Manipulability Optimization of a Rehabilitative Collaborative Robotic System

The use of collaborative robots (or cobots) in rehabilitation therapies is aimed at assisting and shortening the patient's recovery after neurological injuries. Cobots are inherently safe when interacting with humans and can be programmed in different working modalities based on the patient's needs and the level of the injury. This study presents a design optimization of a robotic system for upper limb rehabilitation based on the manipulability ellipsoid method. The human-robot system is modeled as a closed kinematic chain in which the human hand grasps a handle attached to the robot's end effector. The manipulability ellipsoids are determined for both the human and the robotic arm and compared by calculating an index that quantifies the alignment of the principal axes. The optimal position of the robot base with respect to the patient is identified by a first global optimization and by a further local refinement, seeking the best alignment of the manipulability ellipsoids in a series of points uniformly distributed within the shared workspace

Contact mechanics: contact area and interfacial separation from small contact to full contact

We present a molecular dynamics study of the contact between a rigid solid with a randomly rough surface and an elastic block with a flat surface. The numerical calculations mainly focus on the contact area and the interfacial separation from small contact (low load) to full contact (high load). For small load the contact area varies linearly with the load and the interfacial separation depends logarithmically on the load. For high load the contact area approaches the nominal contact area (i.e., complete contact), and the interfacial separation approaches zero. The numerical results have been compared with analytical theory and experimental results. They are in good agreement with each other. The present findings may be very important for soft solids, e.g., rubber, or for very smooth surfaces, where complete contact can be reached at moderate high loads without plastic deformation of the solids.Comment: 15 pages, 23 figure

Energy Consumption of a Battery Electric Vehicle with Infinitely Variable Transmission

Battery electric vehicles (BEVs) represent a possible sustainable solution for personal urban transportation. Presently, the most limiting characteristic of BEVs is their short range, mainly because of battery technology limitations. A proper design and control of the drivetrain, aimed at reducing the power losses and thus increasing BEV range, can contribute to make the electrification of urban transportation a convenient choice. This paper presents a simulation-based comparison of the energy efficiency performance of six drivetrain architectures for BEVs. Although many different drivetrain and transmission architectures have been proposed for BEVs, no literature was found regarding BEVs equipped with infinitely variable transmissions (IVTs). The analyzed drivetrain configurations are: single- (1G) and two-speed (2G) gear drives, half toroidal (HT) and full toroidal (FT) continuously variable transmissions (CVTs), and infinitely variable transmissions (IVTs) with two different types of internal power flow (IVT-I and IVT-II). An off-line procedure for determining the most efficient control action for each drivetrain configuration is proposed, which allows selecting the optimal speed ratio for each operating condition. The energy consumption of the BEVs is simulated along the UDC (Urban Driving Cycle) and Japanese 10-15 driving cycle, with a backward facing approach. In order to achieve the lowest energy consumption, a trade-off between high transmission efficiency and flexibility in terms of allowed speed ratios is required

Influence of the prediction model complexity on the performance of model predictive anti-jerk control for on-board electric powertrains

Anti-jerk controllers compensate for the torsional oscillations of automotive drivetrains, caused by swift variations of the traction torque. In the literature model predictive control (MPC) technology has been applied to anti-jerk control problems, by using a variety of prediction models. However, an analysis of the influence of the prediction model complexity on anti-jerk control performance is still missing. To cover the gap, this study proposes six anti-jerk MPC formulations, which are based on different prediction models and are fine-tuned through a unified optimization routine. Their performance is assessed over multiple tip-in and tip-out maneuvers by means of an objective indicator. Results show that: i) low number of prediction steps and short discretization time provide the best performance in the considered nominal tip-in test; ii) the consideration of the drivetrain backlash in the prediction model is beneficial in all test cases; iii) the inclusion of tire slip formulations makes the system more robust with respect to vehicle speed variations and enhances the vehicle behavior in tip-out tests; however, it deteriorates performance in the other scenarios; and iv) the inclusion of a simplified tire relaxation formulation does not bring any particular benefit

Glucagon-like peptide-1 and interleukin-6 interaction in response to physical exercise: An in-silico model in the framework of immunometabolism

Background and objective: Glucagon-like peptide 1 (GLP-1) is classically identified as an incretin hormone, secreted in response to nutrient ingestion and able to enhance glucose-stimulated insulin secretion. However, other stimuli, such as physical exercise, may enhance GLP-1 plasma levels, and this exercise-induced GLP-1 secretion is mediated by interleukin-6 (IL-6), a cytokine secreted by contracting skeletal muscle. The aim of the study is to propose a mathematical model of IL-6-induced GLP-1 secretion and kinetics in response to physical exercise of moderate intensity. Methods: The model includes the GLP-1 subsystem (with two pools: gut and plasma) and the IL-6 subsystem (again with two pools: skeletal muscle and plasma); it provides a parameter of possible clinical relevance representing the sensitivity of GLP-1 to IL-6 (k0). The model was validated on mean IL-6 and GLP-1 data derived from the scientific literature and on a total of 100 virtual subjects. Results: Model validation provided mean residuals between 0.0051 and 0.5493 pg⋅mL-1 for IL-6 (in view of concentration values ranging from 0.8405 to 3.9718 pg⋅mL-1) and between 0.0133 and 4.1540 pmol⋅L-1 for GLP-1 (in view of concentration values ranging from 0.9387 to 17.9714 pmol⋅L-1); a positive significant linear correlation (r = 0.85, p<0.001) was found between k0 and the ratio between areas under GLP-1 and IL-6 curve, over the virtual subjects. Conclusions: The model accurately captures IL-6-induced GLP-1 kinetics in response to physical exercise