Distributed Parameter Bioprocess Plant Identification and I-Term Control Using Decentralized Fuzzy-Neural Multi-Models

In the last decade, the Computational Intelligence tools (CI), including Artificial Neural Networks (ANN) and Fuzzy Systems (FS), applying soft computing, became universal means for many applications. Because of their approximation and learning capabilities, the ANNs have been widely employed to dynamic process modeling, identification, prediction and control, (Boskovic & Narendra, 1995; Haykin, 1999; Bulsari & Palosaari, 1993; Deng & Li, 2003; Deng et al., 2005; Gonzalez-Garcia et al., 1998; Padhi & Balakrishnan, 2003; Padhi et al., 2001; Ray,1989). Many applications have been done for identification and control of biotechnological plants too, (Padhi et al., 2001). Among several possible neural network architectures the ones most widely used are the Feedforward NN (FFNN) and the Recurrent NN (RNN), (Haykin, 1999). The main NN property namely the ability to approximate complex non-linear relationships without prior knowledge of the model structure makes them a very attractive alternative to the classical modeling and control techniques. Also, a great boost has been made in the applied NN-based adaptive control methodology incorporating integral plus state control action in the control law, (Baruch et al., 2004; Baruch & Garrido, 2005; Baruch et al., 2007). The FFNN and the RNN have been applied for Distributed Parameter Systems (DPS) identification and control too. In (Pietil & Koivo, 1996), a RNN is used for system identification and process prediction of a DPS dynamics an adsorption column for wastewater treatment of water contaminated with toxic chemicals. In (Deng & Li, 2003; Deng et al., 2005) a spectral-approximation-based intelligent modeling approach, including NNs for state estimation and system identification, is proposed for the distributed thermal processing of the snap curing oven DPS that is used in semiconductor packaging industry. In (Bulsari & Palosaari, 1993), it is presented a new methodology for the identification of DPS, based on NN architectures, motivated by standard numerical discretization techniques used for the solution of Partial Differential Equation (PDE). In (Padhi & Balakrishnan, 2003), an attempt is made to use the philosophy of the NN adaptivecritic design to the optimal control of distributed parameter systems. In (Padhi et al., 2001) the concept of proper orthogonal decomposition is used for the model reduction of DPS to form a reduced order lumped parameter problem. The optimal control problem is then solved in the time domain, in a state feedback sense, following the philosophy of adaptive critic NNs. The control solution is then mapped back to the spatial domain using the same basis functions. In (Pietil & Koivo, 1996), measurement data of an industrial process are