Energy-Optimal, Direct-Phase Control of Brushless Motors for Robotic Drives

2020 ◽  
Author(s):  
Amin Ghorbanpour ◽  
Hanz Richter
Keyword(s):  
Energy Consumption ◽  
Motion Control ◽  
Optimization Problem ◽  
Control Method ◽  
Bldc Motor ◽  
Brushless Motors ◽  
Energy Regeneration ◽  
Quadratic Optimization Problem ◽  
Constrained Quadratic Optimization ◽  
Ultra Capacitor

Abstract In this work, simultaneous energy regeneration and motion control for robot manipulators with brushless direct current (BLDC) motors is considered. All joints of the robot are connected to regenerative drives powered from a single ultra-capacitor. A new voltage-based control method is developed to individually command each phase of the BLDC motor. Three independent regenerative drives are interconnected in a wye configuration, and each drives a phase of the motor. The objective is to determine the control inputs for each drive to minimize energy consumption from the ultra-capacitor for a given motion task. To this end, the problem is formulated as constrained quadratic optimization problem that gives the control inputs based on the desired torque generated by a virtual controller. An experimental evaluation is performed using a pendulum actuated by a BLDC motor. It is shown that the suggested control method can accomplish the motion task and it is capable of energy regeneration. The results show a reduction of about 40% in energy consumption for the condition of the study, relative to non-regenerative case.

A Novel Concept for Energy-Optimal, Independent-Phase Control of Brushless Motor Drivers

2021 ◽  
pp. 1-11
Author(s):  
Amin Ghorbanpour ◽  
Hanz Richter
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Robot Motion ◽  
Control Law ◽  
Outer Loop ◽  
Energy Regeneration ◽  
Brushless Motor ◽  
Control Scheme ◽  
Novel Concept ◽  
Constrained Quadratic Optimization

Abstract In this work, a new drive concept for brushless direct current (BLDC) motors is introduced. Energy regeneration is optimally managed with the aim of improving the energy efficiency of robot motion controls. The proposed scheme has three independent regenerative drives interconnected in a wye configuration. An augmented model of the robot, joint mechanisms, and BLDC motors is formed, and then a voltage-based control scheme is developed. The control law is obtained by specifying an outer-loop torque controller followed by minimization of power consumption via online constrained quadratic optimization. An experiment is conducted to assess the performance of the proposed concept against an off-the-shelf driver. It is shown that, in terms of energy regeneration and consumption, the developed driver has better performance. Furthermore, the proposed concept showed a reduction of 15% energy consumption for the conditions of the study.

scholarly journals Efficiency Analysis and Improvement of an Intelligent Transportation System for the Application in Greenhouse

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 946 ◽  
Author(s):  
Tianfan Zhang ◽  
Weiwen Zhou ◽  
Fei Meng ◽  
Zhe Li
Keyword(s):  
Energy Consumption ◽  
Motion Control ◽  
Control Method ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Visual Navigation ◽  
Operating Efficiency ◽  
Load Factor ◽  
Mechatronic Systems ◽  
Related Factors

In view of the future lack of human resources due to the aging of the population, the automatic, Intelligent Mechatronic Systems (IMSs) and Intelligent Transportation Systems (ITSs) have broad application prospects. However, complex application scenarios and limited open design resources make designing highly efficient ITS systems still a challenging task. In this paper, the optimal load factor solving solution is established. By converting the three user requirements including working distance, time and load into load-related factors, the optimal result can be obtained among system complexity, efficiency and system energy consumption. A specialized visual navigation and motion control system has been proposed to simplify the path planning, navigation and motion control processes and to be accurately calculated in advance, thereby further improving the efficiency of the ITS system. The validity of the efficiency calculation formula and navigation control method proposed in this paper is verified. Under optimal conditions, the actual working mileage is expected to be 99.7%, and the energy consumption is 83.5% of the expected value, which provides sufficient redundancy for the system. In addition, the individual ITS reaches the rated operating efficiency of 95.86%; in other words, one ITS has twice the ability of a single worker. This proves the accuracy and efficiency of the designed ITS system.

scholarly journals Discussion of ``V-Matrix Method of Solving Statistical Inference Problems" by Vlamidir Vapnik and Rauf Izmailov

