Challenge

Project - Speed Control of a Direct Current (DC) motor

Aim:

To design a Simulink model for the DC motor will be first developed per the schematic shown in Figure 1.Use the Proportional, Integral, and Derivative (PID) control design knowledge gained in the training to design, develop and test a controller to maintain a desired speed for the motor.

Objectives:

1) To design and simulate the DC motor and Build a control model to regulate the speed of the motor.

2) Finding out the appropiate values of Kp,Kd and Ki to met the required conditions.

Speed Control of DC motor:

1) Armature Control Methods.

2) Field Control Methods.

Simulink Model:

1) Here first we need to creat simulink model of the DC motor and make it as subsytem called the DC motor.

2) So next the subsytem of DC motor is connected to the discrete PID controller which helps in maintaining the desired speed.

3) For finding out Kp,Kd we have to use PLACE command in matlab and Ki we can set manually.

4) Which is used to obtaine the control design objectives.

Figure 1 - Simulink Model for DC motor.

Figure 2 - Simulink Blocks for the PID Controller.

Matlab Script:

Output Obtain from the DC motor -

Ouput Obtain from the DC motor PID Controller -

Comments On Graphs:

In that graph we observed that for the cases we found out that the third case with the praportionality values as follows -

The blue line has the values of constants such as -

Ki =5000 Kp = -2.82 and Kd = 38.31 By this values we observed that the system has high overshoot value and undershoot values.

The black dotted line has the constants values such as -

Ki =5000 Kp =5 and Kd = 120 This values has a overshoot value but lesser undershoot value.

The red line has the constant values such as -

Ki = 8000 Kp =5 and Kd = 120 the values of this constant has low overshoot and low undershoot values so this value we can use it for our PID controller for motors.

Conclusion - For the given DC motor we can design a PID controller with appropriate values of praportions to get the most optimum and desired outcome with less overshoot and undershoot.

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