Metaheuristic optimization approach has become the new framework for control synthesis. The main purposed of the control design are input (command) tracking and load (disturbance) regulating. This research proposes an optimal proportional-derivative (PID) controller design for the brushless dc (BLDC) motor speed control system with input tracking and load regulating constrained optimization by using the cuckoo search (CuS) one of the most efficient population-based metaheuristic optimization techniques. The sum-squared error between population-based metaheuristic optimization techniques. The sum-squared error between the referent input and the controlled output is set as the objective function to be minimized. The rise time the maximum overshoot settling time and steady-state error are set as inequality constraints for tracking purpose while the regulating time and the maximum overshoot of load regulation are set as inequality constraints for regulating purpose. Results obtained by the CuS will be compared with those obtained by the particle swarm optimization (PSO) and genetic algorithm (GA). From simulation results it was found that the parameters of CuS were optimal than both PSO and GA for BLDC motor speed control system satisfying the preset input tracking and load regulating constraints. In addition the simulation results are confirmed by the experimental ones from the BLDC motor speed control system developed in the laboratory.

This research is to study and develop a DC motor speed control by using Visual Basic.Net 2008 in computer programming to use the computer and the microcontroller is processing from computer via the serial port. So that a signal to the motor driver circuit motor speed work as assigned. By the speed can be entered via the computer screen is measured in rpm. and motor control to control the PWM (Pulse Width Modulation) control circuit and the principles of the hardware by using the digital output signal of the microcontroller control Motor speed can be configured to be from 100 to 3000 rpm. And the motor starts to spin at maximum speed and then gradually reduce the speed down to the specified value. Control both the speed and the average error of 4.6% compared to the speed measurement using infrared light. And found that at speeds below 100 rpm. to a maximum error of 12% and the error decreases when the speed increases. The error value is at least 2000 rpm. and 3000 rpm the speed error is 0.5%

This paper presents compare results performance between two conventional controllers for bicycle robot. The bicycle speed control system was a highly nonlinear model multivariable and absolutely unstable. It has used for testing various design control techniques for low power. The objectives of this paper had to developed speed control which derived the mechanic model and experiment. Controllers are Proportional-Integral-Derivatives (PID) and on-off dynamic control. The dynamic system which also includes not stable friction effects the linear zed system model is used as a basis for the design to apply to speed. Computer simulation and experiment have been done controller are capable to control output speed wheel control system successfully. The results show PID controller better then on-off dynamic controller.