NMPC of a Continuous Fermenter Using Wiener-Hammerstein Model Developed from Irregularly Sampled Multi-Rate Data
نویسندگان
چکیده
Control of a bio-reactor is a complex task due to inherent non-linearities and unavailability of measurements of the quality variables at regular sampling intervals. In this work, it proposed to identify Wiener-Hammerstein type fast-rate time series models for the quality variables directly from the irregularly sampled multi-rate input-output data. The identified models are further used to develop a multi-rate nonlinear predictive controller. The efficacy of the proposed modelling and control scheme is demonstrated by conducting simulation studies on a continuous fermenter system that exhibits input multiplicity and gain reversal in the desired operating region.
منابع مشابه
Parameter estimation of the fractional-order Hammerstein–Wiener model using simplified refined instrumental variable fractional-order continuous time
This study proposes a direct parameter estimation approach from observed input–output data of a stochastic singleinput–single-output fractional-order continuous-time Hammerstein–Wiener model by extending a well known iterative simplified refined instrumental variable method. The method is an extension of the simplified refined instrumental variable method developed for the linear fractional-ord...
متن کاملIdentification of Noise Covariances for State Estimation of a Continuous Fermenter Using Extended EM Algorithm
Despite developments in sensor technology, monitoring a biological process using regular sensor measurements is often difficult. Development of Bayesian state observers, such as extended Kalman filter(EKF), is an attractive alternative for soft-sensing of such complex systems. The performance of EKF is dependent on the accurate characterisation of the uncertainties in the state dynamics and in ...
متن کاملNonlinear System Identification Using Hammerstein-Wiener Neural Network and subspace algorithms
Neural networks are applicable in identification systems from input-output data. In this report, we analyze theHammerstein-Wiener models and identify them. TheHammerstein-Wiener systems are the simplest type of block orientednonlinear systems where the linear dynamic block issandwiched in between two static nonlinear blocks, whichappear in many engineering applications; the aim of nonlinearsyst...
متن کاملA New Fault Tolerant Nonlinear Model Predictive Controller Incorporating an UKF-Based Centralized Measurement Fusion Scheme
A new Fault Tolerant Controller (FTC) has been presented in this research by integrating a Fault Detection and Diagnosis (FDD) mechanism in a nonlinear model predictive controller framework. The proposed FDD utilizes a Multi-Sensor Data Fusion (MSDF) methodology to enhance its reliability and estimation accuracy. An augmented state-vector model is developed to incorporate the occurred senso...
متن کاملHammerstein-Wiener Model: A New Approach to the Estimation of Formal Neural Information
A new approach is introduced to estimate the formal information of neurons. Formal Information, mainly discusses about the aspects of the response that is related to the stimulus. Estimation is based on introducing a mathematical nonlinear model with Hammerstein-Wiener system estimator. This method of system identification consists of three blocks to completely describe the nonlinearity of inp...
متن کامل