A penalized version of the local minimization scheme for rate-independent systems

2021 ◽  
Vol 115 ◽  
pp. 106954
Author(s):  
Dorothee Knees ◽  
Viktor Shcherbakov
Keyword(s):  
Minimization Scheme

Energy Consumption Minimization Scheme for NOMA-Based Mobile Edge Computation Networks Underlaying UAV

2021 ◽  
pp. 1-10
Author(s):  
Ishan Budhiraja ◽  
Neeraj Kumar ◽  
Sudhanshu Tyagi ◽  
Sudeep Tanwar
Keyword(s):  
Energy Consumption ◽  
Minimization Scheme

DMRG Approach to Fast Linear Algebra in the TT-Format

2011 ◽  
Vol 11 (3) ◽  
pp. 382-393 ◽  
Author(s):  
Ivan Oseledets
Keyword(s):  
Linear Systems ◽  
Linear Algebra ◽  
Direct Method ◽  
Fast Algorithms ◽  
Approximate Solutions ◽  
Vector Product ◽  
The Matrix ◽  
Minimization Scheme

AbstractIn this paper, the concept of the DMRG minimization scheme is extended to several important operations in the TT-format, like the matrix-by-vector product and the conversion from the canonical format to the TT-format. Fast algorithms are implemented and a stabilization scheme based on randomization is proposed. The comparison with the direct method is performed on a sequence of matrices and vectors coming as approximate solutions of linear systems in the TT-format. A generated example is provided to show that randomization is really needed in some cases. The matrices and vectors used are available from the author or at http://spring.inm.ras.ru/osel

Pseudoinverse-type bi-criteria minimization scheme for redundancy resolution of robot manipulators

Robotica ◽  
2014 ◽  
Vol 33 (10) ◽  
pp. 2100-2113 ◽  
Author(s):  
Bolin Liao ◽  
Weijun Liu
Keyword(s):  
Robot Manipulator ◽  
Robot Manipulators ◽  
Weighting Factor ◽  
Redundancy Resolution ◽  
Type Solution ◽  
Revolute Joints ◽  
Joint Velocity ◽  
Joint Acceleration ◽  
Minimum Velocity ◽  
Minimization Scheme

SUMMARYIn this paper, a pseudoinverse-type bi-criteria minimization scheme is proposed and investigated for the redundancy resolution of robot manipulators at the joint-acceleration level. Such a bi-criteria minimization scheme combines the weighted minimum acceleration norm solution and the minimum velocity norm solution via a weighting factor. The resultant bi-criteria minimization scheme, formulated as the pseudoinverse-type solution, not only avoids the high joint-velocity and joint-acceleration phenomena but also causes the joint velocity to be near zero at the end of motion. Computer simulation results based on a 4-Degree-of-Freedom planar robot manipulator comprising revolute joints further verify the efficacy and flexibility of the proposed bi-criteria minimization scheme on robotic redundancy resolution.

Bi-criteria velocity minimization of robot manipulators using LVI-based primal-dual neural network and illustrated via PUMA560 robot arm

Robotica ◽  
2009 ◽  
Vol 28 (4) ◽  
pp. 525-537 ◽  
Author(s):  
Yunong Zhang ◽  
Kene Li
Keyword(s):  
Neural Network ◽  
Piecewise Linear ◽  
Linear Structure ◽  
Robot Arm ◽  
Planning Scheme ◽  
Primal Dual ◽  
Joint Limits ◽  
Joint Velocity ◽  
High Computational Efficiency ◽  
Minimization Scheme

SUMMARYIn this paper, to diminish discontinuity points arising in the infinity-norm velocity minimization scheme, a bi-criteria velocity minimization scheme is presented based on a new neural network solver, i.e., an LVI-based primal-dual neural network. Such a kinematic planning scheme of redundant manipulators can incorporate joint physical limits, such as, joint limits and joint velocity limits simultaneously. Moreover, the presented kinematic planning scheme can be reformulated as a quadratic programming (QP) problem. As a real-time QP solver, the LVI-based primal-dual neural network is developed with a simple piecewise linear structure and high computational efficiency. Computer simulations performed based on a PUMA560 manipulator model are presented to illustrate the validity and advantages of such a bi-criteria velocity minimization neural planning scheme for redundant robot arms.

scholarly journals A Genetic-Algorithm-Based DC Current Minimization Scheme for Transformless Grid-Connected Photovoltaic Inverters

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 746 ◽  
Author(s):  
Lei Song ◽  
Lijun Huang ◽  
Bo Long ◽  
Fusheng Li
Keyword(s):  
Genetic Algorithm ◽  
Back Propagation ◽  
The Novel ◽  
Proof Of Concept ◽  
Three Phase ◽  
Suppression Process ◽  
Dc Current ◽  
Three Phase Inverter ◽  
Minimization Scheme

Transformerless grid-connected inverters are of great industrial value in photovoltaic power generation. However, the direct current (DC) induced into the inverter’s output degrades the power quality of the grid. Recently, a back-propagation neural work proportional–integral–derivative (BP-PID) scheme has proven helpful in solving this problem. However, this scheme can be improved by reducing the suppressing time and overshoot. A genetic algorithm (GA)-based DC current minimization scheme, namely the genetic-algorithm-based BP-PID (GA-BP-PID) scheme, was established in this study. In this scheme, GA was used off-line to optimize the initial weights within the BP neural network. Subsequently, the optimal weight was applied to the online DC current suppression process. Compared with the BP-PID scheme, the proposed scheme can reduce the suppressing time by 59% and restrain the overshoot. A prototype of the proposed scheme was implemented and tested on experimental hardware as a proof of concept. The results of the scheme were verified using a three-phase inverter experiment. The novel GA-PB-PID scheme proposed in this study was proven efficient in reducing the suppressing time and overshoot.

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