Automated Control with PID-Fuzzy-Neural
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Authors

Kevin Gautama is a systems design and programming engineer with 16 years of expertise in the fields of electrical and electronics and information technology.

He teaches at the Hanoi University of Industry in the period 2003-2011 and he has a certificate of vocational training by the Ministry of Industry and Commerce and the Hanoi University of Industry.

From extensive design experience through numerous engineering projects, the author founded the Enziin Academy.

The Enziin Academy is a startup in the field of educational, it's core goal is to training design engineers in the fields technology related.

The Enziin Academy is headquartered in Stockholm-Sweden with an orientation operating multi-lingual and global.

The author's skills in IT:

  • Implementing the application infrastructure on Amazon's cloud computing platform.
  • Linux server system administration (Sysadmin).
  • Design load balancing and content distribution system.
  • MySQL database administration.
  • C/C++/C# Programming
  • Ruby and Ruby on Rails Programming
  • Python and Django Programming
  • The WPF/C# on the .NET Framework Programming
  • The PHP/JAVA Programming
  • Machine Learning and Expert System.
  • Internet of Things.

The author's skills in the fields of electric and electronic:

  • The design of popular CPU / MCU systems.
  • Design FPGA / CPLD system (Xilinx - Altera).
  • Design and programming of DSP systems (Texas Instruments).
  • Embedded ARM system design.
  • The RTOS Programming
  • Design and programming electronic power systems.
  • PLC - inverter - sensor - electric control cabinet industrial.
  • Control systems distributed connection with Server.

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  • Curriculum
  • 1. Introduction
    • videocam
      The tasks to do in this course

      11m26s
    • videocam
      Overview of automatic control

      11m26s
    • videocam
      Theory of Automatic Control

      11m26s
  • 2. MATLAB in automatic control
    • videocam
      Introduction to MATLAB-Simulink

      11m26s
    • videocam
      MATLAB Programming Language

      11m26s
    • videocam
      Control System Toolbox

      11m26s
    • videocam
      Toolbox Identification System

      11m26s
    • videocam
      Fuzzy Logic Toolbox

      11m26s
    • videocam
      Neural Network Toolbox

      11m26s
    • videocam
      Robust Control Toolbox

      11m26s
    • videocam
      Source Code Generator

      11m26s
    • videocam
      Examples in MATLAB

      11m26s
  • 3. The PID Controller
    • videocam
      Theory of PID Control

      11m26s
    • videocam
      Characteristics of PID

      11m26s
    • videocam
      Determination of PID coefficient by practical method

      11m26s
    • videocam
      The Ziegler–Nichols method

      11m26s
    • videocam
      Implement PID in MATLAB

      11m26s
    • videocam
      Generate C/C++ code for PID

      11m26s
    • videocam
      Examples of PID

      11m26s
  • 4. The Fuzzy Controller
    • videocam
      Introduction to Fuzzy Control

      11m26s
    • videocam
      Fuzzy language variable

      11m26s
    • videocam
      Inference Fuzzy System

      11m26s
    • videocam
      Defuzzifier

      11m26s
    • videocam
      Build a Fuzzy Controller

      11m26s
    • videocam
      The hybrid fuzzy controller

      11m26s
    • videocam
      Fuzzy Logic Toolbox in MATLAB

      11m26s
    • videocam
      The practical application of Fuzzy

      11m26s
  • 5. Artificial Neural Network
    • videocam
      Introduction to Artificial Neural Network

      11m26s
    • videocam
      Architecture of ANN

      11m26s
    • videocam
      Perceptron Network Learning Principle

      11m26s
    • videocam
      Hebb Learning Principle

      11m26s
    • videocam
      Widrow-Hoff Learning Principle

      11m26s
    • videocam
      Back propagation algorithm

      11m26s
    • videocam
      Associated learning rules

      11m26s
    • videocam
      Recurrent neural network

      11m26s
    • videocam
      Neural Network Toolbox in MATLAB

      11m26s
    • videocam
      The practical application of ANN

      11m26s
Auto
Automated Control with PID-Fuzzy-Neural


Automatic control in engineering and technology is a wide generic term covering the application of mechanisms to the operation and regulation of processes without continuous direct human intervention.

It covers the range of application from a household thermostat controlling a boiler, to a large industrial control system with tens of thousands of input measurements and output control signals. In control complexity it can range from simple on-off control to multi-variable high level algorithms.

In the simplest type of an automatic control loop, a controller compares a measured value of a process with a desired set value, and processes the resulting error signal to change some input to the process, in such a way that the process stays at its set point despite disturbances.

This closed-loop control is an application of negative feedback to a system. The mathematical basis of control theory was begun in the 18th century, and advanced rapidly in the 20th.

Open-loop and closed-loop (feedback) control

Fundamentally, there are two types of control loop; open loop control, and closed loop (feedback) control.

In open loop control, the control action from the controller is independent of the "process output" (or "controlled process variable"). A good example of this is a central heating boiler controlled only by a timer, so that heat is applied for a constant time, regardless of the temperature of the building.

