Search results for "Mach"
showing 10 items of 3360 documents
Use of second-order sliding mode observer for low-accuracy sensing in hydraulic machines
2018
Low-accuracy sensing is very common for the large hydraulic machines and does not allow for directly measuring the relative velocity which can be, otherwise, required for the control and monitoring purposes. This paper provides a case study of designing the second-order sliding mode observer based on the super-twisting robust exact differentiator. The nominal part of the system dynamics is derived from the simple available system measurements and incorporated into the observer structure. Parasitic by-effects, arising from the sensor sampling, quantization, and non-modeled distortions due to mechanical sensor interface, are shown as the main causes of hampering the final (steady-state) conve…
Adding Active Damping to Energy-Efficient Electro-Hydraulic Systems for Robotic Manipulators — Comparing Pressure and Acceleration Feedback
2020
The growing interest in energy efficiency, plug-and-play commissioning, and reduced maintenance for heavy-duty robotic manipulators directs towards self-contained, electro-hydraulic cylinders. These drives are characterized by extremely low damping that causes unwanted oscillations of the mechanical structure. Adding active damping to this class of energy-efficient architectures is essential. Hence, this paper bridges a literature gap by presenting a systematic comparison grounded on a model-based tuning of both pressure and acceleration feedback. It is shown that both approaches increase the system damping hugely and improve the performance of the linear system. Acceleration feedback shoul…
Machine Learning Approaches for Activity Recognition and/or Activity Prediction in Locomotion Assistive Devices—A Systematic Review
2020
Locomotion assistive devices equipped with a microprocessor can potentially automatically adapt their behavior when the user is transitioning from one locomotion mode to another. Many developments in the field have come from machine learning driven controllers on locomotion assistive devices that recognize/predict the current locomotion mode or the upcoming one. This review synthesizes the machine learning algorithms designed to recognize or to predict a locomotion mode in order to automatically adapt the behavior of a locomotion assistive device. A systematic review was conducted on the Web of Science and MEDLINE databases (as well as in the retrieved papers) to identify articles published…
Extreme minimal learning machine: Ridge regression with distance-based basis
2019
The extreme learning machine (ELM) and the minimal learning machine (MLM) are nonlinear and scalable machine learning techniques with a randomly generated basis. Both techniques start with a step in which a matrix of weights for the linear combination of the basis is recovered. In the MLM, the feature mapping in this step corresponds to distance calculations between the training data and a set of reference points, whereas in the ELM, a transformation using a radial or sigmoidal activation function is commonly used. Computation of the model output, for prediction or classification purposes, is straightforward with the ELM after the first step. In the original MLM, one needs to solve an addit…
Minimal-model for robust control design of large-scale hydraulic machines
2018
Hydraulic machines are in use where the large forces, at relatively low velocities, are required by varying loads and often hazardous and hard-to-reach environments, like e.g. offshore, mining, forestry, cargo logistics, and others industries. Cranes and excavators equipped with multiple hydraulic cylinders are typical examples for that. For design of the robust feedback controls of hydraulic cylinders, already installed into large-scale machines, there is a general lack of reliable dynamic models. Also the suitable and feasible identification techniques, especially in frequency domain, yield limited. This paper proposes a minimal-modeling approach for determining the most relevant open-lo…
Accelerated bearing life-Time test rig development for low speed data acquisition
2017
Condition monitoring plays an important role in rotating machinery to ensure reliability of the equipment, and to detect fault conditions at an early stage. Although health monitoring methodologies have been thoroughly developed for rotating machinery, low-speed conditions often pose a challenge due to the low signal-to-noise ratio. To this aim, sophisticated algorithms that reduce noise and highlight the bearing faults are necessary to accurately diagnose machines undergoing this condition. In the development phase, sensor data from a healthy and damaged bearing rotating at low-speed is required to verify the performance of such algorithms. A test rig for performing accelerated life-time t…
Hankelet-based action classification for motor intention recognition
2017
Powered lower-limb prostheses require a natural, and an easy-to-use, interface for communicating amputee’s motor intention in order to select the appropriate motor program in any given context, or simply to commute from active (powered) to passive mode of functioning. To be widely accepted, such an interface should not put additional cognitive load at the end-user, it should be reliable and minimally invasive. In this paper we present a one such interface based on a robust method for detecting and recognizing motor actions from a low-cost wearable sensor network mounted on a sound leg providing inertial (accelerometer, gyrometer and magnetometer) data in real-time. We assume that the sensor…
Real-time biomechanical modeling of the liver using Machine Learning models trained on Finite Element Method simulations
2020
[EN] The development of accurate real-time models of the biomechanical behavior of different organs and tissues still poses a challenge in the field of biomechanical engineering. In the case of the liver, specifically, such a model would constitute a great leap forward in the implementation of complex applications such as surgical simulators, computed-assisted surgery or guided tumor irradiation. In this work, a relatively novel approach for developing such a model is presented. It consists in the use of a machine learning algorithm, which provides real-time inference, trained on tens of thousands of simulations of the biomechanical behavior of the liver carried out by the finite element me…
Smart sensing and adaptive reasoning for enabling industrial robots with interactive human-robot capabilities in dynamic environments — a case study
2019
Traditional industry is seeing an increasing demand for more autonomous and flexible manufacturing in unstructured settings, a shift away from the fixed, isolated workspaces where robots perform predefined actions repetitively. This work presents a case study in which a robotic manipulator, namely a KUKA KR90 R3100, is provided with smart sensing capabilities such as vision and adaptive reasoning for real-time collision avoidance and online path planning in dynamically-changing environments. A machine vision module based on low-cost cameras and color detection in the hue, saturation, value (HSV) space is developed to make the robot aware of its changing environment. Therefore, this vision a…
Simulated 3-axis versus 5-axis Processing Toolpaths for Single Point Incremental Forming
2019
Abstract Accuracy and productivity of the parts manufactured by single point incremental forming (SPIF) are influenced by the proper selection of toolpaths. CAM software packages are often used for generating the toolpaths for the process. Literature survey have revealed that contour curves and spatial spirals are the most used toolpaths for SPIF. These toolpaths are generated using 3-axis approaches, meaning that the tool axis is maintained parallel to the vertical axis. The 3-axis approach was justified using 3-axis CNC milling machines as the main technological equipment for SPIF. However, nowadays, the wide spreading of both 5-axis CNC milling machines and industrial robots, with far su…