scholarly journals Application of Online Iterative Learning Tracking Control for Quadrotor UAVs

ISRN Robotics ◽  
2013 ◽  
Vol 2013 ◽  
pp. 1-20 ◽  
Author(s):  
Pong-in Pipatpaibul ◽  
P. R. Ouyang
Keyword(s):  
Tracking Control ◽  
Iterative Learning Control ◽  
Convergence Rates ◽  
Iterative Learning ◽  
Three Dimensions ◽  
Circular Trajectory ◽  
Tracking Errors ◽  
Trajectory Tracking Control ◽  
Monitoring And Surveillance ◽  
Quadrotor Unmanned Aerial Vehicles

Quadrotor unmanned aerial vehicles (UAVs) have attracted considerable interest for various applications including search and rescue, environmental monitoring, and surveillance because of their agilities and small sizes. This paper proposes trajectory tracking control of UAVs utilizing online iterative learning control (ILC) methods that are known to be powerful for tasks performed repeatedly. PD online ILC and switching gain PD online ILC are used to perform a variety of manoeuvring such as take-off, smooth translation, and various circular trajectory motions in two and three dimensions. Simulation results prove the ability and effectiveness of the online ILCs to perform successfully certain missions in the presence of disturbances and uncertainties. It also demonstrates that the switching gain PD ILC is much effective than the PD online ILC in terms of fast convergence rates and smaller tracking errors.

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.

scholarly journals Quadrotor UAV control : online learning approach

2021 ◽  
Author(s):  
Pong-In Pipatpaibul
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Iterative Learning Control ◽  
Convergence Rates ◽  
Learning Approach ◽  
Circular Trajectory ◽  
Quadrotor Uav ◽  
Simulation Results ◽  
Trajectory Tracking Controller ◽  
Gyroscopic Effects ◽  
Quadrotor Unmanned Aerial Vehicles

Quadrotor unmanned aerial vehicles (UAVs) are recognized to be capable of various tasks including search and surveillance for their agilities and small sizes. This thesis proposes a simple and robust trajectory tracking controller for a quadrotor UAV utilizing online Iterative Learning Control (ILC) that is known to be effective for tasks performed repeatedly. Based on a nonlinear model which considers basic aerogynamic and gyroscopic effects, the quadrotor UAV model is simulated to perform a variety of maneuvering such as take-off, landing, smooth translation and horizontal and spatial circular trajectory motions, PD online ILCs wirh switching gain (SPD ILCs) are studies, tested and compared. Simulation results prove the ability of the online ILCs to successfully perform certain missions in the presence of considerably large disturbances and SPD ILCs can obtain faster convergence rates.

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.

Quadrotor UAV Control: Online Learning Approach

2011 ◽  
Author(s):  
Pong-in Pipatpaibul ◽  
P. R. Ouyang
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Iterative Learning Control ◽  
Iterative Learning ◽  
Search And Rescue ◽  
Learning Approach ◽  
Circular Trajectory ◽  
Quadrotor Uav ◽  
Aerial Vehicles ◽  
Simulation Results ◽  
Quadrotor Unmanned Aerial Vehicles

Quadrotor unmanned aerial vehicles (UAVs) are recognized to be capable of various tasks including search and rescue and surveillance for their agilities and small sizes. This paper proposes a simple and robust quadrotor controller utilizing online Iterative Learning Control (ILC) that is known to be useful for tasks performed repeatedly. The controller is used for trajectory tracking to perform a variety of manoeuvring such as take-off, landing, smooth translation, and circular trajectory motion. Different online ILCs are studied and simulation results prove the ability to gain full autonomy and perform successfully certain missions in the presence of considerably large disturbances.

scholarly journals Quadrotor UAV control : online learning approach

2021 ◽  
Author(s):  
Pong-In Pipatpaibul
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Iterative Learning Control ◽  
Convergence Rates ◽  
Learning Approach ◽  
Circular Trajectory ◽  
Quadrotor Uav ◽  
Simulation Results ◽  
Trajectory Tracking Controller ◽  
Gyroscopic Effects ◽  
Quadrotor Unmanned Aerial Vehicles

Quadrotor unmanned aerial vehicles (UAVs) are recognized to be capable of various tasks including search and surveillance for their agilities and small sizes. This thesis proposes a simple and robust trajectory tracking controller for a quadrotor UAV utilizing online Iterative Learning Control (ILC) that is known to be effective for tasks performed repeatedly. Based on a nonlinear model which considers basic aerogynamic and gyroscopic effects, the quadrotor UAV model is simulated to perform a variety of maneuvering such as take-off, landing, smooth translation and horizontal and spatial circular trajectory motions, PD online ILCs wirh switching gain (SPD ILCs) are studies, tested and compared. Simulation results prove the ability of the online ILCs to successfully perform certain missions in the presence of considerably large disturbances and SPD ILCs can obtain faster convergence rates.

A Two-Step Optimization-Based Iterative Learning Control for Quadrotor Unmanned Aerial Vehicles

2021 ◽  
Vol 143 (7) ◽  
Author(s):  
Revant Adlakha ◽  
Minghui Zheng
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Design Problem ◽  
Iterative Learning Control ◽  
Design Method ◽  
Learning Control ◽  
Iterative Learning ◽  
Learning Performance ◽  
Aerial Vehicles ◽  
Quadrotor Unmanned Aerial Vehicles ◽  
Optimization Based Design

Abstract This paper presents a two-step optimization-based design method for iterative learning control and applies it onto the quadrotor unmanned aerial vehicles (UAVs) trajectory tracking problem. Iterative learning control aims to improve the tracking performance through learning from errors over iterations in repetitively operated systems. The tracking errors from previous iterations are injected into a learning filter and a robust filter to generate the learning signal. The design of the two filters usually involves nontrivial tuning work. This paper presents a new two-optimization design method for the iterative learning control, which is easy to obtain and implement. In particular, the learning filter design problem is transferred into a feedback controller design problem for a purposely constructed system, which is solved based on H-infinity optimal control theory thereafter. The robust filter is then obtained by solving an additional optimization to guarantee the learning convergence. Through the proposed design method, the learning performance is optimized and the system's stability is guaranteed. The proposed two-step optimization-based design method and the regarding iterative learning control algorithm are validated by both numerical and experimental studies.

scholarly journals Iterative Learning Control for Linear Discrete-Time Systems with Randomly Variable Input Trail Length

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-6
Author(s):  
Yun-Shan Wei ◽  
Qing-Yuan Xu
Keyword(s):  
Discrete Time ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Input ◽  
Initial State ◽  
Tracking Errors ◽  
Discrete Time Systems ◽  
P Type ◽  
Time Systems

For linear discrete-time systems with randomly variable input trail length, a proportional- (P-) type iterative learning control (ILC) law is proposed. To tackle the randomly variable input trail length, a modified control input at the desirable trail length is introduced in the proposed ILC law. Under the assumption that the initial state fluctuates around the desired initial state with zero mean, the designed ILC scheme can drive the ILC tracking errors to zero at the desirable trail length in expectation sense. The designed ILC algorithm allows the trail length of control input which is different from system state and output at a specific iteration. In addition, the identical initial condition widely used in conventional ILC design is also mitigated. An example manifests the validity of the proposed ILC algorithm.

Sign in / Sign up

Export Citation Format

Share Document