Neural network-based adaptive controller

Having successfully implemented conventional MRAC techniques, the next logical step was to try to incorporate the MRAC techniques into a neural network-based adaptive control system. The ability of multilayered neural networks to approximate linear as well as nonlinear functions is well documented and has foimd extensive application in the area of system identification and adaptive control. The noise-rejection properties of neural networks makes them particularly useful in smart structure applications. Adaptive control schemes require only limited a priori knowledge about the system to be controlled. The methodology also involves identification of the plant model, followed by adaptation of the controller parameters based on a continuously updated plant model. These properties of adaptive control methods makes neural networks ideally suited for both identification and control aspects [7-11]. 

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... 

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. 

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. 

Neural Network-Based Optimizing Controller With On-Line Adaptation... 

Nordgren, R.E. and Meckl, P.H. 1993. An analytical comparison of a neural network and a model-based adaptive controller. IEEE Trans. Neural Networks, 4 685-694. 

Neural Network-Based Model Reference Adaptive Control... 

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). 

Nonmodel-based controllers, such as the least mean square (LMS) and artificial neural network back-propagation adaptive controllers, employ iterative approaches to update control parameters in real time [14-17]. However, those methods may encounter difficulties of numerical divergence and local optimiza-... 

Lin, C., and Kim, H. (1991), CMAC-Based Adaptive Critic Self-Learning Control, IEEE Transactions on Neural Networks, Vol. 2, No. 5, pp. 530-533. 

Chapter 12 introduces the use of neural network techniques and their application in modeling physiological control systems. As their name imphes, neural networks are computational algorithms based upon the computational structure of the nervous system, and are characterized as distributed processing and adaptive. Neural networks have been used to describe the control of arm movements with electrical stimulation and the adaptive control of arterial blood pressure. 

A biomedical control system that utilizes a neurophysiologically-based approach has been developed for use in Functional Neuromuscular Stimulation (FNS) systems [Abbas, 1995 Abbas and Chizeck, 1995). FNS is a rehabilitation engineering technique that uses computer-controlled electrical stimuli to activate paralyzed muscle. The task of a control system is to determine appropriate stimulation levels to generate a given movement or posture. The neural network control system utilizes a block diagram structure that is based on hierarchical models of the locomotor control system. It also utilizes a heterogenous network of neurons, some of which are capable of endogenous oscillation. This network has been shown to provide rapid adaptation of the control system parameters [Abbas and Chizeck, 1995 Abbas and Triolo, 1997] and has been shown to exhibit modulation of reflex responses [Abbas, 1995]. 

Chen, C.-T., Lin, W.-L., Kuo, T.-S., and Wang, C.-Y. 1997. Adaptive control of arterial blood pressure with a learning controller based on multilayer neural networks. IEEE Trans. BME, 44 601-609. 

Araz OU, Salum L (2010) A multi-criteria adaptive control scheme based on neural networks and fuzzy inference for DRC manufacturing systems. Int J Prod Res 48(l) 251-270... 

Fabric inspection is an essential process for maintaining the quality of a fabric. Traditionally fabric inspection has bear carried out manually through human visualisation performed by skilled inspectors. This process can be tedious and can add to the production cost. Besides many defects can be missed, and the inspection can be inconsistent whereby the output is dependent on the training and the performance and skill level of the human inspectors. In recent years automated machines have bear developed based on adaptive and neural network systems. Automatic fabric systems are capable of providing consistent results that can be correlated with certain quality control standards. 

Shen Y (2010) Adaptive online state-of-charge determination based on neuro-controller and neural network. Energy Convers Manag 51 1093-1098. doi I0.1016/j.enconman.2009.12.015... 

Model based control schemes such as model predictive control are highly related to the accuracy of the process model. A regional-knowledge index is proposed in this study and applied in the analysis of dynamic artificial neural network models in process control. To tackle the extrapolation problem and assure stability of the control system, we propose to run a neural adaptive controller in parallel with a model predictive control. A coordinator weights the outputs of these two controllers to make the final control decision. The proposed analysis method and the modified model predictive control architecture have been applied to a neutralization process and excellent control performance is observed in this highly nonlinear system. 

Adaptive Control of Continuous Pulp Digesters based on Radial Basis Function Neural Network Models... 

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