Optimal Iterative Learning Control Approach Research Articles

An Optimal Iterative Learning Control Approach for Linear Systems With Nonuniform Trial Lengths Under Input Constraints

In practical applications of iterative learning control (ILC), the repetitive process may end up early by accident during the performance improvement along the trial axis, which yields the nonuniform trial length problem. For such practical systems, input signals are usually constrained because of some certain physical limitations. This article proposes an optimal ILC algorithm for linear time-invariant multiple-input–multiple-output (MIMO) systems with nonuniform trial lengths under input constraints. The optimal ILC framework is specifically modified for the nonuniform trial length problem, where the primal–dual interior point method is introduced to deal with the input constraints. Hence, the constraint handling capability are improved compared with the conventional counterparts for nonuniform trial lengths. Also, the monotonic convergence property of the proposed optimal ILC algorithm is obtained in the sense of mathematical expectation. Finally, the effectiveness of the proposed algorithm is verified on the numerical simulation of a mobile robot.

On the Way to Purge Volume Control during Purge Processes of a Fuel Cell System by a Predictive Control Strategy

This paper deals with an Optimal Iterative Learning Control approach for the anode pressure during the periodic purge processes of a fuel cell system which is developed to a Predictive Control Strategy under consideration of the correct purge volume. Due to accumulation of diffused nitrogen and water condensate inside the anode volume the chemical reaction is restrained. This adverse effect is avoided through the purge process, by which a short-time opening of the exhaust valve forces the nitrogen and water out of the system. Unfortunately, the opening leads to a pressure drop along the anode volume that causes a force to the membrane. To avoid this mechanical stress the control aim is a constant anode pressure during the purge process by supplying additional hydrogen. Therefore, first an Optimal Iterative Learning Control approach is introduced. Due to an influence of the Optimal Iterative Learning Control on the purge volume, a correction of the purge time interval is applied for a constant purge volume. In the last step a Nonlinear Model Predictive Control approach is presented which allows a correction of the purge volume during load changes as well.

Optimal iterative learning control for a class of non-minimum phase systems

In this paper, an optimal iterative learning control (ILC) approach is proposed for a class of repetitive non-minimum phase (NMP) systems. The control law synthesis is based on the resolution of a quadratic criterion which minimises the errors between the setpoint references and the system outputs at each iteration for each trial. The resolution of the control problem uses a new gain which avoids matricial inversion problems appearing in classical ILC algorithms such as direct model inversion (I-ILC) and optimal ILC (Q-ILC). The new optimal ILC approach improves the learning convergence significantly compared to the previously mentioned algorithms. Furthermore, sufficient and necessary stability conditions are established with convergence properties. The effectiveness of the proposed method is proved by simulations with an NMP mass-spring damper system.

A Cross-Coupled Non-lifted Norm Optimal Iterative Learning Control Approach with Application to a Multi-axis Robotic Testbed

This paper combines previously developed non-lifted norm optimal iterative learning control (NNOILC)with a cross coupled formulation resulting in a cross-coupled non-lifted norm optimal iterative learning control (cross-coupled NNOILC). The objective is to improve the contour tracking performance in precision motion control of multi-axis systems while retaining the computational efficiency properties of the NNOILC. The NNOILC is able to provide many of the same design advantages of norm optimal ILC (NOILC) without the restrictions on trial size. Convergence and robustness properties are provided and shown to be similar to previous efforts in NNOILC. To demonstrate the proposed approach, experiments on a multi-axis robotic testbed are given.

A computationally efficient norm optimal iterative learning control approach for LTV systems

This paper proposes a computationally efficient iterative learning control (ILC) approach termed non-lifted norm optimal ILC (N-NOILC). The objective is to remove the computational complexity issues of previous 2-norm optimal ILC approaches, which are based on lifted system techniques, while retaining the iteration domain convergence properties. The computational complexity needed to implement the proposed method scales linearly with the trial length. Therefore, the approach can be implemented on controlled processes having long trial durations and high sampling rates. Robustness is accomplished by adding a penalty term on the control input in the cost function. Simulations are presented to verify and validate the features of the proposed method.