the MPCPO scheme addresses different levels of physical constraints while preserving the feasible region of decision variables. Moreover, an integration enhanced gradient-based recurrent neural network (IEGRNN) model is designed to solve the MPCPO scheme in noisy environments. Theoretical analyses demonstrate the convergence and robustness of the IEGRNN model. The effectiveness, accuracy, flexibility, and noise tolerance of the MPCPO scheme solved by the IEGRNN model are illustrated through simulations and experiments.