Linear controller design: limits of performance
Linear controller design: limits of performance
State-space interpretation of model predictive control
Automatica (Journal of IFAC)
Iterative Learning Control for Deterministic Systems
Iterative Learning Control for Deterministic Systems
International Journal of Systems Science
Automatica (Journal of IFAC)
Brief paper: Iterative Learning Control for uncertain systems: Robust monotonic convergence analysis
Automatica (Journal of IFAC)
Norm optimal ILC with time-varying weighting matrices
ACC'09 Proceedings of the 2009 conference on American Control Conference
Iterative learning control using a basis signal library
ACC'09 Proceedings of the 2009 conference on American Control Conference
Performing aggressive maneuvers using iterative learning control
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
A novel neuro-fuzzy model-based run-to-run control for batch processes with uncertainties
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Iterative learning control of a crystallisation process using batch wise updated linearised models
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Performing and extending aggressive maneuvers using iterative learning control
Robotics and Autonomous Systems
Brief paper: Linear computational complexity robust ILC for lifted systems
Automatica (Journal of IFAC)
Brief On the design of ILC algorithms using optimization
Automatica (Journal of IFAC)
Model reduction in model predictive control of combined water quantity and quality in open channels
Environmental Modelling & Software
A computationally efficient norm optimal iterative learning control approach for LTV systems
Automatica (Journal of IFAC)
| Hi-index | 22.16 |
In this paper, iterative learning control (ILC) based on a quadratic performance criterion is revisited and generalized for time-varying linear constrained systems with deterministic, stochastic disturbances and noises. The main intended area of application for this generalized method is chemical process control, where excessive input movements are undesirable and many process variables are subject to hard constraints. It is shown that, within the framework of the quadratic-criterion-based ILC (Q-ILC), various practical issues such as constraints, disturbances, measurement noises, and model errors can be considered in a rigorous and systematic manner. Algorithms for the deterministic case, the stochastic case, and the case with bounded parameter uncertainties are developed and relevant properties such as the asymptotic convergence are established under some mild assumptions. Numerical examples are provided to demonstrate the performance of the proposed algorithms.