scholarly journals Trajectory Planning of Robot Manipulator Based on RBF Neural Network

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1207
Author(s):  
Qisong Song ◽  
Shaobo Li ◽  
Qiang Bai ◽  
Jing Yang ◽  
Ansi Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Trajectory Planning ◽  
Polynomial Interpolation ◽  
Robot Manipulator ◽  
Rbf Neural Network ◽  
Tracking Error ◽  
Lyapunov Theory ◽  
Degree Of Freedom ◽  
Quintic Polynomial ◽  
High Degree

Robot manipulator trajectory planning is one of the core robot technologies, and the design of controllers can improve the trajectory accuracy of manipulators. However, most of the controllers designed at this stage have not been able to effectively solve the nonlinearity and uncertainty problems of the high degree of freedom manipulators. In order to overcome these problems and improve the trajectory performance of the high degree of freedom manipulators, a manipulator trajectory planning method based on a radial basis function (RBF) neural network is proposed in this work. Firstly, a 6-DOF robot experimental platform was designed and built. Secondly, the overall manipulator trajectory planning framework was designed, which included manipulator kinematics and dynamics and a quintic polynomial interpolation algorithm. Then, an adaptive robust controller based on an RBF neural network was designed to deal with the nonlinearity and uncertainty problems, and Lyapunov theory was used to ensure the stability of the manipulator control system and the convergence of the tracking error. Finally, to test the method, a simulation and experiment were carried out. The simulation results showed that the proposed method improved the response and tracking performance to a certain extent, reduced the adjustment time and chattering, and ensured the smooth operation of the manipulator in the course of trajectory planning. The experimental results verified the effectiveness and feasibility of the method proposed in this paper.

Trajectory Tracking of a Spherical Robot Based on an RBF Neural Network

2011 ◽  
Vol 383-390 ◽  
pp. 631-637 ◽  
Author(s):  
Ming Hui Zheng ◽  
Qiang Zhan ◽  
Jin Kun Liu ◽  
Yao Cai
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Rbf Neural Network ◽  
Tracking Error ◽  
Lyapunov Theory ◽  
Pd Controller ◽  
Spherical Robot ◽  
Rule Based ◽  
Rbf Network ◽  
Spherical Mobile Robot

This paper deals with trajectory tracking problem of a spherical mobile robot, BHQ-1. First, a desired velocity is obtained by proposing a PD controller based on the kinematics. Then a PD controller with an RBF (Radial Basis Function) neural network is proposed based on the desired velocity and the inexact dynamics. The weights of the RBF network are designed with an adaptive rule based on the tracking error, and hence the network can compensate the uncertainties of the dynamics more effectively. Stability is presented via Lyapunov Theory and simulation results are provided to illustrate the tracking performance.

Application of RBF Neural Network in the Manipulator Trajectory Planning

2014 ◽  
Vol 651-653 ◽  
pp. 659-662 ◽  
Author(s):  
Meng Meng Du ◽  
Hai Tao Zhang
Keyword(s):  
Neural Network ◽  
Trajectory Planning ◽  
Control Strategy ◽  
Polynomial Interpolation ◽  
Rbf Neural Network ◽  
Joint Space ◽  
Interpolation Algorithm ◽  
Matlab Simulation ◽  
Nonlinear Characteristics ◽  
Target Trajectory

In view of the high order, nonlinear characteristics of target trajectory, control strategy is proposed for a kind of RBF neural network and polynomial interpolation combining. According to the specific requirements of trajectory planning, the manipulator in joint space variables are expressed as one or two order derivative function by polynomial interpolation algorithm, then the RBF neural network is used for tracking the target trajectory. Finally, through MATLAB simulation, the accuracy of this method is verified.

scholarly journals New time delay estimation-based virtual decomposition control for n-DoF robot manipulator

2021 ◽  
Vol 10 (3) ◽  
pp. 192
Author(s):  
Hachmia Faqihi ◽  
Khalid Benjelloun ◽  
Maarouf Saad ◽  
Mohammed Benbrahim ◽  
M. Nabil Kabbaj
Keyword(s):  
Time Delay ◽  
Dynamic Model ◽  
Control Strategy ◽  
Robot Manipulator ◽  
Time Delay Estimation ◽  
Lyapunov Theory ◽  
Degree Of Freedom ◽  
Delay Estimation ◽  
The Stability

<p>One of the most efficient approaches to control a multiple degree-of-freedom robot manipulator is the virtual decomposition control (VDC). However, the use of the re- gressor technique in the conventionnal VDC to estimate the unknown and uncertaities parameters present some limitations. In this paper, a new control strategy of n-DoF robot manipulator, refering to reorganizing the equation of the VDC using the time delay estimation (TDE) have been investigated. In the proposed controller, the VDC equations are rearranged using the TDE for unknown dynamic estimations. Hence, the decoupling dynamic model for the manipulator is established. The stability of the overall system is proved based on Lyapunov theory. The effectiveness of the proposed controller is proved via case study performed on 7-DoF robot manipulator and com- pared to the conventionnal Regressor-based VDC according to some evalution criteria. The results carry out the validity and efficiency of the proposed time delay estimation- based virtual decomposition controller (TD-VDC) approach.</p>

