Infinity-norm acceleration minimization of robotic redundant manipulators using the LVI-based primal–dual neural network

Zhang, Yunong; Yin, Jianxin; Cai, Binghuang

doi:10.1016/j.rcim.2008.02.002

Cited by 28 publications

(27 citation statements)

References 13 publications

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“…An LVI-PDNN was developed in ref. [147] with simple piecewise linear dynamics, global (exponential) convergence to optimal solutions, and capability of handling general QP and linear programming problems in an inverse-free manner. In ref.…”

Section: Linear Variational Inequalities-based Primal-dual Neural Netmentioning

confidence: 99%

Inverse Kinematics of Redundant Manipulators Formulated as Quadratic Programming Optimization Problem Solved Using Recurrent Neural Networks: A Review

2019

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SUMMARYThe Inverse Kinematics (IK) problem of manipulators can be divided into two distinct steps: (1) Problem formulation, where the problem is developed into a form which can then be solved using various methods. (2) Problem solution, where the IK problem is actually solved by producing the values of different joint space variables (joint angles, joint velocities or joint accelerations). The main focus of this paper is concentrated on the discussion of the IK problem of redundant manipulators, formulated as a quadratic programming optimization problem solved by different kinds of recurrent neural networks.

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Section: Linear Variational Inequalities-based Primal-dual Neural Netmentioning

confidence: 99%

Inverse Kinematics of Redundant Manipulators Formulated as Quadratic Programming Optimization Problem Solved Using Recurrent Neural Networks: A Review

2019

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“…These networks have been used in predictive control (Li et al, 2015), minimum variance control (Toshani et al, 2016), and more recently in sliding mode control (Toshani and Farrokhi, 2019). Furthermore, PRNNs have been widely utilized in robotics to realize performance goals such as minimum torque (Zhang and Wang, 2002) and optimization of joint norms (Cai and Zhang, 2010; Zhang et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Control of magnetic levitation system using recurrent neural network-based adaptive optimal backstepping strategy

Fatemimoghadam

Toshani

Manthouri

2020

Transactions of the Institute of Measurement and Control

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In this paper, a novel approach is proposed for adjusting the position of a magnetic levitation system using projection recurrent neural network-based adaptive backstepping control (PRNN-ABC). The principles of designing magnetic levitation systems have widespread applications in the industry, including in the production of magnetic bearings and in maglev trains. Levitating a ball in space is carried out via the surrounding attracting or repelling magnetic forces. In such systems, the permissible range of the actuator is significant, especially in practical applications. In the proposed scheme, the procedure of designing the backstepping control laws based on the nonlinear state-space model is carried out first. Then, a constrained optimization problem is formed by defining a performance index and taking into account the control limits. To formulate the recurrent neural network (RNN), the optimization problem is first converted into a constrained quadratic programming (QP). Then, the dynamic model of the RNN is derived based on the Karush-Kuhn-Tucker (KKT) optimization conditions and the variational inequality theory. The convergence analysis of the neural network and the stability analysis of the closed-loop system are performed using the Lyapunov stability theory. The performance of the closed-loop system is assessed with respect to tracking error and control feasibility.

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“…However, The above method is not suitable for acceleration control or torque control, and the velocity layer solution cannot take into account the joint-acceleration limits. Some scholars have proposed many methods for solving redundancy-resolution problem of redundant manipulators on the acceleration layer [8], [18]- [21], [23]. For example, Zhang et al proposed the infinity norm of the joint acceleration (INAM) scheme which was solved by the LVI-based primal-dual neural network [18].…”

Section: Introductionmentioning

confidence: 99%

Acceleration-Level Multi-Criteria Optimization for Remedying Joint-Angle Drift of Redundant Manipulators on Complex Path Tracking

Jia

Chen

et al. 2019

IEEE Access

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In this paper, to remedy the joint-angle drift problem of redundant manipulators at the acceleration level, a multi-criteria optimization motion planning (MCOMP) scheme is proposed and investigated for redundant manipulators. In addition, the proposed scheme considers physical constraints and guarantees values of the joint-velocity and joint-acceleration to approach zero at the end of the path-tracking task. The proposed scheme is reformed into one standard quadratic program problem, which is then solved by the simplified linear variational inequalities-based primal-dual neural network (S-LVI-PDNN). Furthermore, three tracking path computer simulations (i.e., ''Nut line'' pattern, ''Peach'' pattern, and Chinese character ''tu'' pattern) are illustrated, which verify the validity and advantage of the proposed scheme. Finally, the physical experiment based on the JACO 2 manipulator further verifies the practicability of the proposed scheme for solving the joint-angle drift of redundant manipulators. INDEX TERMS Multi-criteria optimization motion planning, joint-angle drift, acceleration-level, redundant manipulators, physical constraints, physical experiment.

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Infinity-norm acceleration minimization of robotic redundant manipulators using the LVI-based primal–dual neural network

Cited by 28 publications

References 13 publications

Inverse Kinematics of Redundant Manipulators Formulated as Quadratic Programming Optimization Problem Solved Using Recurrent Neural Networks: A Review

Inverse Kinematics of Redundant Manipulators Formulated as Quadratic Programming Optimization Problem Solved Using Recurrent Neural Networks: A Review

Control of magnetic levitation system using recurrent neural network-based adaptive optimal backstepping strategy

Acceleration-Level Multi-Criteria Optimization for Remedying Joint-Angle Drift of Redundant Manipulators on Complex Path Tracking

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