Salp swarm algorithm based optimal speed control for electric vehicles

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