Trajectory Tracking Control of Robotic Manipulator via Variable Gain Iterative Learning Algorithm

2018 ◽  
Author(s):  
Haonan Zheng ◽  
Yanhong Liu ◽  
Chao Li ◽  
Guokang Wang
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Learning Algorithm ◽  
Robotic Manipulator ◽  
Iterative Learning ◽  
Trajectory Tracking Control ◽  
Variable Gain

scholarly journals Adaptive Trajectory Tracking Control of a Quadrotor Based on Iterative Learning Algorithm

2018 ◽  
Author(s):  
Mohammad Mehdi Farzaneh ◽  
Alireza Tavakolpour-Saleh
Keyword(s):  
Trajectory Tracking ◽  
Pid Control ◽  
Tracking Control ◽  
Control Method ◽  
Learning Algorithm ◽  
Optimal Algorithm ◽  
Adaptive Method ◽  
Iterative Learning ◽  
Trajectory Tracking Control ◽  
Control Loops

This paper presents a new adaptive and optimal algorithm for the trajectory tracking control of a quadrotor using iterative learning algorithm (ILA) and enumerative learning algorithm. Ordinarily the ILA, as an adaptive method, can perform well with PID control to improve the controller’s performance for a nonlinear system. Quadrotors are considered as non-linear and unstable systems which the use of an adaptive and optimal controller can increase its stability and decrease error level. In this method, a PID controller is proposed for the outer and inner control loops of a quadrotor and the ILA is used to adapt PID control gains. Subsequently, an enumerative learning algorithm is used to optimize the learning rates of the ILA. For this purpose, at first, the dynamic model of the quadrotor is acquired. After that, the structure of the inner and outer control loops is defined. In the end, the simulation results for the trajectory tracking control of a quadrotor are demonstrated. Through simulation, it is concluded that as time increases, the performance of the suggested control method in trajectory tracking control becomes better and better and error signals convergence to zero.

Rapid iterative learning algorithm of nonlinear time-delay system with initial deviation

2020 ◽  
pp. 002072092094057
Author(s):  
Xiaoyan Cheng ◽  
Hongbin Wang ◽  
Qinzhao Wang ◽  
Shaochan Feng
Keyword(s):  
Time Delay ◽  
Trajectory Tracking ◽  
Learning Algorithm ◽  
Tracking Error ◽  
Iterative Learning ◽  
Forgetting Factor ◽  
Initial State ◽  
Trajectory Tracking Control ◽  
Joint Rotation ◽  
Variable Forgetting Factor

A rapid iterative learning control algorithm with variable forgetting factor is applied for a class of nonlinear system with initial error and time-delay. This algorithm eliminats the limitation that the initial state should be reset to the expected one or fixed value at the start of iteration in the learning process of conventional algorithms. The error and the differences between two adjacent error is adopted to correct the controller avoiding the unstable influence of the derivative for PD type algorithm and the available information is fully used to increase convergence rate. Furthermore variable forgetting factor introduced guaranteed a fast convergence of trajectory tracking error Then, with applying the rapid algorithm to the trajectory tracking control of manipulator, the learning speed and tracking performance are both greatly improved. Meanwhile, the control strategy is proposed for the limitation of each joint rotation. The convergence of the method is also proved theoretically. Finally, simulation results illustrates the effectiveness and the real-time ability of the proposed way.

Theoretical Analysis and Experimental Study on Automatic Trajectory Tracking Control of a Shield Tunneling Machine under Pressure Regulation Working Mode

2011 ◽  
Vol 317-319 ◽  
pp. 1444-1451
Author(s):  
Hai Bo Xie ◽  
Xiao Ming Duan ◽  
Hua Yong Yang ◽  
Zhi Bin Liu
Keyword(s):  
Control System ◽  
Working Conditions ◽  
Trajectory Tracking ◽  
Pid Control ◽  
Tracking Control ◽  
Shield Tunneling ◽  
Trajectory Tracking Control ◽  
Variable Gain ◽  
Thrust System ◽  
Tunneling Machine

Hydraulic thrust system is a critical part of shield tunneling machine. Automatic trajectory tracking control is a significant task of thrust system during tunnel excavation. In this article, plane mechanical structure diagram of the thrust system and path planning method are illustrated at first. An integrated control system is proposed to achieve the automatic control of the thrust trajectory. The control system consists of one trajectory planning controller for both cylinders and an individual cylinder controller for each of hydraulic cylinders. Trajectory planning controller is used to generate respective displacement signals of double-cylinder in every thrust stroke and each of cylinder controllers is used to realize the precise control of the given thrust trajectory. Variable-gain PID control strategy applied to achieve the precise tracking control of thrust trajectory under several typical working conditions are done at last. The experimental results demonstrate that variable-gain PID control have good performances with short response time and small overshoot regardless of changes of working conditions.

Iterative learning control for a distributed cloud robot with payload delivery

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jiehao Li ◽  
Shoukun Wang ◽  
Junzheng Wang ◽  
Jing Li ◽  
Jiangbo Zhao ◽  
...  
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Robot System ◽  
Trajectory Tracking Control ◽  
Content Type ◽  
Motion Models ◽  
Distributed Cloud

Purpose When it comes to the high accuracy autonomous motion of the mobile robot, it is challenging to effectively control the robot to follow the desired trajectory and transport the payload simultaneously, especially for the cloud robot system. In this paper, a flexible trajectory tracking control scheme is developed via iterative learning control to manage a distributed cloud robot (BIT-6NAZA) under the payload delivery scenarios. Design/methodology/approach Considering the relationship of six-wheeled independent steering in the BIT-6NAZA robot, an iterative learning controller is implemented for reliable trajectory tracking with the payload transportation. Meanwhile, the stability analysis of the system ensures the effective convergence of the algorithm. Findings Finally, to evaluate the developed method, some demonstrations, including the different motion models and tracking control, are presented both in simulation and experiment. It can achieve flexible tracking performance of the designed composite algorithm. Originality/value This paper provides a feasible method for the trajectory tracking control in the cloud robot system and simultaneously promotes the robot application in practical engineering.

A New Neural Network Based Sliding Mode Adaptive Controller for Tracking Control of Robot Manipulator

2021 ◽  
Vol 11 (2) ◽  
pp. 12-16
Author(s):  
Monisha Pathak ◽  
◽  
Mrinal Buragohain ◽  
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Rbf Neural Network ◽  
Adaptive Controller ◽  
Robotic Manipulator ◽  
Trajectory Tracking Control ◽  
Chattering Effect

In this paper a New RBF Neural Network based Sliding Mode Adaptive Controller (NNNSMAC) for Robot Manipulator trajectory tracking in the presence of uncertainties and disturbances is introduced. The research offers a learning with minimal parameter (LMP) technique for robotic manipulator trajectory tracking. The technique decreases the online adaptive parameters number in the RBF Neural Network to only one, lowering computational costs and boosting real-time performance. The RBFNN analyses the system's hidden non-linearities, and its weight value parameters are updated online using adaptive laws to control the nonlinear system's output to track a specific trajectory. The RBF model is used to create a Lyapunov function-based adaptive control law. The effectiveness of the designed NNNSMAC is demonstrated by simulation results of trajectory tracking control of a 2 dof Robotic Manipulator. The chattering effect has been significantly reduced.

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