This paper integrates a previously developed iterative learning identification (ILI) (Liu, N., and Alleyne, A. G., 2016, “Iterative Learning Identification for Linear Time-Varying Systems,” IEEE Trans. Control Syst. Technol., 24(1), pp. 310–317) and iterative learning control (ILC) algorithms (Bristow, D. A., Tharayil, M., and Alleyne, A. G., 2006, “A Survey of Iterative Learning Control,” IEEE Control Syst. Mag., 26(3), pp. 96–114), into a single norm-optimal framework. Similar to the classical separation principle in linear systems, this work provides conditions under which the identification and control can be combined and guaranteed to converge. The algorithm is applicable to a class of linear time-varying (LTV) systems with parameters that vary rapidly and analysis provides a sufficient condition for algorithm convergence. The benefit of the integrated ILI/ILC algorithm is a faster tracking error convergence in the iteration domain when compared with an ILC using fixed parameter estimates. A simple example is introduced to illustrate the primary benefits. Simulations and experiments are consistent and demonstrate the convergence speed benefit.
This brief presents an approach for identifying the lateral dynamics of an automated off-highway agricultural vehicle for the purpose of automatic steering controller design. A second-order model is proposed to represent the vehicle lateral dynamics. An iterative learning identification (ILI) method is used to identify the model parameters. Simulation and experimental results under various test conditions show parameter convergence. The ILI results are compared with a gradient-based adaptive parameter estimation approach. The results highlight the practical benefit of the ILI approach for systems with repeated trajectories.
This study has proposed a method of improving vehicle ride quality capitalizing on repetitive driving. When the vehicle is driving over the same road features repetitively, a drive-history map is created, and an optimal suspension profile is generated for that road feature. When it is detected that the vehicle is travelling over the same road feature again, the optimized suspension profile is applied to correct any disturbance preemptively to optimize passengers’ experiences. The algorithm is implemented on a full-car model using Carsim, and simulation results are obtained to identify any improvements.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.