Optimal control research on a manipulator’s combined feedback device by the variational method genetic algorithm radial basis function method

This article aims to improve the accuracy of each joint in a manipulator and to ensure the high-speed and real-time requirements. A method called the variational method genetic algorithm radial basis function, which is based on a combination feedback controller, is proposed to solve the optimal control problem. It is proposed a combined feedback with a linear part and a nonlinear part. We reconstruct the manipulator’s kinematics and dynamics models with a feedback control. In this model, the optimal trajectory, which was solved by the variation method, is regarded as the desired output. The other one is also established an improved genetic algorithm radial basis function neural network model. The optimal trajectory is rapidly solved by using the desired output and the improved genetic algorithm radial basis function neural network. This method can greatly improve the speed of the calculation and guarantee real-time performance while simultaneously ensuring accuracy.

[1]  Erdinc Sahin Conkur.  Path planning using potential fields for highly redundant manipulators , 2005, Robotics Auton. Syst..

[2]  Xiaoyang Liu,et al.  A new optimization particle filtering navigation location method for aquatic plants cleaning workboat in crab farming , 2018, International Journal of Advanced Robotic Systems.

[3]  Amir H. Mohammadi,et al.  GA-RBF model for prediction of dew point pressure in gas condensate reservoirs , 2016 .

[4]  Leiyuan Li,et al.  Autonomous positioning control of manipulator and fast surface fitting based on particle filter and point cloud library technology , 2017 .

[5]  Yong Tao,et al.  A Sliding Mode Control-Based on a RBF Neural Network for Deburring Industry Robotic Systems , 2016 .

[6]  Gursel Alici,et al.  Locomotion analysis and optimization of actinomorphic robots with soft arms actuated by shape memory alloy wires , 2018, International Journal of Advanced Robotic Systems.

[7]  Zhongyu Wang,et al.  Calibration of visual model for space manipulator with a hybrid LM–GA algorithm , 2016 .

[8]  Ari Berger,et al.  On Energy-Optimal and Time-Optimal Precise Displacement of Rigid Body with Friction , 2017, J. Optim. Theory Appl..

[9]  Abdelkrim Boukabou,et al.  Design of an intelligent optimal neural network-based tracking controller for nonholonomic mobile robot systems , 2017, Neurocomputing.

[10]  Hee-Jun Kang,et al.  A novel adaptive finite-time tracking control for robotic manipulators using nonsingular terminal sliding mode and RBF neural networks , 2016 .

[11]  Frank Chongwoo Park,et al.  Fast Robot Motion Generation Using Principal Components: Framework and Algorithms , 2008, IEEE Transactions on Industrial Electronics.

[12]  Utku Büyükşahin,et al.  A low-cost, human-like, high-resolution, tactile sensor based on optical fibers and an image sensor , 2018, International Journal of Advanced Robotic Systems.

[13]  Saeed Khorashadizadeh,et al.  Optimal sliding mode control of a robot manipulator under uncertainty using PSO , 2016 .

[14]  Bin Yao,et al.  Non-linear sliding mode control of the lower extremity exoskeleton based on human–robot cooperation , 2016 .

[15]  Yanzhu Hu,et al.  3D Reconstruction of End-Effector in Autonomous Positioning Process Using Depth Imaging Device , 2016 .

[16]  Qibing Jin,et al.  Decoupled ARX and RBF Neural Network Modeling Using PCA and GA Optimization for Nonlinear Distributed Parameter Systems , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[17]  Amin Nikoobin,et al.  Indirect solution of optimal control problems with state variable inequality constraints: finite difference approximation , 2015, Robotica.

[18]  Dean Zhao,et al.  An optimized RBF neural network algorithm based on partial least squares and genetic algorithm for classification of small sample , 2016, Appl. Soft Comput..

[19]  Mir Mohammad Ettefagh,et al.  Robust adaptive control of a bio-inspired robot manipulator using bat algorithm , 2016, Expert Syst. Appl..

[20]  Viet-Thanh Pham,et al.  Optimal adaptive higher order controllers subject to sliding modes for a carrier system , 2018 .

[21]  Liviu Moldovan,et al.  ANN Based Inverse Dynamic Model of the 6-PGK Parallel Robot Manipulator , 2016, Int. J. Comput. Commun. Control.

[22]  Yuan Feng,et al.  Real-time fast moving object tracking in severely degraded videos captured by unmanned aerial vehicle , 2018 .

[23]  Mahnaz Hashemi.  Adaptive neural dynamic surface control of MIMO nonlinear time delay systems with time-varying actuator failures , 2017 .

[24]  Meng Chen,et al.  Optimal randomized path planning for redundant manipulators based on Memory-Goal-Biasing , 2018 .

[26]  Ilian A. Bonev,et al.  Online pose correction of an industrial robot using an optical coordinate measure machine system , 2018, International Journal of Advanced Robotic Systems.

[27]  Xiaoming Zhang,et al.  Relative posture-based kinematic calibration of a 6-RSS parallel robot by optical coordinate measurement machine , 2018 .