Identification of low-complexity LPV input-output models for control of a turbocharged combustion engine
Complexity issues related to experimentally identified LPV models are addressed, in particular the trade-off between model accuracy and number of scheduling parameters. For this purpose, an existing identification algorithm for LPV input-output models is combined with a parameter reduction technique based on principal component analysis. The approach is illustrated with experimental results on control of a turbocharged combustion engine. A low-complexity LPV input-output model is identified and validated. After transforming this model into state-space form-taking dynamic dependence on scheduling parameters into account-an LPV gain-scheduling controller is designed and assessed in closed-loop simulation with a validated nonlinear model.