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Research in Engineering International Journal of Advanced in Engineering and Technology (IJARET) International Journal of Advanced Research and Technology (IJARET), ISSN 0976 – 6480(Print) IJARET ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 2, Number 1, Jan - Feb (2011), © IAEME ISSN 0976 – 6499(Online) Volume 2 Number 1, Jan - Feb (2011), pp. 01- 11 © IAEME © IAEME, http://www.iaeme.com/ijaret.html ANALYSIS OF INTELLIGENT SYSTEM DESIGN BY NEURO ADAPTIVE CONTROL Dr. Manish Doshi Hemchandracharya North Gujarat University Patan, E-Mail: manishdos@gmail.com ABSTRACT Design of Intelligent system is very crucial and simple mathematical modeling is not used for the analysis. Hence this paper represents the analysis of intelligent system design by Neuro adaptive control. Here various methods like Neural networks for identification, Series-Parallel Model, Supervised control, Inverse control are taken and at last, Neuro fuzzy adaptive control is used to solve design of intelligent system. Keywords: Adaptive Control, series parallel model, inverse control I. INTRODUCTION The adaptive control techniques described above assume the availability of an explicit model for the system dynamics (as in the gain scheduling technique) or at least a dynamic structure based on a linear experimental model determined through identification (as in STR and MRAC). This may not be the case for a large class of complex nonlinear systems characterized by poorly known dynamics and time-varying parameters that may operate in ill-defined environments. Besides, conventional adaptive control techniques lack the important feature of learning. This implies that an adaptive control scheme cannot use the knowledge it has acquired in the past to tackle similar situations in the present or in the future. In other words, while adaptive control techniques have been used effectively for controlling a large class of systems with predefined structure and slowly time-varying parameters, they nevertheless lack the ability of learning and lack the ability for tackling the global control issues of nonlinear systems. Making assumption of linear structures of processes is not always possible and designers have to deal with the inherent nonlinear aspect of the systems dynamics. [1]. 1 International Journal of Advanced Research in Engineering and Technology (IJARET) ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 2, Number 1, Jan - Feb (2011), © IAEME As a potential remedy to some of these issues, designers have devised new control approaches that avoid the explicit mathematical modeling of dynamic processes. Some researchers have termed them as expert intelligent control approaches. Some of these approaches, such as those based on fuzzy logic theory, permit for an explicit modeling of the systems. Other approaches use well-defined learning algorithms through which the system is implicitly modeled and adapts autonomously its controller parameters so as to accommodate unpredictable changes in the system’s dynamics. Here, the dynamical structure is not constrained to be linear, as is the case for most conventional adaptive control techniques. The study of these approaches has constituted a resurgent area of research in the last several years and several important results were obtained. [2] The family of controllers using fuzzy logic theory are basically designed on the premise that they take full advantage of the (linguistic) knowledge gained by designers from past experiences. We tackle here another class of intelligent controllers, which are based on neural modeling and learning. They are built using algorithms that allow for the system to learn for itself from a set of collected training patterns. They have the distinctive feature of learning and adjusting their parameters in response to unpredictable changes in the dynamics or operating environment of the system. Their capability for dealing with nonlinearities, for executing parallel computing tasks and for tolerating a relatively large class of noises make them powerful tools for tackling the identification and control aspect of systems characterized by highly nonlinear behavior, time-varying parameters and possible operation within an unpredictable environment.[3] Given the fact that we deal here with dynamical models involving the states of the model at different time steps, it is only natural to design a specialized structure of neural networks that have the capability for “memorizing” earlier states of the system and for accommodating feedback signals. This is in contrast with the conventional neural networks (based on BPL) used mostly for system classification or function approximation. In fact, despite their proven capabilities as universal approximators, a limitation of standard feed forward neural networks, using backpropagation as the learning mechanism, is their limitations for exclusively learning static input– output 2 International Journal of Advanced Research in Engineering and Technology (IJARET) ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 2, Number 1, Jan - Feb (2011), © IAEME mappings. While adept at generalizing pattern-matching problems in which the time dimension is not significant, systems with a non-stationary dynamics cannot be strictly modeled. This is the case of dynamic systems for which the representation is made through time-dependent states. [4] One way of addressing this problem is to make the network dynamic, that is, by providing it with a memory and feedbacks. A particular class of the recurrent structure is the so-called recurrent time-delay neural networks. One way for accomplishing this is to incorporate feedback connections from the output of the network to its input layer and include time delays into the NN structure through their connections. As there are propagation delays in natural neurobiological systems, the addition of these time delays stems from theoretical, as well as practical, motivations. Time-Delay Neural Networks (TDNNs) accomplish this by replicating the network across time. One can envision this structure as a series of copies of the same network, each temporally displaced from the previous one by one discrete time unit similar to the BPTT described earlier. The resultant structures can be quite large, incorporating many connections, and novel learning algorithms are employed to ensure rapid training of the network.