A memristor-based hybrid analog-digital computing platform for mobile robotics

2020 ◽  
Vol 5 (47) ◽  
pp. eabb6938 ◽  
Author(s):  
Buyun Chen ◽  
Hao Yang ◽  
Boxiang Song ◽  
Deming Meng ◽  
Xiaodong Yan ◽  
...  
Keyword(s):  
Energy Efficient ◽  
Mobile Robotics ◽  
Optimization Method ◽  
Control Algorithms ◽  
Digital Platforms ◽  
Computing Platform ◽  
Model Free ◽  
Order Of Magnitude ◽  
Computing Platforms ◽  
Conductance States

Algorithms for mobile robotic systems are generally implemented on purely digital computing platforms. Developing alternative computational platforms may lead to more energy-efficient and responsive mobile robotics. Here, we report a hybrid analog-digital computing platform enabled by memristors on a mobile inverted pendulum robot. Our mobile robotic system can tune the conductance states of memristors adaptively using a model-free optimization method to achieve optimal control performance. We implement sensor fusion and the motion control algorithms on our hybrid analog-digital computing platform and demonstrate more than one order of magnitude enhancement of speed and energy efficiency over traditional digital platforms.

Energy-Efficient Wind Mill Control Algorithms

2017 ◽  
pp. 31-35
Author(s):  
Oleg V. KRYUKOV ◽  
◽  
Artem V. SEREBRYAKOV ◽  
Keyword(s):  
Energy Efficient ◽  
Control Algorithms ◽  
Mill Control

scholarly journals Strategies to Implement Edge Computing in a P2P Pervasive Grid

2018 ◽  
Vol 11 (1) ◽  
pp. 1-15
Author(s):  
Luiz Angelo Steffenel ◽  
Manuele Kirsch Pinheiro ◽  
Lucas Vaz Peres ◽  
Damaris Kirsch Pinheiro
Keyword(s):  
Big Data ◽  
Context Awareness ◽  
Edge Computing ◽  
Computing Platform ◽  
Geophysical Application ◽  
Two Factors ◽  
Computing Platforms ◽  
Distributed Computing Platform ◽  
Big Data Computing ◽  
Analyze Data

The exponential dissemination of proximity computing devices (smartphones, tablets, nanocomputers, etc.) raises important questions on how to transmit, store and analyze data in networks integrating those devices. New approaches like edge computing aim at delegating part of the work to devices in the “edge” of the network. In this article, the focus is on the use of pervasive grids to implement edge computing and leverage such challenges, especially the strategies to ensure data proximity and context awareness, two factors that impact the performance of big data analyses in distributed systems. This article discusses the limitations of traditional big data computing platforms and introduces the principles and challenges to implement edge computing over pervasive grids. Finally, using CloudFIT, a distributed computing platform, the authors illustrate the deployment of a real geophysical application on a pervasive network.

scholarly journals REINFORCEMENT LEARNING BASED ANTI-COLLISION ALGORITHM FOR RFID SYSTEMS

2019 ◽  
pp. 155-168
Author(s):  
Murukesan Loganathan ◽  
Thennarasan Sabapathy ◽  
Mohamed Elobaid Elshaikh ◽  
Mohamed Nasrun Osman ◽  
Rosemizi Abd Rahim ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Radio Frequency Identification ◽  
Energy Efficient ◽  
Current Standard ◽  
Tag Identification ◽  
Rfid Systems ◽  
Order Of Magnitude ◽  
Control Message ◽  
Frequency Identification ◽  
Better Than

Efficient collision arbitration protocol facilitates fast tag identification in radio frequency identification (RFID) systems. EPCGlobal-Class1-Generation2 (EPC-C1G2) protocol is the current standard for collision arbitration in commercial RFID systems. However, the main drawback of this protocol is that it requires excessive message exchanges between tags and the reader for its operation. This wastes energy of the already resource-constrained RFID readers. Hence, in this work, reinforcement learning based anti-collision protocol (RL-DFSA) is proposed to address the energy efficient collision arbitration problem in the RFID system. The proposed algorithm continuously learns and adapts to the changes in the environment by devising an optimal policy. The proposed RL-DFSA was evaluated through extensive simulations and compared with the variants of EPC-C1G2 algorithms that are currently being used in the commercial readers. Based on the results, it is concluded that RL-DFSA performs equal or better than EPC-C1G2 protocol in delay, throughput and time system efficiency when simulated for sparse and dense environments while requiring one order of magnitude lesser control message exchanges between the reader and the tags.

scholarly journals Energy Efficient Opportunistic Sensing Management in Fog Cloud Environment

