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 An Output-Recurrent-Neural-Network-Based Iterative Learning Control for Unknown Nonlinear Dynamic Plants

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Ying-Chung Wang ◽  
Chiang-Ju Chien
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Iterative Learning Control ◽  
Design Method ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Learning Performance ◽  
Adaptive Laws ◽  
Control Objective

We present a design method for iterative learning control system by using an output recurrent neural network (ORNN). Two ORNNs are employed to design the learning control structure. The first ORNN, which is called the output recurrent neural controller (ORNC), is used as an iterative learning controller to achieve the learning control objective. To guarantee the convergence of learning error, some information of plant sensitivity is required to design a suitable adaptive law for the ORNC. Hence, a second ORNN, which is called the output recurrent neural identifier (ORNI), is used as an identifier to provide the required information. All the weights of ORNC and ORNI will be tuned during the control iteration and identification process, respectively, in order to achieve a desired learning performance. The adaptive laws for the weights of ORNC and ORNI and the analysis of learning performances are determined via a Lyapunov like analysis. It is shown that the identification error will asymptotically converge to zero and repetitive output tracking error will asymptotically converge to zero except the initial resetting error.

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 Iterative learning control with applications in energy generation, lasers and health care

2016 ◽  
Vol 472 (2193) ◽  
pp. 20150569 ◽  
Author(s):  
E. Rogers ◽  
O. R. Tutty
Keyword(s):  
Health Care ◽  
Iterative Learning Control ◽  
Design Method ◽  
Learning Control ◽  
Iterative Learning ◽  
Free Electron Lasers ◽  
Time Duration ◽  
Physical Systems ◽  
Mode Of Operation ◽  
Duration Task

Many physical systems make repeated executions of the same finite time duration task. One example is a robot in a factory or warehouse whose task is to collect an object in sequence from a location, transfer it over a finite duration, place it at a specified location or on a moving conveyor and then return for the next one and so on. Iterative learning control was especially developed for systems with this mode of operation and this paper gives an overview of this control design method using relatively recent relevant applications in wind turbines, free-electron lasers and health care, as exemplars to demonstrate its applicability.

An Iterative Learning Control for Uncertain Systems Using Structured Singular Value

1999 ◽  
Vol 121 (4) ◽  
pp. 660-667 ◽  
Author(s):  
Tae-Yong Doh ◽  
Jung-Ho Moon ◽  
Myung Jin Chung
Keyword(s):  
Robust Stability ◽  
Iterative Learning Control ◽  
Design Method ◽  
Learning Control ◽  
Convergence Condition ◽  
Singular Value ◽  
Iterative Learning ◽  
Structured Singular Value ◽  
Plant Uncertainty ◽  
Uncertain Linear Systems

To deal with an iterative learning control (ILC) system with plant uncertainty, a set of new terms related with robust convergence is first defined. This paper proposes a sufficient condition for not only robust convergence but also robust stability of ILC for uncertain linear systems, including plant uncertainty. Thus, to find a new condition unrelated to the uncertainty, we first separate it into a known part and uncertainty one using linear fractional transformations (LFTs). Then, robust convergence and robust stability of an ILC system is determined by structured singular value (μ) of only the known part. Based on the novel condition, a learning controller and a feedback controller are developed at the same time to ensure robust convergence and robust stability of the ILC system under plant uncertainty. Lastly, the feasibility of the proposed convergence condition and design method are confirmed through computer simulation on an one-link flexible arm.

Iterative Learning Control for Hybrid Systems

2020 ◽  
Author(s):  
Kirti D. Mishra ◽  
K. Srinivasan
Keyword(s):  
Hybrid Systems ◽  
Iterative Learning Control ◽  
Design Method ◽  
Tracking Error ◽  
Control Design ◽  
Learning Control ◽  
Iterative Learning ◽  
Linear Matrix ◽  
Triangular Structure ◽  
Form Representation

Abstract Iterative learning control (ILC) has been growing in applicability, along with growth in theory for classes of linear and nonlinear systems, and this study extends the theory of ILC to hybrid systems. A lifted form representation of hybrid systems with input-output dependent switching rules is developed, and the proposed lifted form representation is modeled as a switched system with arbitrary/unconstrained switching rules in the trial domain for control design. The causality of hybrid systems in the time domain results in the (lower) triangular structure of switched systems in the trial domain, the triangular structure enabling systematic and efficient control design. A unique aspect of the control design method developed for ILC of hybrid systems in this study is that a solution to the required set of linear matrix inequalities (LMIs) is guaranteed to exist under mild assumptions, which is in contrast to many other studies proposing LMI based solutions in controls literature. The proposed method is validated numerically for a motion control application, and robust and monotonic convergence of the tracking error to zero is demonstrated.

Discrete Fourier Transform Based Iterative Learning Control Design for Linear Plants With Experimental Verification

2009 ◽  
Vol 131 (3) ◽  
Author(s):  
C. T. Freeman ◽  
P. L. Lewin ◽  
E. Rogers ◽  
D. H. Owens ◽  
J. J. Hatonen
Keyword(s):  
Fourier Transform ◽  
Discrete Fourier Transform ◽  
Experimental Verification ◽  
Iterative Learning Control ◽  
Design Method ◽  
Control Design ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Algorithms ◽  
Straightforward Manner

This paper considers the design of linear iterative learning control algorithms using the discrete Fourier transform of the measured impulse response of the system or plant under consideration. It is shown that this approach leads to a transparent design method whose performance is then experimentally benchmarked on an electromechanical system. The extension of this approach to the case when there is uncertainty associated with the systems under consideration is also addressed in both algorithm development and experimental benchmarking terms. The robustness results here have the applications oriented benefit of allowing the designer to manipulate the convergence and robustness properties of the algorithm in a straightforward manner.

Fuzzy Iterative Learning Control of Servo System

2011 ◽  
Vol 217-218 ◽  
pp. 917-923
Author(s):  
Hong Li Liu ◽  
Qi Xin Zhu ◽  
Xiang Wen Chen
Keyword(s):  
Fuzzy Control ◽  
Iterative Learning Control ◽  
Fuzzy Controller ◽  
Design Method ◽  
Servo System ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Input ◽  
Control Performance ◽  
System A

According to the repeatability characteristics of servo system, a fuzzy iterative learning controller is proposed which combining advantages of fuzzy control and iterative learning control. Fuzzy control has better robustness, and does not require accurate model of the system, only need the previous experience, the design method is simple. Fuzzy controller is used in the position-loop, but the static error is difficult to eliminate, iterative learning controller use the control error to adjust the previous control input to reduce the error of next time. The combination of these two intelligent control algorithms can improve the control performance of servo system effectively. Simulation results show that the fuzzy control combined with iterative learning control can achieve better control performance in servo system.

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