This work presents the design, development and validation of a partially powered knee prosthesis that enhances key features of energetically passive microprocessor-controlled knees (MPKs). We introduce a novel mechatronic architecture which combines a compact electro-hydrostatic actuation (EHA) unit with a controllable hydraulic valve. The design rationale is focused towards the support of swing-related activities and, as such, does not require large amounts of torque. Furthermore, the proposed solution retains the backdrivability properties of conventional MPKs and enhances the versatility through a highly integrated actuation unit (active and passive sides share the same motion transmission system), which may lead to improved walking capabilities. To this end, a prototype is designed, built and validated experimentally on a test bench to verify its active and passive functionalities. Results highlight comparable passive features with respect to state of the art MPKs with the possibility to inject active power at the joint to support swing related activities during level ground walking and stairs ascent tasks.
In this paper, we present a loop-shaping method that incorporates unstable poles for the magnetic-head positioning system in a hard disk drive (HDD). The proposed method involves introducing unstable poles instead of the conventional stable poles for the resonance filter used in loop shaping. In our approach, the resonant filter is designed to realize a circle that includes the coordinate $[-1,0 j]$ on the Nyquist plot of the open-loop characteristics in the control system. Validation results using the HDD benchmark problem demonstrate that our proposed methods effectively reject disturbances in the high-frequency range while maintaining robust performance.
An accurate modeling approach to predict the trajectory of a drillstring plays a critical role in drilling operation. Nonlinear Delay Differential Equations (DDEs) have been considered as an effective tool to serve the purpose. This paper introduces a novel data-driven approach to model the borehole propagation dynamics by incorporating nonlinear DDEs with Linear Complementarity Problem (LCP) using the Sparse Identification of Nonlinear Dynamics (SINDy) method. The developed model can predict borehole propagation without relying on physics-based information while retaining the same dynamics as those predicted by physics-based nonlinear DDEs. To assess the resilience of the proposed approach, we introduce noise into the dataset, demonstrating the robustness of the SINDy method. Additionally, a stability analysis of the data-driven DDEs offering insights into its reliability and potential applications.
This paper focuses on an automatic solution for the detection of manufacturing errors in the context of automatic wiring harness assembly. In the proposed setup, a robot grasp the wires and places them in specific assembly clips according to the wiring harness design. However, due to the deformability of cables, the process outcome is not completely predictable since sometimes the cables remains entangled in other parts of the assembly or do not fit properly into the clips. The proposed error detector verifies the correct insertion of each cables within the clip, considering that the number of cables and their dimension change along the different assembly stage. The proposed solution covers possible state-of-the-art network learning model that use point clouds as input source, while the network architecture is designed to offer precision and scalability in the context of a flexible and dynamic automation. The developed solution achieved a 96% precision on a dataset composed by various scenario. Therefore, despite being conceived for a robotic wiring harness manufacturing system, the proposed solution can be potentially applied as an online quality control system in manual wiring harness manufacturing.
When a crane lifts a payload, there are complex dynamic effects that occur as the payload transitions from being supported underneath by its original support surface to being supported above by the overhead rigging. For example, when payloads are lifted off the ground, the payload may unexpectedly swing sideways. This occurs when the payload is not directly beneath the hoist and the hoist cables are at an angle relative to vertical. The dynamics become even more complicated when a payload is held in a jig or structure that applies forces to the sides of the payload during lift, rather than simply resting on a ground surface. Such lift-off effects occur during the dangerous activity of tower crane disassembly when the various parts of the tower crane are removed one-by-one by a mobile telescopic boom crane. The assisting boom crane must attempt to maneuver its endpoint directly over top of the rigging connection point before it can remove each section without inducing complex lift-off dynamic effects. This paper presents a model to predict the lift-off dynamic effects when the assist crane is not positioned perfectly overhead. The model can be used to assess what range of offset angles are relatively innocuous and also to identify conditions that would lead to increased challenges in a lift-off process. The model can be integrated into a control system that performs autonomouscentering solutions to mitigate the detrimental effects of offcentered lifts during tower crane disassembly.
