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Direct Learning for Parameter-Varying Feedforward Control: A Neural-Network Approach
- Publication Year :
- 2023
-
Abstract
- The performance of a feedforward controller is primarily determined by the extent to which it can capture the relevant dynamics of a system. The aim of this paper is to develop an input-output linear parameter-varying (LPV) feedforward parameterization and a corresponding data-driven estimation method in which the dependency of the coefficients on the scheduling signal are learned by a neural network. The use of a neural network enables the parameterization to compensate a wide class of constant relative degree LPV systems. Efficient optimization of the neural-network-based controller is achieved through a Levenberg-Marquardt approach with analytic gradients and a pseudolinear approach generalizing Sanathanan-Koerner to the LPV case. The performance of the developed feedforward learning method is validated in a simulation study of an LPV system showing excellent performance.<br />Comment: Final author version, accepted for publication at 62nd IEEE Conference on Decision and Control, Singapore, 2023
- Subjects :
- Electrical Engineering and Systems Science - Systems and Control
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2309.12722
- Document Type :
- Working Paper