Controlling a complex electromechanical process on the basis of a neurofuzzy approach
Future Generation Computer Systems
Nonlinear system identification: From multiple-model networks to Gaussian processes
Engineering Applications of Artificial Intelligence
Feedback Linearization Using Neural Networks: Application to an Electromechanical Process
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Direct Adaptive Soft Computing Neural Control of a Continuous Bioprocess via Second Order Learning
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Controlling a complex electromechanical process on the basis of a neurofuzzy approach
Future Generation Computer Systems
International Journal of Applied Mathematics and Computer Science
Nonlinear system identification using two-dimensional wavelet-based state-dependent parameter models
International Journal of Systems Science
Multiple fuzzy neural networks modeling with sparse data
Neurocomputing
Applied Computational Intelligence and Soft Computing
Recurrent neural control of a continuous bioprocess using first and second order learning
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
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This paper investigates in detail the possible application of neural networks to the modeling and adaptive control of nonlinear systems. Nonlinear neural-network-based plant modeling is first discussed, based on the approximation capabilities of the multilayer perceptron. A structure is then proposed to utilize feedforward networks within a direct model reference adaptive control strategy. The difficulties involved in training this network, embedded within the closed-loop are discussed and a novel neural-network-based sensitivity modeling approach proposed to allow for the backpropagation of errors through the plant to the neural controller. Finally, a novel nonlinear internal model control (IMC) strategy is suggested, that utilizes a nonlinear neural model of the plant to generate parameter estimates over the nonlinear operating region for an adaptive linear internal model, without the problems associated with recursive parameter identification algorithms. Unlike other neural IMC approaches the linear control law can then be readily designed. A continuous stirred tank reactor was chosen as a realistic nonlinear case study for the techniques discussed in the paper