2018 ◽  
Author(s):  
Niharika Gauraha
Keyword(s):  
Conditional Probability ◽  
Optimization Problem ◽  
Probability Function ◽  
Future Research ◽  
Short Discussion ◽  
Quadratic Optimization Problem ◽  
Inference Problems ◽  
Consistent Solution ◽  
Constrained Quadratic Optimization ◽  
Conditional Probability Function

We would like to begin by stating that we have not fully understood the formulation of V-matrix conceptually. However, We are fascinated by the idea of estimation of conditional probability function without assuming any probabilistic model. In this short discussion, we would like to present that the proposed constrained quadratic optimization problem for conditional probability estimation using v-matrix based method may not have a consistent solution always. We are sure that the paper will stimulate a deeper exploration of V-matrix based methods for inference in high-dimensional problems in future research.

scholarly journals Characterization of Robust Solution Quadratically Constrained Quadratic Optimization Problem Subjected to Data Uncertainty

2021 ◽  
pp. 1-23
Author(s):  
Moussa BARRO ◽  
Satafa SANOGO ◽  
Mohamed ZONGO ◽  
Sado TRAORÉ
Keyword(s):  
Robust Optimization ◽  
Optimization Problem ◽  
Real Life ◽  
Quadratic Optimization ◽  
Data Uncertainty ◽  
Robust Solution ◽  
Quadratic Optimization Problem ◽  
Robust Duality ◽  
Constrained Quadratic Optimization ◽  
Quadratically Constrained

Robust Optimization (RO) arises in two stages of optimization, first level for maximizing over the uncertain data and second level for minimizing over the feasible set. It is the most suitable mathematical optimization procedure to solve real-life problem models. In the present work, we characterize robust solutions for both homogeneous and non-homogeneous quadratically constrained quadratic optimization problem where constraint function and cost function are uncertain. Moreover, we discuss about optimistic dual and strong robust duality of the considered uncertain quadratic optimization problem. Finally, we complete this work with an example to illustrate our solution method. Mathematics Subject Classification: (2010) 90C20 - 90C26 - 90C46-90C47 Keywords: Robust Optimization, Data Uncertainty, Quadratic Optimization Strong Duality, Robust Solution, DPJ-Convex.

Two-Stage Feature Selection with Unsupervised Second Stage

2018 ◽  
Vol 27 (07) ◽  
pp. 1860014
Author(s):  
Ke Xu ◽  
Crystal Maung ◽  
Hiromasa Arai ◽  
Haim Schweitzer
Keyword(s):  
Feature Selection ◽  
Optimization Problem ◽  
Complexity Analysis ◽  
Feature Selection Problem ◽  
Quadratic Optimization Problem ◽  
Second Stage ◽  
Dimensionality Reduction Technique ◽  
Constrained Quadratic Optimization ◽  
Two Stages ◽  
Simple Filter

Feature selection is a common dimensionality reduction technique of fundamental importance in big data. A common approach for reducing the running time of feature selection is to perform it in two stages. In the first stage a fast and simple filter is applied to select good candidates. The number of candidates is further reduced in the second stage by an accurate algorithm that may run significantly slower. There are two main variants of feature selection: unsupervised and supervised. In the supervised variant features are selected for predicting labels, while the unsupervised variant does not use labels at all. We describe a general framework that can use an arbitrary off-the-shelf unsupervised algorithm for the second stage. The algorithm is applied to the selection obtained in the first stage weighted appropriately. Our main technical result is a method for calculating weights for the columns that need to be selected in the second stage. We show that these weights can be computed as the solution to a constrained quadratic optimization problem. The solution is deterministic, and improves on previously published studies that use probabilistic ideas to compute similar weights. To the best of our knowledge our approach is the first technique for converting a supervised feature selection problem into an unsupervised problem. Complexity analysis shows that the proposed technique is very fast, can be implemented in a single pass over the data, and can take advantage of data sparsity. Experimental results show that the accuracy of the proposed method is comparable to that of much slower techniques.