2021 ◽  
Vol 10 (10) ◽  
pp. 20-26
Author(s):  
Anju Gupta ◽  
R K Bathla
Keyword(s):  
Energy Efficient ◽  
Cost Effective ◽  
Sensory Data ◽  
Computing Platform ◽  
Opportunistic Sensing ◽  
Battery Power ◽  
Wide Range ◽  
Sensor Cloud ◽  
Set Up ◽  
Potential Challenge

With so many people now wearing mobile devices with sensors (such as smartphones), utilizing the immense capabilities of these business mobility goods has become a prospective skill to significant behavioural and ecological sensors. A potential challenge for pervasive context assessment is opportunistic sensing, has been effectively used to a wide range of applications. The sensor cloud combines cloud technology with a wireless sensor, resulting in a scalable and cost-effective computing platform for real-time applications. Because the sensor's battery power is limited and the data centre’s servers consume a significant amount of energy to supply storage, a sensor cloud must be energy efficient. This study provides a Fog-based semantic for enabling these kinds of technologies quickly and successfully. The suggested structure is comprised of fundamental algorithms to help set up and coordinate the fog sensing jobs. It creates effective multihop routes for coordinating relevant devices and transporting acquired sensory data to fog sinks. It was claimed that energy-efficient sensor cloud approaches were categorized into different groups and that each technology was examined using numerous characteristics. The outcomes of a series of thorough test simulation in NS3 to define the practicality of the created console, as well as the proportion of each parameter utilized for each technology, are computed.

Micro-Testing While Drilling for Rate of Penetration Optimization: Experiments and Simulations

2021 ◽  
pp. 1-49
Author(s):  
Magnus Nystad ◽  
Bernt Aadnoy ◽  
Alexey Pavlov
Keyword(s):  
Optimization Method ◽  
Extremum Seeking ◽  
Constraint Handling ◽  
Drilling Process ◽  
Test Results ◽  
Rate Of Penetration ◽  
Drilling Rig ◽  
Model Free ◽  
Drilling Parameters ◽  
Weight On Bit

Abstract The Rate of Penetration (ROP) is one of the key parameters related to the efficiency of the drilling process. Within the confines of operational limits, the drilling parameters affecting the ROP should be optimized to drill more efficiently and safely, to reduce the overall cost of constructing the well. In this study, a data-driven optimization method called Extremum Seeking (ES) is employed to automatically find and maintain the optimal Weight on Bit (WOB) which maximizes the ROP. The ES algorithm is a model-free method which gathers information about the current downhole conditions by automatically performing small tests with the WOB and executing optimization actions based on the test results. In this paper, this optimization method is augmented with a combination of a predictive and a reactive constraint handling technique to adhere to operational limitations. These methods of constraint handling within ES application to drilling are demonstrated for a maximal limit imposed on the surface torque, but the methods are generic and can be applied on various drilling parameters. The proposed optimization scheme has been tested with experiments on a downscaled drilling rig and simulations on a high-fidelity drilling simulator of a full-scale drilling operation. The experiments and simulations show the method's ability to steer the system to the optimum and to handle constraints and noisy data, resulting in safe and efficient drilling at high ROP.

Nonlinear Model Reduction for Automotive System Descriptions

2001 ◽  
Author(s):  
Zeyu Liu ◽  
John Wagner
Keyword(s):  
Dynamic System ◽  
Model Reduction ◽  
Nonlinear Model ◽  
Order System ◽  
Control Algorithms ◽  
Model Based Control ◽  
Reduction Strategy ◽  
Nonlinear Model Reduction ◽  
Model Based ◽  
Model Free

Abstract The mathematical modeling of dynamic systems is an important task in the design, analysis, and implementation of advanced automotive control systems. Although most vehicle control algorithms tend to use model-free calibration architectures, a need exists to migrate to model-based control algorithms which offer greater operating performance. However, in many instances, the analytical descriptions are too complex for real-time powertrain and chassis model-based control algorithms. Therefore, model reduction strategies may be applied to transform the original model into a simplified lower-order form while preserving the dynamic characteristics of the original high-order system. In this paper, an empirical gramian balanced nonlinear model reduction strategy is examined for the simplification process of dynamic system descriptions. The empirical gramians may be computed using either experimental or simulation data. These gramians are then balanced and unimportant system dynamics truncated. For comparison purposes, a Taylor Series linearization will also be introduced to linearize the original nonlinear system about an equilibrium operating point and then a balanced realization linear reduction strategy will be applied. To demonstrate the functionality of each model reduction strategy, two nonlinear dynamic system models are investigated and respective transient performances compared.

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