Post Graduate Program in Motion Control BY Skill Lync in 2024, Program, Course, Fees, Admission, Syllabus

The course is ideal for –

• Students and recent graduates who are interested in learning everything there is to know about motion control in autonomous cars, preferably in the fields of electronics, electrical, mechanical, or aerospace engineering.
• Postgraduate or PhD students studying the same topics.

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• Click here: https://skill-lync.com/computer-science-engineering-courses/masters-motion-control/about#pricing to visit the course website.
• For a more thorough understanding of the course, choose a private demo session.
• By entering basic information like your name, email address, etc., you may create or log into your Skill Lync account by clicking the “Enrol Now” button.
• Pay the necessary fee in accordance with the plan of your choice.
• Settle down.

To make an account with Skill Lync, you must fill out the online form. Details like your name, email address, and custom password must be entered. After completing that, simply pay the cost, and you’re ready to travel.

The syllabus

Course 1: Automotive Systems and Controls using MATLAB/Simulink

Week 01 – Introduction to Modelling Techniques – Part I

• Introduction and Preliminaries
  • Control Systems
  • Modeling a Physical System
  • Laplace Transform
  • Control System Design
  • Types of Control Strategies

Week 02 – Introduction to Modelling Techniques – Part II

• Modeling Techniques: Nonlinearities and Control
• Mathematical Modelling of Systems
  • Mechanical
  • Electrical
  • Fluid system
  • Thermal system
• Nonlinearities
• Effects of Nonlinearities
• Linearization

Week 03 – System Analysis – Part I

• System Analysis: Model Reductions
  • Block Diagrams
  • Signal-Flow Graphs (SFGs)
  • Mason’s Rule
  • SFGs of Differential Equations

Week 04 – System Analysis – Part II

• System Analysis: Fundamental Elements
  • Poles, Zeros, and System Response
  • First-Order Systems
  • Second-Order Systems
  • Higher-Order Systems
  • System Response with Zeros
  • Analysis of Block Diagrams
  • Modifying the System Response

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Week 05 – System Analysis – Part III

• System Analysis: Stability
  • Routh-Hurwitz Criterion
  • Special Cases of Routh-Hurwitz Criterion
  • Steady-State Errors for Unity Feedback Systems
  • Static Error Constants and System Type
  • Steady-State Errors for Non-Unity Feedback Systems
  • Sensitivity

Week 06 – System Analysis – Part IV

• System analysis: Root locus
  • Definition of Root Locus
  • Conditions of Root Locus
  • Sketching the Root Locus – Part I
  • Sketching the Root Locus – Part II
  • Pole Sensitivity

Week 07 – Design Techniques – Part I

• Design Techniques: Bode and Nyquist Plots
  • Bode Plots
  • Gain Margin and Phase Margin
  • Polar Plots
  • Nyquist Plots
  • Stability Analysis using Nyquist Plots

Week 08 – Design Techniques – Part II

• Design Techniques: Compensators – Part I
  • Automatic Control Systems
  • PID Controller Design using Ziegler-Nichols Rules
  • Lag Compensation
  • Lead Compensation
  • Lag-Lead Compensation
  • Computer-Based Design
  • Physical Realization of Compensation
  • Systems with Time-Delay

Week 09 – Design techniques – Part III

• Design Techniques: Compensators – Part II
  • Gain Adjustment
  • Lag Compensation
  • Lead Compensation
  • Lag-Lead Compensation

Week 10 – State-Space representation – Modelling and analysis

• State-Space Representation: Modelling and Analysis
  • State-Space Representation
  • State-Space to Transfer Functions
  • Transfer Functions to State-Space
  • Alternative Representations in State-Space
  • Controllability
  • Observability
  • Stability in State-Space

Week 11 – State-Space representation – Design

• State-Space Representation: Design
  • Similarity Transformations
  • Controller Design
  • Alternative Approaches to Controller Design
  • Observer Design
  • Alternative Approaches to Observer Design

Week 12 – Digital Control Systems and Future Scope

• Digital Control Systems and Future Scope
  • Modeling the Digital Computer
  • The Z-Transform
  • Transfer Functions
  • Stability
  • Transformations

Course 2: Model Predictive Controls for Autonomous Driving

Week 1 – Introduction to Linear Algebra and Controls

• Linear Algebra Refresher
• Basics of Controls

Week 2 – Motion Models for Autonomous Driving

• Motion Models
• Lateral and Longitudinal Dynamics

Week 3 – Getting a Hold on LQR for Goal Reaching

• Linear Quadratic Regulator
• Problem Formulation
• Stability and Controllability of LQR
• Convergence of LQR
• Using LQR and Motion Model for a goal-reaching problem

Week 4 – Introduction to Convex and Non Convex Optimization

• Basics of Convex Optimization
• Introduction to Non-Linear Cost Function

Week 5 – Introduction to MPC

• Introduction to MPC
• Feedback in Optimal Control
• The Specialty of MPC’s Model
• Structure of LQR and What MPC Adds to it?
• Online Feedback

