Abstract
A conventional Proportional–Integral–Derivative (PID) controller is not able to adapt to the changes in the system of a plant having nonlinear dynamics. In this paper, a Neural Network PID (NN-PID) controller is designed based on Multi-layer Neural Network (MLN) technique for controlling of the liquid level in a nonlinear surge tank system. A separate MLN identifier is implemented to approximate the plant’s dynamics which operates in parallel to the controller with disturbance and parametric uncertainties in the system. The NN-PID controller works using backpropagation algorithm and weights are updated according to the gradient descent based learning rule. The simulation results show that as the variations occur in the plant, MLN identifier follows the plant’s dynamics by adjusting its parameters and the controller reads just the plant’s output to the desired level by adjusting its own parameters. In addition to this, NN-PID controller response is much accurate and faster than the conventional PID controller and an improvement of 97.35% was achieved in terms of ISE.
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Agrawal, A., Goyal, V., Mishra, P. (2019). Adaptive Control of a Nonlinear Surge Tank-Level System Using Neural Network-Based PID Controller. In: Malik, H., Srivastava, S., Sood, Y., Ahmad, A. (eds) Applications of Artificial Intelligence Techniques in Engineering. Advances in Intelligent Systems and Computing, vol 698. Springer, Singapore. https://doi.org/10.1007/978-981-13-1819-1_46
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DOI: https://doi.org/10.1007/978-981-13-1819-1_46
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