Robust Control of a System with a Pneumatic Spring

Abstract: Recently, series elasticity has been realized using pneumatics in human-robot interaction systems. Pneumatic circuits provide not only a flexible power transmission, but also the elastic element in a series elastic actuator (SEA). Pneumatic series elastic systems involve more than twice the number of parameters that influence system behaviors in comparison with rigid robotic systems. In this study, a position controller is proposed for pneumatic SEA systems that eliminates the need of identifying a system model by employing the time delay estimation (TDE) technique. The TDE technique is effective in compensating for system dynamics and all uncertainties involved in system behaviors without imposing computation load. TDE error is cancelled out through an adaptive way, which improves control performance and leads to asymptotic stability. A simulation study demonstrates the robustness of the proposed controllers against uncertainties imposed on the motor system as well as uncertainties on the end-effector. It shows the efficacy of the adaptive compensation for TDE error

Study on operational space control of a redundant robot with un-actuated joints: experiments under actuation failure scenarios

This paper analyzes operational space dynamics for redundant robots with un-actuated joints and reveals their highly nonlinear dynamic impacts on operational space control (OSC) tasks. Unlike conventional OSC approaches that partly address the under-actuated system by introducing rigid grasping or contact constraints, we deal with the problem even without such physical constraints which have been overlooked, yet it includes a wide range of applications such as free-floating robots and manipulators with passive joints or unwanted actuation failure. In addition, as an intuitive application example of the drawn result, an OSC is formulated as an optimization problem to alleviate the dynamics disturbance stemmed from the un-actuated joints and to satisfy other inequality constraints. The dynamic analysis and the proposed control method are verified by a number of numerical simulations as well as physical experiments with a 7-degrees-of-freedom robotic arm. In particular, we consider joint actuation failure scenarios that can be occurred at certain joints of a torque-controlled robot and practical case studies are performed with an actual redundant robot arm

A Generalized Index for Fault-tolerant Control in Operational Space under Free-swinging Actuation Failure

Actuation failure and fault-tolerant control under the actuation failure scenario have drawn more attention in accordance with the recent increasing demand for reliable robot control applications such for long-term and remote operation. The emergence of control torque loss, i.e., the free-swinging failure, is particularly challenging when the robot performs dynamic operational space tasks due to complexities stemming from redundancies in the kinematic structure as well as a dynamical disturbance in the under-actuated multi-body system. To reinforce robustness and accuracy of task-space control under the failure condition, this letter proposes a performance index, named generalized failure-susceptibility (GFS), which is formulated to render thorough dynamic and kinematic effects caused by the un-actuated joints. The GFS index is then exploited with the hierarchical task controller, where self-motion is controlled to minimize the index in real-time. Several experiments with a seven-degrees-of-freedom torque-controlled robot verify that the proposed control strategy with the GFS index effectively improves fault tolerance against anticipating actuation failure

Analysis of task decoupling characteristics of null space projector with uncertainty from modelling error

Methods for designing a multitask controller for redundant robots are often implemented by a null space projector. Although null-space projection-based controllers can achieve the task decoupling performance among prioritized tasks, it is vulnerable to uncertainty due to modeling errors in practice. Accordingly, this paper provides an analytical proof of the task coupling effect caused by the uncertainty and verifies it through a simulation

Learning-based adaption of robotic friction models

In the Fourth Industrial Revolution, wherein artificial intelligence and the automation of machines occupy a central role, the deployment of robots is indispensable. However, the manufacturing process using robots, especially in collaboration with humans, is highly intricate. In particular, modeling the friction torque in robotic joints is a longstanding problem due to the lack of a good mathematical description. This motivates the usage of data-driven methods in recent works. However, model-based and data-driven models often exhibit limitations in their ability to generalize beyond the specific dynamics they were trained on, as we demonstrate in this paper. To address this challenge, we introduce a novel approach based on residual learning, which aims to adapt an existing friction model to new dynamics using as little data as possible. We validate our approach by training a base neural network on a symmetric friction data set to learn an accurate relation between the velocity and the friction torque. Subsequently, to adapt to more complex asymmetric settings, we train a second network on a small dataset, focusing on predicting the residual of the initial network's output. By combining the output of both networks in a suitable manner, our proposed estimator outperforms the conventional model-based approach and the base neural network significantly. Furthermore, we evaluate our method on trajectories involving external loads and still observe a substantial improvement, approximately 60-70\%, over the conventional approach. Our method does not rely on data with external load during training, eliminating the need for external torque sensors. This demonstrates the generalization capability of our approach, even with a small amount of data-only 43 seconds of a robot movement-enabling adaptation to diverse scenarios based on prior knowledge about friction in different settings

Online Learning of Centroidal Angular Momentum Towards Enhancing DCM-Based Locomotion

Gait generation frameworks for humanoid robots typically assume a constant centroidal angular momentum (CAM) throughout the walking cycle, which induces undesirable contact torques in the feet and results in performance degradation. In this work, we present a novel algorithm to learn the CAM online and include the obtained knowledge within the closed-form solutions of the Divergent Component of Motion (DCM) locomotion framework. To ensure a reduction of the contact torques at the desired center of pressure position, a CAM trajectory is generated and explicitly tracked by a whole-body controller. Experiments with the humanoid robot TORO demonstrate that the proposed method substantially increases the maximum step length and walking speed during locomotion