CEF Techniques for Uncertain Systems with Partial Structure Information
Abstract
This chapter considers the adaptive iterative learning control (ILC) for continuous-time parametric nonlinear systems with partial structure information under iteration-varying trial length environments. In particular, two types of partial structure information are taken into account. The first type is that the parametric system uncertainty can be separated as a combination of time-invariant and time-varying part. The second type is that the parametric system uncertainty mainly contains time-invariant part, whereas the designed algorithm is expected to deal with certain unknown time-varying uncertainties. A mixing-type adaptive learning scheme and a hybrid-type differential-difference learning scheme are proposed for the two types of partial structure information cases, respectively. The convergence analysis under iteration-varying trial length environments is strictly derived based on a novel composite energy function. Illustrative simulations are provided to verify the effectiveness of the proposed schemes.
References
- 1.Sun M, Wang D (2002) Iterative learning control with initial rectifying action. Automatica 38(7):1177–1182MathSciNetCrossRefGoogle Scholar
- 2.Xu JX, Yan R (2005) On initial conditions in iterative learning control. IEEE Trans Autom Control 50(9):1349–1354MathSciNetCrossRefGoogle Scholar
- 3.Zeng C, Shen D, Wang J (2018) Adaptive learning tracking for uncertain systems with partial structure information and varying trial lengths. J Frankl Inst 355(15):7027–7055MathSciNetCrossRefGoogle Scholar