Surrogate model-based indirect adaptive control for real-time null-space reconfiguration in redundant manipulators

The growing demand for robotic systems in physical human–robot interaction (pHRI) has driven the need for advanced control strategies that ensure both task performance and human safety. This has led to more complex, sophisticated control strategies to overcome inherent parametric uncertainties and external disturbances encountered during pHRI tasks. Many recent strategies rely on learning-based methods, which, despite their excellent results, are often limited by computational demands rather than control design matters. These learning-based approaches require vast amounts of data and extensive training times. As an alternative, indirect adaptive control offers a more efficient solution by enabling real-time identification and compensation for changes in the robotic system during its operation. While indirect adaptive control has emerged as a promising approach for handling time-varying dynamics, its real-time implementation can be computationally intensive, especially with complex models or when iterative identification is used. To address this, the present work proposes a novel, lightweight surrogate model-based indirect adaptive control strategy that combines radial basis function regression with a metaheuristic optimization algorithm. The proposed strategy iteratively reconfigures the robot’s null space when external forces are detected along its kinematic chain, aiming to minimize them. At the same time, the parameters of a Cartesian impedance controller are dynamically updated to preserve the end-effector performance. The proposed strategy is validated using a Franka EMIKA Panda robot manipulator, addressing regulation and path-tracking control problems, as well as in a constrained task. The outcomes show the proposal’s capability to reconfigure the null-space while maintaining the end-effector position and orientation squared errors within the (Formula presented) and (Formula presented) ranges, respectively.