RNN for Perturbed Manipulability Optimization of Manipulators Based on a Distributed Scheme: A Game-Theoretic Perspective

Zhang, Jiazheng; Jin, Long; Cheng, Long

doi:10.1109/tnnls.2020.2963998

Cited by 64 publications

(18 citation statements)

References 41 publications

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“…Secondly, for promoting the simulation process, the adopted parameters are defined as the dimensions p = 6 and m = 3; the desired path being the plum blossom curve; the whole execution time T = 7 s; the sampling gap δ = 0.001 s; the selected robot being UR5 [44]; the lower boundθ − (t) and the upper boundθ rad/s, respectively; the injected perturbation ψ is supposed to random one that ψ ∈ [3,7] 6×1 ; the index reflecting the motion effect of the robot = ρ − ρ d embodying the distance between the generated path and the expected one. Besides, the design parameters related to the models are set as γ = κ = 1000.…”

Section: Algorithm 1 94lvimentioning

confidence: 99%

Discrete Perturbation-Immunity Neural Network for Dynamic Constrained Redundant Robot Control

Yang

Chen

et al. 2020

IEEE Access

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Considering the necessity of merging the physical constraints in joint space for redundant robot motion control in practice by regarding the kinematics of robots, a discrete perturbation-immunity neural network (DPINN) model with high robustness and predominant convergence is proposed in this article for managing the problem. It is worth emphasizing that this proposed neural network is developed to provide a solution algorithm to dispose of the practical applications in robot motion control that is investigated by reforming it into perturbed dynamic quadratic programming (QP) with equality and inequality constraints, which remedies the lack and drawbacks of existing methodologies. Moreover, theoretical verifications and results verify the convergence performance and noise suppression ability of the proposed neural network, which is further verified by simulation examples. In addition, the simulations on robot motion control embody its superiority and powerful versatility compared to the existing methods. In summary, the proposed neural network extends the sphere of application in perturbed dynamic QP with double-bound constraints and offers a feasible scheme capable of controlling robots in a neural-network thought. INDEX TERMS Discrete perturbation-immunity neural network (DPINN), physical constraints, robots, theoretical verifications.

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Section: Algorithm 1 94lvimentioning

confidence: 99%

Discrete Perturbation-Immunity Neural Network for Dynamic Constrained Redundant Robot Control

Yang

Chen

et al. 2020

IEEE Access

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“…This method has been extended to the research of the manipulator with unknown structure and achieved remarkable results [22]. In addition, according to the game theory, the manipulability optimization method of manipulators has been proposed in [23], which is assisted by a specially designed recurrent neural network with the great noise resistance. In practical applications, considering the convenience of algorithm implementation in hardware, Ferreira et al have creatively proposed a classic gradient-based neural network (GNN) with parallel processing capabilities to solving the DSLEs [3].…”

Section: Introductionmentioning

confidence: 99%

Two DTZNN Models of O(τ⁴) Pattern for Online Solving Dynamic System of Linear Equations: Application to Manipulator Motion Generation

et al. 2020

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In this paper, firstly, a high accuracy one-step-ahead numerical differentiation formula with O(τ 4) pattern is proposed for discretization. Meanwhile, a high precision first-order derivative formula of the backward difference rule with O(τ 4) pattern error is given to approximate the derivative information. Then, two high accuracy discrete-time zeroing-type models (HADTZTM) with O(τ 4) pattern, i.e., HADTZTM with derivative information known (HADTZTM-K) and HADTZTM with derivative information unknown (HADTZTM-U), are developed, analyzed and investigated for online solving the dynamic system of linear equations (DSLEs). In addition, the 0-stability, consistency, and convergence of the HADTZTM-K and HADTZTM-U are verified for DSLEs. From a theoretical/numerical viewpoint, the classical models are revisited and analyzed for online solving DSLEs. Ultimately, simulation experiment including an application to the path-tracking of the four-link planar manipulator is conducted to demonstrate the efficiency and superiority of the HADTZTM-K and HADTZTM-U, where the HADTZTM-U overcomes the difficulty of derivative information unknown in practical applications. INDEX TERMS Dynamic system of linear equations (DSLEs), discrete-time zeroing neural network (DTZNN), backward difference formula, theoretical results, steady-state residual error.

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“…State adjustment refers to the process of moving the manipulator from the current state to the target state [26] without the displacement of the end-effector. Most of the research on the redundant manipulator focuses on motion planning such as the manipulability optimization method stated in [27] during the task execution of the redundant manipulator, but there are few achievements on the problem of state adjustment [28]- [30]. In fact, state adjustment is imperative in certain situations [31], [32].…”

Section: Introductionmentioning

confidence: 99%

Recurrent Neural Network for State Adjustment of Redundant Manipulators

Jin

Huang

et al. 2020

IEEE Access

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To make joints of a redundant manipulator moving automatically to a target state, a stateadjustment (SA) scheme is studied and modified in this paper. Specifically, owing to the problem of non-zero initial joint velocity in the SA scheme leading to a potential hazard to redundant manipulators, a modified state-adjustment (MSA) scheme is obtained on the basis of the SA scheme. The MSA scheme achieves the state adjustment by minimizing the differences between the joint angles and the target values. For solving the MSA scheme, a recurrent neural network (RNN) model is derived, of which the critical component is to iterate over the joint angles and joint velocities. The MSA scheme solved by the RNN model enables the redundant manipulator to adjust to the target state automatically while ensuring that the initial joint velocity is zero. Beyond that, several comparative simulations demonstrate the availability and accuracy of the MSA scheme solved by the RNN model in controlling the state adjustment of redundant manipulators. INDEX TERMS State-adjustment scheme, recurrent neural network, redundant manipulators.

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RNN for Perturbed Manipulability Optimization of Manipulators Based on a Distributed Scheme: A Game-Theoretic Perspective

Cited by 64 publications

References 41 publications

Discrete Perturbation-Immunity Neural Network for Dynamic Constrained Redundant Robot Control

Discrete Perturbation-Immunity Neural Network for Dynamic Constrained Redundant Robot Control

Two DTZNN Models of O(τ⁴) Pattern for Online Solving Dynamic System of Linear Equations: Application to Manipulator Motion Generation

Recurrent Neural Network for State Adjustment of Redundant Manipulators

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RNN for Perturbed Manipulability Optimization of Manipulators Based on a Distributed Scheme: A Game-Theoretic Perspective

Cited by 64 publications

References 41 publications

Discrete Perturbation-Immunity Neural Network for Dynamic Constrained Redundant Robot Control

Discrete Perturbation-Immunity Neural Network for Dynamic Constrained Redundant Robot Control

Two DTZNN Models of O(τ4) Pattern for Online Solving Dynamic System of Linear Equations: Application to Manipulator Motion Generation

Recurrent Neural Network for State Adjustment of Redundant Manipulators

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Two DTZNN Models of O(τ⁴) Pattern for Online Solving Dynamic System of Linear Equations: Application to Manipulator Motion Generation