A direct adaptive neural control scheme with integral terms

Abstract

A direct adaptive neural control scheme with single and double integral-plus-state (IPS) actions is proposed. The control scheme contains two recurrent trainable neural network (RTNN) models, which are a plant parameter identifier and state estimator, an IPS feedback /feedforward controller, and one or two I-terms. The good performance of the adaptive IPS control scheme is confirmed by closed-loop systems analysis and by simulation results obtained with a MIMO plant, corrupted by noise.

Decentralized direct and indirect I-term adaptive fuzzy-neural control of a bioprocess plant

“…The applied Recurrent Trainable Neural Network (RTNN) topology and Backpropagation (BP) learning are described in [7], [14]. The aim of this paper is to extend the obtained in [17], [18] results of decentralized DPS bioprocess control using the LevenbergMarquardt learning algorithm, [21], and incorporating an Iterm, [14], [15], in the direct and indirect fuzzy-neural control to augment the system resistance to imperfections and noise.…”

“…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 [5]. 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, [14], [15]. The FFNN and the RNN have been applied for Distributed Parameter Systems (DPS) identification and control too [6]- [13].…”

Hierarchical genetic algorithms for topology optimization in fuzzy control systems

“…In principle, there is no general rule or method for the fuzzy logic set-up, although a heuristic and iterative procedure for modifying the membership functions to improve performance has been proposed (Sepulveda et al 2005). Recently, many researchers have considered a number of intelligent schemes for the task of tuning the fuzzy system (Baruch and Garrido 2005). The noticeable Neural Network (NN) approach (Jang and Sun 1995) and the Genetic Algorithm (GA) approach (Homaifar and McCormick 1995) to optimize either the membership functions or rules, have become a trend for fuzzy logic system development.…”

Centralized Distributed Parameter Bioprocess Identification and I-Term Control Using Recurrent Neural Network Model

“…The multilayer feedforward NN realizing a NARMA model for systems identification has the inconvenience that it is sequential in nature and require input and feedback tap-delays for its realization. In (Baruch et al, 2002;Baruch et al, 2004;Baruch et al, 2005a;Baruch et al, 2005b;Baruch et al, 2007a;Baruch et al, 2007b;Baruch et al, 2008;Baruch & Mariaca-Gaspar, 2009;Baruch & Mariaca-Gaspar, 2010), a new completelly parallel canonical Recurrent Trainable NN (RTNN) architecture, and a dynamic BP learning algorithm has been applied for systems identification and control of nonlinear plants with equal input/output dimensions, obtaining good results. The RTNN do not need the use of tap delays and has a minimum number of weights due to its Jordan canonical structure.…”