• Trity Course Scilab IoT

    Scilab for the Internet of Things

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  • Trity Course RPi IoT

    Raspberry Pi for the Internet of Things (with Pi-3)

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  • Trity Course Scilab AI

    Artificial Intelligence with Scilab

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  • Trity Course Scilab NCV

    Numerical Computation and Visualization with Scilab

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  • Trity Course Scilab IP

    Scilab for Image Processing and Computer Vision

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  • Trity Course Scilab BDA

    Big Data Training Series : Practical Guide to Big Data Analytics with Pig Latin, Hive and Scilab

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  • Trity Course Scilab DM

    Scilab for Data Mining

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  • Python Deep Learning

    Python for Machine and Deep Learning

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Scilab Courses

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Scilab is an open source, cross-platform numerical computational package and a high-level, numerically oriented programming language. It can be used for signal and image processing, statistical analysis, Internet of Things, data mining, etc. In Trity Technologies we have developed more than 20 courses based on Scilab since last few years.

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Raspberry Pi Courses

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The Raspberry Pi is a series of credit card–sized single-board computers developed in the United Kingdom by the Raspberry Pi Foundation with the intent to promote the teaching of basic computer science in schools and developing countries. Our very first Raspberry Pi Training is the aplication in IoT, and we are extending the training into other fields from time to time. 

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E4Coder - Automatic Code Generation

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E4Coder is a set of tools that can be used to simulate control algorithms and to generate code for embedded microcontrollers running with or without a realtime operating system. Our course focus on using the block diagram for algorithms development and the codes would be automatically generated and downloaded into the embedded boards such as Arduino Uno. A mobile robot application would be used for the training for practical hands-on. 

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Control Systems Design with Scilab & Xcos

Scilab for Control System Analysis and Design, With Both Coding and Graphical Programming Approach

Getting started with Scilab for control systems design allows us to save more instead of using expensive software, and it could be freely used in industry as well!

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Having the Scilab to assist, the control design and simulation is much easier and cheaper. Furthermore, it does not stop in simulation, the capability extent to real-time implementation!


Course Synopsis


Designing a control system is a creative process involving a number of choices and decisions. These choices depend on the properties of the system that is to be controlled and on the requirements that are to be satisfied by the controlled system. The decisions imply compromises between some of the requirements.

It is therefore crucial to derive useful mathematical models of systems, signals and the performance requirements. For the success of a control system design, the depth of understanding of the dynamical properties of the system and the signals often is more important than the a priori qualifications of the particular design method.

Most often, control engineers utilize feedback when designing control systems. For example, in an automobile with cruise control, the vehicle's speed is continuously monitored and fed back to the system which adjusts the motor's torque accordingly. Where there is regular feedback, control theory can be used to determine how the system responds to such feedback. In practically all such systems stability is important and control theory can help ensure stability is achieved.


Course Objectives

The main objective of workshop is to introduce how to use Scilab and Xcos to teach and for research in Control. Through these techniques, participants will be able to use the Scilab and Xcos for Control Design and Analysis as the alternative for Matlab/Simulink.


Who Must Attend

Engineer, researchers, scientists, and managers from the academic, manufacturing, government and defense sectors who want to use or plan to use Control System design, and to learn the fundamental knowledge in Control System using Scilab/Xcos to replace Matlab/Simulink.


Candidates must have experience with basic computer operation. Preferably attended our Numerical Computation with SCILAB course


Course Outline

Overview of Control System Design

  • Computer aided control system
  • The flow of control design
  • Modeling of a system
  • Controller design
  • Simulation and controller tuning
  • Testing and implementation

Representing Model in Scilab and Xcos

  • Modeling of LTI systems using Scilab
  • Stability of dynamic systems
  • Poles and zeros of dynamic systems
  • System step response
  • Drawing block diagram in Xcos

System Identification and Physical Modeling

  • Mathematical modeling
  • System Identification
  • Physical modeling

Analysis with Visualisation Tools and GUI

  • Root locus and frequency response
  • Using the "rldesign" GUI to analyze dynamic systems

Linearization and Model Reduction

  • Linearization of simple model
  • Model reduction for effective analysis and simulation
  • Linearization of non-linear model

PID Controller design

  • Perform time and frequency domain analysis of LTI models
  • Introducing tools for designing feedback controllers
  • PID Controller design
  • Fine tuning PID controller


  • Using Xcos for Simulation
  • Continuous vs Discrete time


  • Implementing controller in real plant (OpenLABBox)
  • Implementation of controller in embedded system (Discussion)


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