Automated generation of feedforward control using feedback linearization of local model networks

An effective but yet simple approach is introduced to automatically attain a dynamic feedforward control law for non-linear dynamic systems represented by discrete-time local model networks (LMN). In this context, feedback linearization is applied to the generic model structure of LMN and the resulting input transformation is used as model inverse. This general and automated approach for model inversion is applicable even when the overall model complexity may be high. Thus, by representing a non-linear dynamic system by an LMN and applying the proposed feedforward control law generation, a dynamic feedforward control for such a non-linear system can be found automatically with the knowledge of measured input-output data only. However, when feedback linearization is considered, the stability of the internal dynamics plays a key role. This paper analyses the occurring internal dynamics for LMN, which directly result from the chosen model structure in identification, and discusses the effects on the transformed system. Finally, the effectiveness of the proposed data-driven feedforward control is demonstrated by a simulation example as well as by an actual application to the pre-distortion of a microelectromechanical systems (MEMS) loudspeaker with electrostatic actuation.