Modeling for tracking error of theodolite based on RBF neural network

2012 ◽  
Vol 20 (4) ◽  
pp. 818-825
Author(s):  
李淼 LI Miao ◽  
高慧斌 GAO Hui-bin
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Tracking Error

scholarly journals Neural Network-Based State Estimation for a Closed-Loop Control Strategy Applied to a Fed-Batch Bioreactor

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Santiago Rómoli ◽  
Mario Serrano ◽  
Francisco Rossomando ◽  
Jorge Vega ◽  
Oscar Ortiz ◽  
...  
Keyword(s):  
Neural Network ◽  
Closed Loop ◽  
Rbf Neural Network ◽  
Control Strategies ◽  
Tracking Error ◽  
Closed Loop Control ◽  
Online Information ◽  
Fed Batch ◽  
Loop Control ◽  
Batch Bioreactor

The lack of online information on some bioprocess variables and the presence of model and parametric uncertainties pose significant challenges to the design of efficient closed-loop control strategies. To address this issue, this work proposes an online state estimator based on a Radial Basis Function (RBF) neural network that operates in closed loop together with a control law derived on a linear algebra-based design strategy. The proposed methodology is applied to a class of nonlinear systems with three types of uncertainties: (i) time-varying parameters, (ii) uncertain nonlinearities, and (iii) unmodeled dynamics. To reduce the effect of uncertainties on the bioreactor, some integrators of the tracking error are introduced, which in turn allow the derivation of the proper control actions. This new control scheme guarantees that all signals are uniformly and ultimately bounded, and the tracking error converges to small values. The effectiveness of the proposed approach is illustrated on the basis of simulated experiments on a fed-batch bioreactor, and its performance is compared with two controllers available in the literature.

Globally exponential continuous controller/observer for position tracking in robot manipulators with hysteretic joint friction

Robotica ◽  
2009 ◽  
Vol 28 (5) ◽  
pp. 759-763 ◽  
Author(s):  
Srinivasulu Malagari ◽  
Brian J. Driessen
Keyword(s):  
Robot Manipulator ◽  
Robot Manipulators ◽  
Tracking Error ◽  
Degree Of Freedom ◽  
Position Tracking ◽  
Observer Error ◽  
Joint Friction

SUMMARYIn this work, we present a continuous observer and continuous controller for a multiple degree of freedom robot manipulator with hysteretic joint friction. The fictitious hysteresis state is of course unknown to the controller and must be estimated. The joint velocities are assumed measured here. For this considered plant, we propose and present a continuous observer/controller that estimates or observes the hysteresis state and drives the position tracking error to zero. We prove that the combined tracking error and observer error converges to zero globally exponentially.

Stiffness-based trajectory planning of a 6-DOF cable-driven parallel manipulator

2016 ◽  
Vol 231 (21) ◽  
pp. 3999-4011 ◽  
Author(s):  
Wenjia Zhang ◽  
Weiwei Shang ◽  
Bin Zhang ◽  
Fei Zhang ◽  
Shuang Cong
Keyword(s):  
Velocity Profile ◽  
Trajectory Planning ◽  
Parallel Manipulator ◽  
Kinematic Model ◽  
Degree Of Freedom ◽  
Performance Indices ◽  
Curved Trajectories ◽  
Quintic Polynomial ◽  
Point To Point ◽  
Six Degree Of Freedom

The stiffness of the cable-driven parallel manipulator is usually poor because of the cable flexibility, and the existing methods on trajectory planning mainly take the minimum time and the optimal energy into account, not the stiffness. To solve it, the effects of different trajectories on stiffness are studied for a six degree-of-freedom cable-driven parallel manipulator, according to the kinematic model and the dynamic model. The condition number and the minimum eigenvalue of the dimensionally homogeneous stiffness matrix are selected as performance indices to analyze the stiffness changes during the motion. The simulation experiments are implemented on a six degree-of-freedom cable-driven parallel manipulator, to study the stiffness of three different trajectory planning approaches such as S-type velocity profile, quintic polynomial, and trigonometric function. The accelerations of different methods are analyzed, and the stiffness performances for the methods are compared after planning the point-to-point straight and the curved trajectories. The simulation results indicate that the quintic polynomial and S-type velocity profile have the optimal performance to keep the stiffness stable during the motion control and the travel time of the quintic polynomial can be optimized sufficiently while keeping stable.

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