[5],[6]. II. PROBLEM FORMULATION To illustrate the idea let us presume that the dynamic system input–output behavior of a nonlinear dynamic system is represented by the following equation: y(k 1) = f [ y(k), . . . , y(k n); u(k), . . . , u(k n)] where y(k 1) represents the output of network at ttime (k 1), and y(k), y(k 1), . . . , y(k n) are the delayed output states serving as part of the network input along with the input signal u(k) and its delayed components: u(k 1), . . . , u(k n). The network representation of such systems is depicted in Figure 1. Notice that this network is recurrent (feedback signals) with time-delayed inputs. While several algorithms have been proposed in the literature to deal with the training of time-delayed recurrent networks, the dynamic backpropagation algorithm has been among the standard techniques used for this purpose. 3 International Journal of Advanced Research in Engineering and Technology (IJARET) ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 2, Number 1, Jan - Feb (2011), © IAEME Figure 1 Time delayed recurrent neural network 2.1 Neural Networks for Identification As universal approximators, neural networks have been used in recent years as identifiers for a relatively wide range of complex dynamic systems (linear and nonlinear). Their capability for processing tasks in parallel and for tolerating noisy versions of the input signals make them excellent candidates for system identification. The process of systems identification, as mentioned previously, aims to find a dynamical model that approximates within a predefined degree of accuracy the actual plant dynamics when both systems are excited with the same signals. This means that we require a minimization of the error e(k 1) between the predicted output Np(k 1) and the actual out- put yp(k 1) of the system: e(k + 1) = yp (k + 1) − yp (k + 1) Since this error depends on the parameters of the network, the solution should provide the set of weights that would minimize it. Four major classes of models encompass a wide range of nonlinear input–output model representations: n−1 model1: yp(k +1) = ∑ayp(k −i) + g[u(k),......., y(k − m+1)] i i=0 m−1 mod el 2: yp(k +1) = f [ yp(k ),...., yp(k − n +1)] + ∑biu(k − i)] i =0 model3: yp(k +1) = f [yp(k),...., yp(k −n+1)]+ g[u(k),......., y(k −m+1)] mod el 4: yp(k +1) = f [ yp(k ),...., yp(k − n +1); u(k ),......., y(k − m +1)] In each of the models, the pair (u(k), yp(k)) represents the input–output pair of the identified plant at sample k and m ≤ n. f and g are two smooth functions in 4 International Journal of Advanced Research in Engineering and Technology (IJARET) ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 2, Number 1, Jan - Feb (2011), © IAEME their arguments. However, among the four models, the fourth one has been used most often given its sharing relevance to a wide majority of nonlinear dynamic systems. The choice for the appropriate learning algorithm for the neural identifiers (dynamic or static) depends mostly on whether the network takes as its inputs a delayed version of its output, o r directly uses sample outputs from the plant itself. The two schemes that have been used most frequently are the series-parallel scheme and the parallel scheme. To illustrate the ideas, and if model four is taken as the system model of choice, the two corresponding schemes are then given as: 2.2. Series-Parallel Model The future value pof the output N (k 1) in this model is expressed as: yp(k +1) = NNI[ yp(k),...., yp(k − n +1)]; u(k),......., y(k − m +1)] where NNI stands for mapping provided by the neural network identifier. This model is represented in Figure 2, and a careful inspection of its structure shows that while it still requires a set of delayed signals for it inputs, it does not involve feedback from the network output, which is the case for the second model known as parallel. 2.3. Parallel Model The estimated future value of the output N (k 1) in the parallel model is p expressed as: yp(k +1) = NNI[ yp(k),...., yp(k − n +1)]; u(k),......., y(k − m +1)] This model is illustrated in Figure 3 and uses the delayed recursions of the established output as some of its input. One may notice here that given the particular structure of the series- parallel model, which doesn’t involve recurrent states of the network output, the standard BPL could be used for extracting the parameters of the network. This is, however, not the case for the parallel structure given that the model includes a feedback loop including nonlinear elements. As such BPL cannot be applied in this case, and dynamic backpropagation would be the learning algorithm of choice. There have been a number of control schemes proposed in recent years involving neural networks for identification and control. Following categories: 5 International Journal of Advanced Research in Engineering and Technology (IJARET) ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 2, Number 1, Jan - Feb (2011), © IAEME supervised (“teacher” or model-based) control, inverse model- based control, and neuro-adaptive control are outlined next. Figure 2 Series-parallel scheme for neural identification Figure 3 Parallel scheme for neural identification 6 International Journal of Advanced Research in Engineering and Technology (IJARET) ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 2, Number 1, Jan - Feb (2011), © IAEME III NEURAL NETWORKS FOR CONTROL 3.1 Supervised control The first category assumes that a controller is synthesized on the basis of the knowledge acquired from the operation of the plant by a skilled operator (as shown in Figure 4) or through well-tuned PID controllers. During the nominal operation of the plant (at a particular operating condition) experimental data are collected from sensor devices, and are later used as a training set for a neural network. Once the training is carried out adequately, the process operator then becomes an upper-level supervisor without the need for being part of the control loop of the process. This is particularly useful in the case of hazardous environments or for improving the automation level of the plant. The neural network could also play the role of a gain interpolator of a set of PID controllers placed within the control loop of the plant. Once trained, the neural network will act