Non-linear model order reduction using input to state Hammerstein structures

AbstractIn this paper, the focus will be on approximating original model of process systems using block-structured models. The context of model reduction is to improve the computational efficiency (simulation time). The reduced order models are important for online applications. Hammerstein structures have been used to approximate a mathematical non-linear model of a process. Input-Output Hammerstein structure can be defined as classical Hammerstein model but the technique is extended here to Input-State Hammerstein structure. It is shown that Input-State Hammerstein structure can be derived from Taylor series. Approximation accuracy has been improved by approximation for second term. The approximated Input-state Hammerstein block structure model gives good approximation of the original non-linear system. Over an operating domain of a process, the Input-State Hammerstein structure provides opportunities for reducing the computational load by order reduction of states and Jacobians. The methodology has been applied to a high purity distillation benchmark and satisfactory results are obtained as far as approximation is concerned. Reduction in states and Jacobian size by 70% is attained.