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. Note to Practitioners—This paper is motivated by the goal of enhancing trajectory tracking performance in redundant manipulators. Many schemes only consider controlling the position of the end-effector of a redundant manipulator while neglecting orientation. Besides, the commonly used schemes address multiple levels of physical constraints by using conversion techniques, which diminishes the feasible region of decision variables. To overcome these weaknesses, a model predictive control of position and orientation (MPCPO) scheme is constructed in this paper, which controls the position and orientation of the end-effector simultaneously. Additionally, the MPCPO scheme addresses different levels of physical constraints while preserving the feasible region. Moreover, most existing MPC solvers do not account for the influence of noises, which reduces their reliability in real-world scenarios. To remedy this, an integration enhanced gradient-based recurrent neural network (IEGRNN) model is designed to solve the MPCPO scheme, with theoretical guarantees on its convergence and robustness in noisy environments. Simulations and experiments validate the effectiveness of the proposed method. Its superiority is further demonstrated through comparisons with other schemes.