A New Discrete-time Adaptive ILC for Nonlinear Systems with Time-varying Parametric Uncertainties

Chi, Ronghu; Sui, Shulin; Hou, Zhongsheng

doi:10.3724/sp.j.1004.2008.00805

Cited by 26 publications

(5 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In comparison, the nonlinear systems may be strictly unknown in the proposed schemes while they should be uncertain in the previous works [12] and [13], and exactly known in other works [14] and [15]. In contrast to [16,17] and [18], the proposed schemes don't require any prerequisite assumptions on the systems.…”

Section: Ilc With Forgetting Factormentioning

confidence: 85%

Iterative learning control for strictly unknown nonlinear systems subject to external disturbances

Bouakrif

2011

Int. J. Control Autom. Syst.

View full text Add to dashboard Cite

This paper deals with Iterative Learning Control ILC schemes to solve the trajectory tracking problem of strictly unknown nonlinear systems subject to external disturbances, and performing repetitive tasks. Two ILC laws are presented, the first law is the high order, i.e., the information (error) of several iterations are used in the control law. The second law is the ILC with forgetting factor, i.e., the control of the preceding iteration is multiplied by a matrix of the gains. Indeed, the advantage of these algorithms, it is not only applicable for nonlinear systems with model uncertainty, but also for nonlinear systems with no data exists, neither in the structure model nor in the system parameters. In addition, the control design is very simple in the sense that there is no requirement on the choice of the learning gains. Furthermore, the convergence of our algorithms is independent of initial conditions. The asymptotic stability of the closed loop system is guaranteed. This proof is based upon the use of a Lyapunov-like positive definite sequence, which is shown to be monotonically decreasing under the proposed control schemes. Finally, simulation results on nonlinear system are provided to illustrate the effectiveness of the proposed controllers.

show abstract

Section: Ilc With Forgetting Factormentioning

confidence: 85%

Iterative learning control for strictly unknown nonlinear systems subject to external disturbances

Bouakrif

2011

Int. J. Control Autom. Syst.

View full text Add to dashboard Cite

show abstract

“…Applying the Key Technical Lemma [36] over iterations, the convergence of tracking error was demonstrated. This technique was further formalized in [37][38][39]. In [40][41][42], the problem that arises when the sign of the input coefficient is unknown is addressed.…”

Section: Introductionmentioning

confidence: 99%

Multiple Estimation Models for Discrete-time Adaptive Iterative Learning Control

Padmanabhan¹,

Makam²,

George³

2022

Preprint

View full text Add to dashboard Cite

This article presents a complete framework for multiple estimation models in the discretetime Adaptive Iterative Learning Control (ILC) problem. The use of multiple models improves transient response and error convergence in adaptive systems and has not been previously explored in the context of Adaptive ILC. A new control and identification scheme with a single estimation model is presented. This scheme enables the extension to multiple models, unlike existing Adaptive ILC strategies. Next, the fundamentals of the Multiple Models, Switching and Tuning (MMST) methodology are used for multiple estimation models in Adaptive ILC. Two switching strategies are outlined, and convergence is proved for all techniques using the properties of square-summable sequences. Simulation results demonstrate the efficacy of the proposed strategies, with improved convergence using multiple estimation models.

show abstract

“…When the initial states were random and the reference trajectory was iteration varying, the new ALIC could achieve the pointwise convergence over a finite time interval asymptotically along the iterative learning axis. A constructive discrete-time adaptive ILC approach was designed, which could perform well when the initial state value and the target trajectory were varying along the iteration axis [17,18].…”

Section: Introductionmentioning

confidence: 99%

Iterative learning control for nonlinear systems with uncertain state delay and arbitrary initial error

Hong-bin

Wang

2011

J. Control Theory Appl.

View full text Add to dashboard Cite

Most of the existing iterative learning control algorithms proposed for time-delay systems are based on the condition that the time-delay is precisely available, and the initial state is reset to the desired one or a fixed value at the start of each operation, which makes great limitation on the practical application of corresponding results. In this paper, a new iterative learning control algorithm is studied for a class of nonlinear system with uncertain state delay and arbitrary initial error. This algorithm needs to know only the boundary estimation of the state delay, and the initial state is updated, while the convergence of the system is guaranteed. Without state disturbance and output measurement noise, the system output will strictly track the desired trajectory after successive iteration. Furthermore, in the presence of state disturbance and measurement noise, the tracking error will be bounded uniformly. The convergence is strictly proved mathematically, and sufficient conditions are obtained. A numerical example is shown to demonstrate the effectiveness of the proposed approach.

show abstract

A New Discrete-time Adaptive ILC for Nonlinear Systems with Time-varying Parametric Uncertainties

Cited by 26 publications

References 9 publications

Iterative learning control for strictly unknown nonlinear systems subject to external disturbances

Iterative learning control for strictly unknown nonlinear systems subject to external disturbances

Multiple Estimation Models for Discrete-time Adaptive Iterative Learning Control

Iterative learning control for nonlinear systems with uncertain state delay and arbitrary initial error

Contact Info

Product

Resources

About