Iterative Learning Control of Constrained Systems With Varying Trial Lengths Under Alignment Condition
Mouquan Shen, Xingzheng Wu, Ju H. Park, Yang Yi, Yonghui Sun
Abstract
This brief is concerned with iterative learning control (ILC) of constrained multi-input multi-output (MIMO) nonlinear systems under the state alignment condition with varying trial lengths. A modified reference trajectory is constructed to meet the alignment condition by adjusting the reference trajectory to be spatially closed. Resorting to the barrier composite energy function (BCEF) approach, an adaptive ILC scheme is built to guarantee the bounded convergence of the resultant closed-loop system. Illustrative examples are presented to verify the validity of the proposed iteration scheme.
Topics & Concepts
Iterative learning controlControl theory (sociology)TrajectoryConvergence (economics)Bounded functionNonlinear systemComputer scienceScheme (mathematics)Mathematical optimizationMIMOControl (management)MathematicsArtificial intelligenceMathematical analysisAstronomyEconomic growthQuantum mechanicsChannel (broadcasting)EconomicsComputer networkPhysicsIterative Learning Control SystemsAdvanced machining processes and optimizationAdvanced Surface Polishing Techniques