here as a gain feeder for the PID controller, providing it with appropriate gains every time the operating conditions of the system change. This is a good alternative for interpolating between the gains instead of delivering them in a discrete way, as is the case of conventional gain scheduling. One of the main issues, however, is that the training data collected are usually corrupted with a large amount of noise and as such have to be filtered before becoming useful as valid training data. This has the potential of making of the neural network an expensive and possibly a time-consuming process. Another issue pertains to difficulties in extracting the knowledge acquired by the experts into a set of patterns t h at could be used for the network’s training. Figure 4 Neural network acting as a supervisory controller 7 International Journal of Advanced Research in Engineering and Technology (IJARET) ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 2, Number 1, Jan - Feb (2011), © IAEME 3.2 Inverse control The second category where neural networks could be implemented within the control loop is known as inverse control or inverse plant modeling. Here, the designer seeks t o build a neural network structure capable of mapping the input– output behavior of an inverse controller. The inverse controller is by definition a controller which, when applied to the plant, leads ideally to an overall transfer function of unity. A schematic representation of this control scheme is shown in Figure 4. Designing an inverse neural controller should be always done under the assumption t h a t the process is minimum phase and causal. The main advantage of implementing an inverse controller into a neural structure is Figure 4 Neural network as inverse model-based controller 8 International Journal of Advanced Research in Engineering and Technology (IJARET) ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 2, Number 1, Jan - Feb (2011), © IAEME that it would allow for a faster execution and tolerance to a range of noises. The implementation of a neural network as an inverse controller, however, has been hindered by several difficulties, including the induction of unwanted time delays leading to possible discretization of processes that are originally continuous. This is mostly due to the effect of noncausality of the inverse controller. Moreover, the inverse controller cannot realistically match exactly the real plant, a fact that leads to unpredictable errors in the controller design. IV. NEURO-ADAPTIVE CONTROL The third category of neural controllers pertains to implementing two dynamical neural networks within an adaptive control architecture, similar to the MRAC one. One of the networks serves here as an identifier, while the second serves as a controller. A large amount of research work has been dedicated to controllers belonging to this category. Given the dynamic nature of the system being identified and controlled, recurrent time-delayed neural networks have been the tools of choice for this case. The main advantages of these neuro-adaptive structures pertain to their capability in effectively tackling the nonlinear behavior of the systems without compromising on their representation using linear approximations (such as the ARMA model auto-regressive moving average). This has not always been possible with the conventional schemes of adaptive control systems as described in earlier sections. As in the identification section, the controller here is handled using another structure of a time-delay neural network for which the output serves as input to the plant. The same output is delayed in time and fed back to the network to serve as part of its input signals. Now combining the two aspects (identification and control) within a well-defined adaptive structure such as the one described in a previous section leads to the representation of Figure 5. In this structure, which is very similar to the MRAC, identification is carried out first, and the identified model is then compared with the output of a reference model. The recorded error is then used to update the control law through modifications of the neural controller weight. This process is described in ample detail. Despite the fact that the structure is similar to the MRAC, the current scheme developed here is known as an inverse neuro-adaptive control 9 International Journal of Advanced Research in Engineering and Technology (IJARET) ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 2, Number 1, Jan - Feb (2011), © IAEME scheme. This is mainly due to the fact that the error provided t o the neural controller is n ot computed directly as the difference between the plant output and the reference, but rather between the model identified and the reference. Figure 5 Neural network as neuro-adaptive controller IV. CONCLUSION The motivation for the early development of neural networks stemmed from the desire to mimic the functionality of the human brain. A neural network is an intelligent data-driven modeling tool that is able to capture and represent complex and non-linear input/output relationships. Neural networks are used in many important applications, such as function approximation, pattern recognition and classification, memory recall, prediction, optimization and noise-filtering. They are used in many commercial products such as modems, image-processing and recognition systems, speech recognition software, data mining, knowledge acquisition systems and medical instrumentation, etc. V. REFERENCES 1. Fausett, L., Fundamentals of Neural Networks, Prentice-Hall, Englewood Cliffs, NJ, 1994. 2. Ham, F. and Kostanic, I., Principles of Neurocomputing for Science and Engineering, McGraw Hill, New York, NY, 2001. 10 International Journal of Advanced Research in Engineering and Technology (IJARET) ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 2, Number 1, Jan - Feb (2011), © IAEME 3. Haykin, S. (1994) Neural Networks: A Comprehensive Foundation, Englewood Cliffs, NJ: McMillan College Publishing Company. 4. Hopfield, J.J. and Tank, D.W., Computing with Neural Circuits: A Model, Science, Vol. 233, 625−633, 1986. 5. Hopgood, A. (1993) Knowledge-based Systems for Engineers and Scientists, Boca Raton, Florida, CRC Press, 159–85. 6. Jang, J. S., Sun, C. T., and Mizutani, E., Neuro-Fuzzy and Soft Computing, Prentice Hall, Englewood Cliffs, NJ, 1997. 11