Salp swarm algorithm based optimal speed control for electric vehicles

Abstract

<p>The paper is all about the implementation of a novel bio-inspired metaheuristic<br />salp swarm algorithm (SSA) for speed control of brushless DC<br />(BLDC) motor drive that is run in sensorless control mode. The angular<br />speed of the motor is evaluated using an extended kalman filter, in which the<br />dynamics of the motor are nonlinear. The error in speeds between actual and<br />estimated is fed to the PID controller. To achieve the good transient<br />operation of the motor drive, the parameters of the PID are tuned with the<br />SSA. The optimum PID gains are determined by the minimization of integral<br />square error and then final optimum gains are validated on the laboratory<br />testbed. The proposed method is also tested in various cases to check the<br />performance of the drive. The experiments are also performed at low speeds<br />to know the superiority of the proposed method.</p>