Optimally Controlling the Timing of Energy Transfer in Elastic Joints: Experimental Validation of the Bi-Stiffness Actuation Concept
Elastic actuation taps into elastic elements' energy storage for dynamic motions beyond rigid actuation. While Series Elastic Actuators (SEA) and Variable Stiffness Actuators (VSA) are highly sophisticated, they do not fully provide control over energy transfer timing. To overcome this problem on the basic system level, the Bi-Stiffness Actuation (BSA) concept was recently proposed. Theoretically, it allows for full link decoupling, while simultaneously being able to lock the spring in the drive train via a switch-and-hold mechanism. Thus, the user would be in full control of the potential energy storage and release timing. In this work, we introduce an initial proof-of-concept of Bi-Stiffness-Actuation in the form of a 1-DoF physical prototype, which is implemented using a modular testbed. We present a hybrid system model, as well as the mechatronic implementation of the actuator. We corroborate the feasibility of the concept by conducting a series of hardware experiments using an open-loop control signal obtained by trajectory optimization. Here, we compare the performance of the prototype with a comparable SEA implementation. We show that BSA outperforms SEA 1) in terms of maximum velocity at low final times and 2) in terms of the movement strategy itself: The clutch mechanism allows the BSA to generate consistent launch sequences while the SEA has to rely on lengthy and possibly dangerous oscillatory swing-up motions. Furthermore, we demonstrate that providing full control authority over the energy transfer timing and link decoupling allows the user to synchronously release both elastic joint and gravitational energy. This facilitates the optimal exploitation of elastic and gravitational potentials in a synergistic manner.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Differentiable Material Point Method for the Control of Deformable Objects
Controlling the deformation of flexible objects is challenging due to their non-linear dynamics and high-dimensional configuration space. This work presents a differentiable Material Point Method (MPM) simulator targeted…
Optimizing Wireless Discontinuous Reception via MAC Signaling Learning
We present a Reinforcement Learning (RL) approach to the problem of controlling the Discontinuous Reception (DRX) policy from a Base Transceiver Station (BTS) in a cellular network. We do so by means of optimally timing …
Reinforcement Learning (RL)An Energy-Efficient Transaction Model for the Blockchain-enabled Internet of Vehicles (IoV)
The blockchain is a safe, reliable and innovative mechanism for managing numerous vehicles seeking connectivity. However, following the principles of the blockchain, the number of transactions required to update ledgers …
ClusteringEfficient methods for determining folding free energies in single-molecule pulling experiments
The remarkable accuracy and versatility of single-molecule techniques make possible new measurements that are not feasible in bulk assays. Among these, the precise estimation of folding free energies using fluctuation th…
Quantum state-agnostic work extraction (almost) without dissipation
We investigate work extraction protocols designed to transfer the maximum possible energy to a battery using sequential access to $N$ copies of an unknown pure qubit state. The core challenge is designing interactions to…