TUNING OF DIGITAL PID CONTROLLERS USING PARTICLE SWARM OPTIMIZATION ALGORITHM FOR A CAN-BASED DC MOTOR SUBJECT TO STOCHASTIC DELAYS

Abstract

In this project proposed the tuning problem of digital proportional integral derivative (PID) parameters for a DC motor controlled via the controller area network (CAN). Firstly, the model of the DC motor is presented with its parameters being identified with experimental data. By studying the CAN network characteristics, we obtain the CAN-induced delays related to the load rate and the priorities. Then, considering the system model, the network properties, and the digital PID controller, the tuning problem of PID parameters for CAN based DC motor is transformed into a design problem of a static output feedback (SOF) controller for a time delayed system. To solve this problem, particle swarm optimization (PSO) algorithm and linear quadratic regulator (LQR) method are adopted with incorporating the sufficient condition of time varying delay system. Finally, the effectiveness of the proposed PID tuning strategy is validated by experimental results.

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