In closed loop control, the control action from the controller is dependent on the process output. In the case of the boiler analogy this would include a thermostat to monitor the building temperature, and thereby feed back a signal to ensure the controller maintains the building at the temperature set on the thermostat.

A closed loop controller therefore has a feedback loop which ensures the controller exerts a control action to give a process output the same as the "Reference input" or "set point". For this reason, closed loop controllers are also called feedback controllers.

The definition of a closed loop control system according to the British Standard Institution is 'a control system possessing monitoring feedback, the deviation signal formed as a result of this feedback being used to control the action of a final control element in such a way as to tend to reduce the deviation to zero.

The advanced type of automation that revolutionized manufacturing, aircraft, communications and other industries, is feedback control, which is usually continuous and involves taking measurements using a sensor and making calculated adjustments to keep the measured variable within a set range.

The theoretical basis of closed loop automation is control theory.

Discrete control (on/off)

One of the simplest types of control is on-off control. An example is the thermostat used on household appliances which either opens or closes an electrical contact.

Sequence control, in which a programmed sequence of discrete operations is performed, often based on system logic that involves system states. An elevator control system is an example of sequence control.

PID controller

A block diagram of a PID controller in a feedback loop, r(t) is the desired process value or "set point", and y(t) is the measured process value.

A proportional–integral–derivative controller (PID controller) is a control loop feedback mechanism (controller) widely used in industrial control systems.

A PID controller continuously calculates an error value e(t) as the difference between a desired setpoint and a measured process variable and applies a correction based on proportional, integral, and derivative terms, respectively (sometimes denoted P, I, and D) which give their name to the controller type.

The theoretical understanding and application dates from the 1920s, and they are implemented in nearly all analogue control systems; originally in mechanical controllers, and then using discrete electronics and latterly in industrial process computers.

Fuzzy Control System

A fuzzy control system is a control system based on fuzzy logic—a mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1, in contrast to classical or digital logic, which operates on discrete values of either 1 or 0 (true or false, respectively).

Fuzzy logic is widely used in machine control. The term "fuzzy" refers to the fact that the logic involved can deal with concepts that cannot be expressed as the "true" or "false" but rather as "partially true". Although alternative approaches such as genetic algorithms and neural networks can perform just as well as fuzzy logic in many cases, fuzzy logic has the advantage that the solution to the problem can be cast in terms that human operators can understand, so that their experience can be used in the design of the controller.

This makes it easier to mechanize tasks that are already successfully performed by humans.

Artificial Neural Network

Artificial neural networks (ANNs) or connectionist systems are computing systems inspired by the biological neural networks that constitute animal brains. Such systems learn (progressively improve performance) to do tasks by considering examples, generally without task-specific programming.

For example, in image recognition, they might learn to identify images that contain cats by analyzing example images that have been manually labeled as "cat" or "no cat" and using the results to identify cats in other images. They have found most use in applications difficult to express in a traditional computer algorithm using rule-based programming.

An ANN is based on a collection of connected units or nodes called artificial neurons (analogous to biological neurons in an animal brain). Each connection (synapse) between neurons can transmit a signal from one to another. The receiving (postsynaptic) neuron can process the signal(s) and then signal downstream neurons connected to it.

In common ANN implementations, the synapse signal is a real number, and the output of each neuron is calculated by a non-linear function of the sum of its input. Neurons and synapses may also have a weight that varies as learning proceeds, which can increase or decrease the strength of the signal that it sends downstream.

Further, they may have a threshold such that only if the aggregate signal is below (or above) that level is the downstream signal sent.

Table of Content

1. Introduction

  • The tasks to do in this course
  • Overview of automatic control
  • Theory of Automatic Control

2. MATLAB in automatic control

  • Introduction to MATLAB-Simulink
  • MATLAB Programming Language
  • Control System Toolbox
  • Toolbox Identification System
  • Fuzzy Logic Toolbox
  • Neural Network Toolbox
  • Robust Control Toolbox
  • Source Code Generator
  • Examples in MATLAB

3. The PID Controller

  • Theory of PID Control
  • Characteristics of PID
  • Determination of PID coefficient by practical method
  • The Ziegler–Nichols method
  • Implement PID in MATLAB
  • Generate C/C++ code for PID
  • Examples of PID

4. The Fuzzy Controller

  • Introduction to Fuzzy Control
  • Fuzzy language variable
  • Inference Fuzzy System
  • Defuzzifier
  • Build a Fuzzy Controller
  • The hybrid fuzzy controller
  • Fuzzy Logic Toolbox in MATLAB
  • The practical application of Fuzzy

5. Artificial Neural Network

  • Introduction to Artificial Neural Network
  • Architecture of ANN
  • Perceptron Network Learning Principle
  • Hebb Learning Principle
  • Widrow-Hoff Learning Principle
  • Back propagation algorithm
  • Associated learning rules
  • Recurrent neural network
  • Neural Network Toolbox in MATLAB
  • The practical application of ANN