Iterative learning control of inhomogeneous distributed parameter systems—frequency domain design and analysis

Huang, Deqing; Li, Xuefang; Xu, Jian‐Xin; Xu, Chao; He, Wei

doi:10.1016/j.sysconle.2014.08.003

Cited by 55 publications

(18 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many engineers pay attention to study ILC for various distributed parameter systems, one can refer to [11,12] and reference therein. Recently, Huang et al [13] study P-type and D-type ILC for linear parabolic distributed parameter systems in the sense of sup-norm. Guo et al [14] study ILC for a class of nonaffine-in-input processes in Hilbert space, where the plant operators are quite general in the sense that they could be static or dynamic, differentiable, or non-differentiable, continuous-time or discrete-time, and so forth.…”

Section: Introductionmentioning

confidence: 99%

On the iterative learning control of fractional impulsive evolution equations in Banach spaces

Debbouche

Wang

2015

Math Methods in App Sciences

View full text Add to dashboard Cite

In this paper, we study P‐type, PIα‐type, and D‐type iterative learning control for fractional impulsive evolution equations in Banach spaces. We present triple convergence results for open‐loop iterative learning schemes in the sense of λ‐norm through rigorous analysis. The proposed iterative learning control schemes are effective to fractional hybrid infinite‐dimensional distributed parameter systems. Finally, an example is given to illustrate our theoretical results. Copyright © 2015 John Wiley & Sons, Ltd.

show abstract

Section: Introductionmentioning

confidence: 99%

On the iterative learning control of fractional impulsive evolution equations in Banach spaces

Debbouche

Wang

2015

Math Methods in App Sciences

View full text Add to dashboard Cite

show abstract

“…When the fractional order α = 1, the fractional order distributed parameter System (1) is reduced to an integral order one, which has been widely investigated in [11,[16][17][18][19].…”

Section: Remarkmentioning

confidence: 99%

“…A D-type anticipatory ILC scheme was applied to the boundary control of a class of inhomogeneous heat equations in [17]. In [18], a framework in the frequency domain of ILC was designed for linear inhomogeneous distributed parameter systems. By using the Gronwall-Bellman inequality, a close-loop P-type iterative learning algorithm is proposed for linear parabolic distributed parameter systems in [19].…”

Section: Introductionmentioning

confidence: 99%

ILC with Initial State Learning for Fractional Order Linear Distributed Parameter Systems

Lan

Cui

2018

Algorithms

View full text Add to dashboard Cite

This paper presents a second order P-type iterative learning control (ILC) scheme with initial state learning for a class of fractional order linear distributed parameter systems. First, by analyzing the control and learning processes, a discrete system for P-type ILC is established, and the ILC design problem is then converted to a stability problem for such a discrete system. Next, a sufficient condition for the convergence of the control input and the tracking errors is obtained by introducing a new norm and using the generalized Gronwall inequality, which is less conservative than the existing one. Finally, the validity of the proposed method is verified by a numerical example.

show abstract

“…Some research used the nature of vehicles combined with filters to design a passive controller which cannot deal with external disturbances [14]. Iterative learning control derives more accurate model parameters through iterative learning in the presence of complicated motion [15], [16]. He et al [17] considered a flexible micro aerial vehicle with spationtemporally varying disturbances and designed two iterative learning control schemes to realize tracking a prescribed trajectory.…”

Section: Introductionmentioning

confidence: 99%

Active Disturbance Rejection Attitude Control for Flapping Wing Micro Aerial Vehicle With Nonaffine-in-Control Characteristics

Feng

Wang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

This study presents attitude control research of flapping wing micro aerial vehicles with nonaffine-in-control characteristics via the active disturbance rejection control technique. With consideration of the uncertainties in moment of inertia caused by wings flapping and external disturbances, an extended state observer based on the unit quaternion is first designed to estimate the angular velocity and total uncertainties. The norm constraint on the unit quaternion can be guaranteed theoretically. The requirement of accurate value of moment of inertia is circumvented in the extended state observer. Based on the designed extended state observer, an output feedback controller using dynamic inversion method is presented to resolve the nonaffine-in-control characteristics caused by the uncertainties in moment of inertia. Rigorous closed-loop system stability is proven by employment of Lyapunov functions and the proposed control structure possesses the multi-timescale property. Finally, numerical simulations are provided to validate the effectiveness and good tracking performance of the proposed control scheme. INDEX TERMS Active disturbance rejection control, dynamic inversion control, extended state observer, flapping wing micro aerial vehicle, nonaffine-in-control.

show abstract

Iterative learning control of inhomogeneous distributed parameter systems—frequency domain design and analysis

Cited by 55 publications

References 22 publications

On the iterative learning control of fractional impulsive evolution equations in Banach spaces

On the iterative learning control of fractional impulsive evolution equations in Banach spaces

ILC with Initial State Learning for Fractional Order Linear Distributed Parameter Systems

Active Disturbance Rejection Attitude Control for Flapping Wing Micro Aerial Vehicle With Nonaffine-in-Control Characteristics

Contact Info

Product

Resources

About