In this paper we describe a technical system for DC motor speed control. The speed of DC motor is controlled using Neural Network Based Model Reference and Predictive controllers with the use of Matlab/Simulink. The analysis of the DC motor is done with and without input side Torque disturbance input and the simulation results obtained by comparing the desired and actual speed of the DC motor using random reference and sinusoidal speed inputs for the DC motor with Model Reference and Predictive controllers. The DC motor with Model Reference controller shows almost the actual speed is the same as the desired speed with a good performance than the DC motor with Predictive controller for the system with and without input side disturbance. Finally the comparative simulation result prove the effectiveness of the DC motor with Model Reference controller.
Table of Contents
1.1 Introduction
2.1 Mathimatical Model of DC Motor
3.1 The Proposed Controller Design
3.1 Model-Reference Controller Design
3.2 Predictive Controller Design
4. Result and Discussion
4.1 Comparison of DC motor with Model Reference and Predictive controllers without input disturbance
4.2 Comparison of DC motor with Model Reference and Predictive controllers with input disturbance
5. Conclusion
Research Objectives and Core Themes
The primary objective of this study is to design and evaluate the performance of Neural Network-based Model Reference and Predictive controllers for DC motor speed regulation, specifically analyzing their robustness against input-side torque disturbances using Matlab/Simulink simulations.
- Mathematical modeling of DC motor dynamics and state-space representation.
- Implementation of Neural Network-based control strategies for motor speed management.
- Performance benchmarking through random reference and sinusoidal speed input scenarios.
- Comparative analysis of control accuracy in the presence and absence of external torque disturbances.
Excerpt from the Book
2.1 Mathimatical Model of DC Motor
The system structure of a DC motor is shown in Figure 1, including the armature resistance Ra and winding leakage inductance La. According to the Kirchhoff’s voltage law, the electrical equation of the DC motor is described as Ri a(t) + La di a(t)/dt + Vb(t) = Vs(t). Where ia(t) is the armature current, vb(t) is the back emf voltage and vs(t) is the voltage source. The back emf voltage vb(t) is proportional to the angular velocity (t) of the rotor in the motor, expressed as Vb(t) = kb(t). Where kb is the back emf constant. In addition, the motor generates a torque TM proportional to the armature current, given as TM(t) = kTia(t).
Where kT is the torque constant. If the input voltage Vs(t)=Vs is a constant, the resulted armature current ia(t)=Ia, angular velocity (t)= and torque TM(t)=T are also constant in the steady state. From (1) to (3), we have RaIa + kb = Vs. T = kTia. Under the conservation of power, we know that the input power IaVs is equal to the external power T and the power consumed in the resistance, i.e. Ra(Ia)^2.
Summary of Chapters
1.1 Introduction: Provides an overview of the importance of DC motor speed control in industrial applications and outlines the need for high-performance control systems.
2.1 Mathimatical Model of DC Motor: Establishes the governing electrical and mechanical equations of the DC motor and derives its state-space representation.
3.1 The Proposed Controller Design: Details the conceptual framework for the control strategies employed in the study.
3.1 Model-Reference Controller Design: Explains the structure and neural network components required for the model reference control approach.
3.2 Predictive Controller Design: Describes the design of the predictive controller, focusing on training the neural network to represent plant dynamics.
4. Result and Discussion: Presents the comparative analysis and simulation results for different controller configurations under various operating conditions.
4.1 Comparison of DC motor with Model Reference and Predictive controllers without input disturbance: Evaluates controller efficiency under ideal conditions using random and sine wave references.
4.2 Comparison of DC motor with Model Reference and Predictive controllers with input disturbance: Analyzes the robustness of the controllers when facing external torque disturbances.
5. Conclusion: Summarizes the effectiveness of the Model Reference controller as the superior solution based on the simulation findings.
Keywords
DC motor, Neural Network, Model Reference controller, Predictive controller, Speed control, Matlab, Simulink, Armature voltage, Torque disturbance, State-space model, Industrial automation, Control systems, Robotics, Servomechanisms, Performance analysis.
Frequently Asked Questions
What is the core focus of this research?
The research focuses on controlling the speed of a DC motor using Neural Network-based Model Reference and Predictive controllers.
What are the primary themes discussed in the paper?
The paper covers mathematical modeling of DC motors, the design of neural network controllers, and the comparative performance analysis of these controllers under varying disturbance conditions.
What is the ultimate goal of the proposed controller design?
The goal is to achieve minimized steady-state error and short settling times for motor speed, ensuring the motor follows a desired speed signal effectively.
Which scientific software environment is used for this study?
The entire analysis and simulation for the DC motor control are conducted using Matlab/Simulink.
What does the main body of the work address?
The main body details the mathematical derivation of the motor model, the architecture of the controllers, and a series of simulation experiments testing performance with random and sinusoidal inputs.
Which keywords best characterize the study?
Key terms include DC motor, Neural Network, Model Reference controller, Predictive controller, and torque disturbance.
How does the Model Reference controller compare to the Predictive controller?
According to the simulation results, the Model Reference controller demonstrates superior performance by tracking the desired speed more accurately than the Predictive controller.
What impact does input-side torque disturbance have on the results?
The disturbance tests reveal that while both controllers are affected, the Model Reference controller maintains better stability and tracking efficiency at the midpoint of the simulation.
- Quote paper
- Mustefa Jibril (Author), 2020, DC Motor Speed Control with the Precence of Input Disturbance using Neural Network Based Model Reference and Predictive Controllers, Munich, GRIN Verlag, https://www.grin.com/document/542040
