Design of Fractional Order PID & Neural Network Controller for Magnetic Levitation Train System
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Date
2018-12
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Addis Ababa University
Abstract
Magnetic levitation systems are systems which operate based on the principle of magnetic attraction and repulsion to levitate an object and widely used in frictionless bearings, high-speed Maglev passenger trains, levitation of wind tunnel models, and so on. Their attracting feature is that they have a zero frictional force which considerably reduces the energy required to drive the systems. However, magnetic levitation systems are highly nonlinear and open loop unstable which makes their control very difficult. Moreover, magnetic levitation trains are more advantageous than conventional trains because train weight is lower due to the absence of wheels, axles, and engine, and hence can save huge energy consumption; the energy loss due to unwanted friction is negligible; it allows the train to move at a very high speed and to be environment friendly. This Thesis work investigates the control mechanism of Maglev systems based on linear unity feedback controller (PID and FOPID) with Taylor series linearization techniques and nonlinear controllers (ANN) based on nonlinear methods. To improve the closed loop performance of the PID controller more advanced PIλDδ controller is used for the system. Stability is also ensured due to the additional tunable parameters λ (0.99811) and δ (0.99998) respectively. Then the proposed FOPID controller has resulted good performance i.e. the PI��D�� controller improved a design performance specifications of settling time, overshoot, steady state error and peak response from (0.103 to 0.015 second), (18.452% to 4.37%), and (0.18 to 0.043) and (1.184 to 1.043) cm in Y direction. Lastly, for position control and stabilization of the Maglev train system, a powerful ANN controller is designed. Furthermore, a comparison between simulation results of the Conventional PID, fractional order PID, and NN controller is made to check the performance characteristics and stability of the Maglev train system. FOPID controller is more improved by ANN controller; performance characteristics of Maglev train obtained by ANN controller has resulted good. The neural network controller improved rise time, settling time, overshoot, steady state error and peak response from (0.009 to 0.0057 second), (0.015 to 0.0069 second), (4.37% to 0.864%), (0.043 to 0), and (1.043 to 1) cm in Y direction.
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Keywords
Maglev Train, EDS, EMS, FOPID controller, Artificial Neural Network (ANN) controller