Adaptive Tracking Control Algorithm for Picking Wheel Robot

2012 ◽  
Vol 10 (8) ◽  
Author(s):  
Zhiyong Zhang ◽  
Dongjian He
Keyword(s):  
Tracking Control ◽  
Control Algorithm ◽  
Adaptive Tracking Control ◽  
Adaptive Tracking

RBF Neural Network Based Adaptive Tracking Control for a Class of Nonlinear Plant Using Stochastic U-Model

2011 ◽  
Vol 474-476 ◽  
pp. 1209-1214 ◽  
Author(s):  
Bin Wang ◽  
Cai Liu ◽  
Xue Li Wu ◽  
Lei Liu
Keyword(s):  
Neural Network ◽  
Tracking Control ◽  
Control Algorithm ◽  
Design Procedure ◽  
Least Mean Square ◽  
Unknown Parameters ◽  
Model Framework ◽  
Adaptive Tracking Control ◽  
Adaptive Tracking ◽  
Wide Range

In this paper an adaptive tracking control algorithm and its step by step implementation procedure are developed for a class of nonlinear plants within a U-model framework with unknown parameters. With the author’s previous justification, not only the control oriented model represents a wide range of smooth (polynomial) nonlinear dynamic plants (without using linearisation approximation at all), but also make almost all linear control system design techniques directly applicable (with a root solver bridging the linear design and calculation of controller output). A new technique is proposed to design an online control algorithm using the Radial Basis Functions Neural Network (RBFNN). The plant parameters are estimated online and are used to update the weights of the RBFNN. The weights update equations are derived based on the well known LMS (least mean square). A number of simulated case studies are conducted to illustrate the efficiency of the claimed insight and design procedure.

Multi-Robot Obstacle Avoidance Based on the Improved Artificial Potential Field and PID Adaptive Tracking Control Algorithm

Robotica ◽  
2019 ◽  
Vol 37 (11) ◽  
pp. 1883-1903 ◽  
Author(s):  
Zhenhua Pan ◽  
Dongfang Li ◽  
Kun Yang ◽  
Hongbin Deng
Keyword(s):  
Obstacle Avoidance ◽  
Potential Field ◽  
Tracking Control ◽  
Formation Control ◽  
Control Algorithm ◽  
Situational Awareness ◽  
Artificial Potential Field ◽  
Adaptive Tracking Control ◽  
Adaptive Tracking ◽  
Multi Robot

SummaryAs for the obstacle avoidance and formation control for the multi-robot systems, this paper presents an obstacle-avoidance method based on the improved artificial potential field (IAPF) and PID adaptive tracking control algorithm. In order to analyze the dynamics and kinematics of the robot, the mathematical model of the robot is built. Then we construct the motion situational awareness map (MSAM), which can map the environment information around the robot on the MSAM. Based on the MSAM, the IAPF functions are established. We employ the rotating potential field to solve the local minima and oscillations. As for collisions between robots, we build the repulsive potential function and priority model among the robots. Afterwards, the PID adaptive tracking algorithm is utilized to multi-robot formation control. To demonstrate the validity of the proposed method, a series of simulation results confirm that the approaches proposed in this paper can successfully address the obstacle- and collision-avoidance problem while reaching formation.

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