Nonlinear model predictive control of spacecraft relative motion

2018 ◽  
Vol 233 (11) ◽  
pp. 3906-3919 ◽  
Author(s):  
Piotr A Felisiak ◽  
Krzysztof S Sibilski ◽  
Kaiyu Qin ◽  
Gun Li ◽  
Wiesław A Wróblewski
Keyword(s):  
Model Predictive Control ◽  
Motion Control ◽  
Predictive Control ◽  
Relative Motion ◽  
Control Algorithm ◽  
Optimization Problem ◽  
Control Method ◽  
Nonlinear Model Predictive Control ◽  
Model Predictive Control Algorithm ◽  
Control Trajectory

This investigation deals with the problem of spacecraft relative motion control, which is typically associated with the spacecraft rendezvous and proximity maneuvers. Relative position and linear velocity are considered. A distinguishing attribute of the presented approach is consideration of definitely larger relative distance between the satellites than it is commonly addressed in the literature. The presented control method is applicable in the case where the chief satellite moves in a known, highly elliptical orbit. A quasi-optimal control is found by a model predictive control algorithm, where the nonlinear optimization problem is reduced to quadratic optimization by preliminary estimation of the future control trajectory. Significance of the method has been verified using a computer simulation.

scholarly journals Optimization Issues of a Hammer Mill Working Process Using Statistical Modelling

2021 ◽  
Vol 13 (2) ◽  
pp. 973
Author(s):  
Gigel Paraschiv ◽  
Georgiana Moiceanu ◽  
Gheorghe Voicu ◽  
Mihai Chitoiu ◽  
Petru Cardei ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Optimization Problem ◽  
Specific Energy Consumption ◽  
Statistical Modelling ◽  
Hammer Mill ◽  
Objective Functions ◽  
Working Process ◽  
Rotor Spinning ◽  
Milled Material ◽  
Different Types

Our paper presents the hammer mill working process optimization problem destined for milling energetic biomass (MiscanthusGiganteus and Salix Viminalis). For the study, functional and constructive parameters of the hammer mill were taken into consideration in order to reduce the specific energy consumption. The energy consumption dependency on the mill rotor spinning frequency and on the sieve orifices in use, as well as on the material feeding flow, in correlation with the vegetal biomass milling degree was the focus of the analysis. For obtaining this the hammer mill was successively equipped with 4 different types of hammers that grind the energetic biomass, which had a certain humidity content and an initial degree of reduction ratio of the material. In order to start the optimization process of hammer mill working process, 12 parameters were defined. The objective functions which minimize hammer mill energy consumption and maximize the milled material percentage with a certain specific granulation were established. The results obtained can serve as the basis for choosing the optimal working, constructive, and functional parameters of hammer mills in this field, and for a better design of future hammer mills.

scholarly journals Efficient Placement of Service Function Chains in Cloud Computing Environments

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 323
Author(s):  
Marwa A. Abdelaal ◽  
Gamal A. Ebrahim ◽  
Wagdy R. Anis
Keyword(s):  
Energy Consumption ◽  
Load Balancing ◽  
Response Time ◽  
Optimization Problem ◽  
Service Providers ◽  
Virtual Network ◽  
Placement Problem ◽  
Service Function ◽  
Network Functions ◽  
Bandwidth Consumption

The widespread adoption of network function virtualization (NFV) leads to providing network services through a chain of virtual network functions (VNFs). This architecture is called service function chain (SFC), which can be hosted on top of commodity servers and switches located at the cloud. Meanwhile, software-defined networking (SDN) can be utilized to manage VNFs to handle traffic flows through SFC. One of the most critical issues that needs to be addressed in NFV is VNF placement that optimizes physical link bandwidth consumption. Moreover, deploying SFCs enables service providers to consider different goals, such as minimizing the overall cost and service response time. In this paper, a novel approach for the VNF placement problem for SFCs, called virtual network functions and their replica placement (VNFRP), is introduced. It tries to achieve load balancing over the core links while considering multiple resource constraints. Hence, the VNF placement problem is first formulated as an integer linear programming (ILP) optimization problem, aiming to minimize link bandwidth consumption, energy consumption, and SFC placement cost. Then, a heuristic algorithm is proposed to find a near-optimal solution for this optimization problem. Simulation studies are conducted to evaluate the performance of the proposed approach. The simulation results show that VNFRP can significantly improve load balancing by 80% when the number of replicas is increased. Additionally, VNFRP provides more than a 54% reduction in network energy consumption. Furthermore, it can efficiently reduce the SFC placement cost by more than 67%. Moreover, with the advantages of a fast response time and rapid convergence, VNFRP can be considered as a scalable solution for large networking environments.

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