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

scholarly journals Adaptive Robust Controller Design-Based RBF Neural Network for Aerial Robot Arm Model

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 831
Author(s):  
Izzat Al-Darraji ◽  
Dimitrios Piromalis ◽  
Ayad A. Kakei ◽  
Fazal Qudus Khan ◽  
Milos Stojemnovic ◽  
...  
Keyword(s):  
Neural Network ◽  
Controller Design ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Robot Arm ◽  
Robust Controller ◽  
Practical Applications ◽  
Control Loops ◽  
Modeling Errors ◽  
Aerial Robot

Aerial Robot Arms (ARAs) enable aerial drones to interact and influence objects in various environments. Traditional ARA controllers need the availability of a high-precision model to avoid high control chattering. Furthermore, in practical applications of aerial object manipulation, the payloads that ARAs can handle vary, depending on the nature of the task. The high uncertainties due to modeling errors and an unknown payload are inversely proportional to the stability of ARAs. To address the issue of stability, a new adaptive robust controller, based on the Radial Basis Function (RBF) neural network, is proposed. A three-tier approach is also followed. Firstly, a detailed new model for the ARA is derived using the Lagrange–d'Alembert principle. Secondly, an adaptive robust controller, based on a sliding mode, is designed to manipulate the problem of uncertainties, including modeling errors. Last, a higher stability controller, based on the RBF neural network, is implemented with the adaptive robust controller to stabilize the ARAs, avoiding modeling errors and unknown payload issues. The novelty of the proposed design is that it takes into account high nonlinearities, coupling control loops, high modeling errors, and disturbances due to payloads and environmental conditions. The model was evaluated by the simulation of a case study that includes the two proposed controllers and ARA trajectory tracking. The simulation results show the validation and notability of the presented control algorithm.

On-line multi-criteria based collision-free posture generation of redundant manipulator in constrained workspace

Robotica ◽  
2002 ◽  
Vol 20 (6) ◽  
pp. 625-636 ◽  
Author(s):  
Jin-Liang Chen ◽  
Jing-Sin Liu ◽  
Wan-Chi Lee ◽  
Tzu-Chen Liang
Keyword(s):  
Null Space ◽  
Large Degree ◽  
Redundancy Resolution ◽  
Secondary Tasks ◽  
End Effector ◽  
Robotic Arms ◽  
Tracking Motion ◽  
On Line ◽  
Joint Limits ◽  
Joint Velocity

The manipulator with a large degree of redundancy is useful for realizing multiple tasks such as maneuvering the robotic arms in the constrained workspace, e.g. the task of maneuvering the end-effector of the manipulator along a pre-specified path into a window. This paper presents an on-line technique based on a posture generation rule to compute a null-space joint velocity vector in a singularity-robust redundancy resolution method. This rule suggests that the end of each link has to track an implicit trajectory that is indirectly resulted from the constraint imposed on tracking motion of the end-effector. A proper posture can be determined by sequentially optimizing an objective function integrating multiple criteria of the orientation of each link from the end-effector toward the base link as the secondary task for redundancy resolution, by assuming one end of the link is clamped. The criteria flexibly incorporate obstacle avoidance, joint limits, preference of posture in tracking, and connection of posture to realize a compromise between the primary and secondary tasks. Furthermore, computational demanding of the posture is reduced due to the sequential link-by-link computation feature. Simulations show the effectiveness and flexibility of the proposed method in generating proper postures for the collision avoidance and the joint limits as a singularity-robust null-space projection vector in maneuvering redundant robots within constrained workspaces.

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.

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.

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