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ANJUMAN ENGINEERING COLLEGE

ANJUMAN ENGINEERING COLLEGE. Department of Electrical & Electronics. Seminar Report. On. NEURAL NETWORKS IN PROCESS CONTROL. Guided by. Presented by. ASHIF RAHMATHULLA T.T. Mr.ANIL KADLE M.Tech.[I.E.]. ABSTRACT.

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ANJUMAN ENGINEERING COLLEGE

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  1. ANJUMAN ENGINEERING COLLEGE Department of Electrical & Electronics Seminar Report On NEURAL NETWORKS IN PROCESS CONTROL Guided by Presented by ASHIF RAHMATHULLA T.T Mr.ANIL KADLE M.Tech.[I.E.]

  2. ABSTRACT The effectiveness of operation of process is decided by the level of control associated with it. Over a period of time necessity is being felt to make the control technique intelligent to increase the effectiveness of the control strategies. In order to achieve the above objective we can in cooperate the intelligence features in the control algorithms to make it adaptive with time and take care of all variation and fluctuation by in cooperating the elements of intelligence using ANN. If it try to realize it analytically we will be handle conveniently ect . With the advent of computers it is possible to develop intelligent control systems for dedicated and shared application in process industry.

  3. LEARNING TECHNIQUES • Multilayer neural networks(MLNN) • Error back propogation(EBB) • Radial basis functions(RBF) • Reinforcement learning • Temporal deference learning • Adaptive resonance theory(ART) • Genetic algorithm

  4. ANN BASED CONTROL CONFIGURATION • Direct inverse control • Direct adaptive control • Indirect adaptive control • Internal model control • Model reference adaptive control

  5. CONCLUSION This paper present the state of ANN in process control applications. The Ability of MLNN to model arbitrary non linear process is used for the identification and control of a complex process. Since the unknown Complex system are online modeled. And are controlled by the input_output dependent neural networks,the control mechanism are robust For for varying system parameters. it is found that the MLNN with EBP training algorithm are best suited for identification and control since the learning is of supervised nature And can handle the nonlinearity present in the plants with only input_output Information. however, there are difficulties in implementing MLNN with EBP. Like selection of learning rate, momentum factor, selection of network size etc thus it becomes very much essential to have some concrete guard Lines for selecting the network. further ,there is lot of scope in developing Different effective configurations based on ANN for identification and control of the complex process.

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