Two/Infinity Norm Criteria Resolution of Manipulator Redundancy at Joint-Acceleration Level Using Primal-Dual Neural Network

2011 ◽  
Vol 14 (4) ◽  
pp. 1036-1046 ◽  
Author(s):  
Yu-Nong Zhang ◽  
Bing-Huang Cai ◽  
Jiang-Ping Yin ◽  
Lei Zhang
Keyword(s):  
Neural Network ◽  
Infinity Norm ◽  
Acceleration Level ◽  
Primal Dual ◽  
Joint Acceleration

Tri-Criteria Optimization Motion Planning at Acceleration-Level of Dual Redundant Manipulators

Robotica ◽  
2019 ◽  
Vol 38 (6) ◽  
pp. 983-999
Author(s):  
Zhaoli Jia ◽  
Siyuan Chen ◽  
Zhijun Zhang ◽  
Nan Zhong ◽  
Pengchao Zhang ◽  
...  
Keyword(s):  
Motion Planning ◽  
Joint Angle ◽  
Weighting Factor ◽  
Quadratic Program ◽  
Redundant Manipulators ◽  
Repetitive Motion ◽  
Acceleration Level ◽  
Primal Dual ◽  
Joint Velocity ◽  
Joint Acceleration

SUMMARYIn order to solve joint-angle drift problem of dual redundant manipulators at acceleration-level, an acceleration-level tri-criteria optimization motion planning (ALTC-OMP) scheme is proposed, which combines the minimum acceleration norm, repetitive motion planning, and infinity-norm acceleration minimization solutions via weighting factor. This scheme can resolve the joint-angle drift problem of dual redundant manipulators which will arise in single criteria or bi-criteria scheme. In addition, the proposed scheme considers joint-velocity joint-acceleration physical limits. The proposed scheme can not only guarantee joint-velocity and joint-acceleration within their physical limits, but also ensure that final joint-velocity and joint-acceleration are near to zero. This scheme is realized by dual redundant manipulators which consist of left and right manipulators. In order to ensure the coordinated operation of manipulators, two motion planning problems are reformulated as two general quadratic program (QP) problems and further unified into one standard QP problem, which is solved by a simplified linear-variational-inequalities-based primal-dual neural network at the acceleration-level. Computer-simulation results based on dual PUMA560 redundant manipulators further demonstrate the effectiveness and feasibility of the proposed ALTC-OMP scheme to resolve joint-angle drift problem arising in the dual redundant manipulators.

scholarly journals Does Fiscal Decentralization in Indonesia have Asymmetrical Information?: Principal-Agent Model, Primal-Dual, and a Neural Network Analysis

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Kun Haribowo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Central Government ◽  
Fiscal Decentralization ◽  
Principal Agent ◽  
Agent Model ◽  
Primal Dual ◽  
Principal Agent Model ◽  
Artificial Neural ◽  
Subnational Governments

In reality, subnational governments suffer from moral hazard, creating uncertainty which, in turn, causes economic inefficiency. The behavior of subnational governments cannot be observed by the central government. An analysis which takes into account this phenomenon is therefore needed. Decentralization implies delegating authority from central government to subnational governments. In this study, the subnational government is represented by the local government. This study utilizes a model of principal-agent. The central government acts as a principal who delegates fiscal authority to subnational governments who act as agents. By applying principal-agent model, we can use the primal-dual approach to analyze both revenue and expenditure assignment associated with the tax effort of the subnational governments. The result from artificial neural network approach shows that asymmetric information between central and subnational governments exists in Indonesia.Keywords: Artificial Neural Network, Fiscal Decentralization, Local Tax Effort, Primal-Dual, Principal-Agent.

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.

Repetitive motion planning of PA10 robot arm subject to joint physical limits and using LVI-based primal–dual neural network

Mechatronics ◽  
2008 ◽  
Vol 18 (9) ◽  
pp. 475-485 ◽  
Author(s):  
Yunong Zhang ◽  
Xuanjiao Lv ◽  
Zhonghua Li ◽  
Zhi Yang ◽  
Ke Chen
Keyword(s):  
Neural Network ◽  
Motion Planning ◽  
Robot Arm ◽  
Repetitive Motion ◽  
Primal Dual
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