Neural Network-Based Model Reference Adaptive Control

1 Neural Network-Based Model Reference Adaptive Control 

After successful implementation of conventional model-reference adaptive controllers on smart structures, the next logical step was to investigate the possibility of using a neural network for adaptive control implementations. The linear and nonlinear mapping properties of neural networks have been extensively utilized in the design of multilayered feed-forward neural networks for the implementation of adaptive control algorithms [10]. 

A schematic diagram of the neural network-based adaptive control technique is shown in Fig. 4.9. A neural network identification model is trained using a static backpropagation algorithm to generate p(fc + 1), given past values of y and u. The identification error is then used to update the weights of the neural identification model. The control error is used to update the 

In the nemal network-based adaptive control scheme, a neurocontroller is trained to approximate an inverse model of the plant. We have introduced an adaptive activation function for increasing the training rate of the neural controller, and the proposed function is described in this section. 

In order to train a neural controller, a multilayered network with linear activation functions was initially considered. During the training process, a large sum-squared error occurred due to the unbounded nature of the linear activation function that caused a floating point overflow. To avoid the floating point overflow we used the h3rperbolic tangent activation functions in the hidden layers of the network. The network was unable to identify the forward 

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A neural network-based model reference adaptive control scheme for nonlinear plants is presented in this section. 

Hoskins, D.A. Neural Network Based Model-Reference Adaptive Control. Ph. D. Dissertation, University of Washington, UMI Dissertation Services, Ann Arbor, MI (1990)... 

The control law This is the information flow structure through which the manipulated variables are handled based on the measurements. The complexity of the control law is determined by the diversity of the control objective. As a result, the controller can be simple (on—off, proportional, proportional-integrated differential), more complicated adaptive model-based, empirical (expert systems), fuzzy or neural network-based. Detailed references on the various control systems applied on anaerobic digesters can be found in Boe (2006) and Find et al. (2003). 

Various neural network-based adaptive control techniques were discussed in this study. A major problem in implementing neural network-based MRACs is the translation of the output error between the plant and the reference model so as to train the neural controller. A technique called iterative inversion, which inverts the neural identification model of the plant for calculating neural controller gains, has been used. Due to the real-time computer hardware limitations, the performance of neural network-based adaptive control systems is verified using simulation studies only. These results show that neural-network based MRACs can be designed and implemented on smart structures. 

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