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

2020 ◽  
Author(s):  
Mohammad Mehdi FARZANEH ◽  
Alireza TAVAKOLPOUR-SALEH
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Learning Algorithm ◽  
Iterative Learning ◽  
Trajectory Tracking Control

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.

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.

Iterative Learning Control With Switching Gain Feedback for Nonlinear Systems

2010 ◽  
Vol 6 (1) ◽  
Author(s):  
P. R. Ouyang ◽  
B. A. Petz ◽  
F. F. Xi
Keyword(s):  
Feedback Control ◽  
Nonlinear Systems ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Trajectory Tracking Control ◽  
Control Scheme

Iterative learning control (ILC) is a simple and effective technique of tracking control aiming at improving system tracking performance from trial to trial in a repetitive mode. In this paper, we propose a new ILC called switching gain PD-PD (SPD-PD)-type ILC for trajectory tracking control of time-varying nonlinear systems with uncertainty and disturbance. In the developed control scheme, a PD feedback control with switching gains in the iteration domain and a PD-type ILC based on the previous iteration combine together into one updating law. The proposed SPD-PD ILC takes the advantages of feedback control and classical ILC and can also be viewed as online-offline ILC. It is theoretically proven that the boundednesses of the state error and the final tracking error are guaranteed in the presence of uncertainty, disturbance, and initialization error of the nonlinear systems. The convergence rate is adjustable by the adoption of the switching gains in the iteration domain. Simulation experiments are conducted for trajectory tracking control of a nonlinear system and a robotic system. The results show that fast convergence and small tracking error bounds can be observed by using the SPD-PD-type ILC.

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