Week 6 – MPC for Goal Reaching Problem

• Sequential Quadratic Programming
• Tutorial on CVX_OPT
• Coding MPC
• MPC for Multiple-step Problem

Week 7 – Constrained MPC

• QP to MPC
• Constraints of MPC
• Static Obstacle Avoidance – Theory

Week 8 – MPC for Collision Avoidance

• MPC for a Static Obstacle in Practice
• Multiple Static Obstacle Condition
• MPC for Dynamic Obstacles
• Adding Pedestrians to Constraints

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Week 9 – Lateral Conditions of MPC

• Lane Keeping Constraints in MPC
• Lane Change condition

Week 10 – Uncertainty in MPC

• Adding Uncertainty to a Motion Model
• Types of MPC and Different Cost Formulations

Week 11 – Setting Up CARLA Simulator

• Setting up CARLA
• Using CARLA to run a small client-server controller
• Testing PID in CARLA

Week 12 – Future Scope of MPC

• Future Scope of MPC
• MPC with Deep Learning Approach
• Details on Project Implementation

Course 3: Robust Controls

Week – 1 – Introduction

• Motivation
• Preliminaries
• Engineering background of robust control in automobiles
• Techno-commercial evaluations
• Business implications
• Future scope
• Fundamentals of non-linear/linear systems
• Linear algebra and function analysis
• Basic control theory
• System performance
• Mathematical modelling

Week – 2 – Robust control Fundamentals – Part 1

• Robust control fundamentals:
  • Time domain and frequency domain properties
  • Stabilization of linear systems
  • Optimization theory
  • Basics of convex analysis and linear matrix inequalities (LMI)

Week – 3 – Robust control Fundamentals – Part 2

• Robust control fundamentals:
  • Algebraic Riccati equations
  • Limitations of feedback control
  • Robust Analysis: Small gain principle
  • Robust Analysis: Lyapunov method
  • Robust control of parametric system

Week – 4 – Robust control Fundamentals – Part 3

• Robust control theory:
  • H_{2} control
  • H_{\infty} control

Week – 5 – Hands-on demonstration

• Implementation of triple inverted pendulum in MATLAB/Simulink
  • Explanation of the design theory
  • Computer simulations

Week – 6 – Hands-on – 2

• Implementation of triple inverted pendulum in MATLAB/Simulink
  • Explanation of the design theory
  • Computer simulations

Week – 7 – Advanced Robust control Theory – Part 1

• Robust control fundamentals:
  • Mu synthesis
  • Regional pole placement
  • Gain scheduled control
  • Disturbance Observers

Week – 8 – Advanced Robust control Theory – Part 2

• Robust control fundamentals:
  • Repetitive control for time-delayed systems
  • H_{\infty} loop shaping control
  • Integral quadratic constraint (IQC) control

Week – 9 – Hands-on – 3

• Implementation of hard disk drive in MATLAB/Simulink
  • Explanation of the design theory
  • Computer simulations

Week – 10 – Hands-on – 4

• Implementation of distillation column in MATLAB/Simulink
  • Explanation of the design theory
  • Computer simulations

Week – 11 – Hands-on – 5

• Implementation of a rocket in MATLAB/Simulink
  • Explanation of the design theory
  • Computer simulations

Week – 12 – Hands-on – 6

• Implementation of flexible link manipulator in MATLAB/Simulink
  • Explanation of the design theory
  • Computer simulations

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Course 4: Optimal Controls

Week 01 – Introduction to optimal controls

• Terminology and notations
• General mathematical expressions
• Optimal control for static systems
• Constrained parameter optimization
• Unconstrained parameter optimization

Week 02 – Optimal controls for dynamic systems (Part 1)

• Calculus of variations
• Brachistochrone theory and example

Week 03 – Optimal controls for dynamic systems (Part 2)

• Two-point boundary value theory
• Introduction to Lagrange multipliers
• Geometric meaning of Lagrange multipliers
• Equality & inequality constraints

Week 04 – Optimal feedback control

• Neighbouring extremals
• Hamilton-Jacobi-Bellman (HJB) equation
• Pontryagin’s minimum/maximum principle
• Transversality conditions

Week 05 – Dynamic programming

• Principle of optimality
• Dynamic programming theory
• Connection between dynamic programming and Pontryagin’s maximum principle

Week 06 – Linear Quadratic Regulator (LQR) Problems (Part 1)

• LQR theory and derivation
• Riccati equations and their properties
• Linear systems with quadratic criteria

Week 07 – Linear Quadratic Regulator (LQR) Problems (Part 2)

• Case with input constraints – the minimum principle
• Case with minimum time constraints
• Case with path constraints
• Singular arcs

Week 08 – Introduction to stochastic processes

• Expectation Operator
• Gaussian Random Variables
• Stochastic Processes

Week 09 – Optimal filtering theory (Part 1)

• Introduction
• Estimation of parameters using weighted least squares
• Linear Kalman filter derivation

Week 10 – Optimal filtering theory (Part 2)

• Extended Kalman Filter (EKF) derivation
• Duality of optimal control and optimal estimation

Week 11 – Linear Quadratic Gaussian (LQG)

• LQG theory and design
• Infinite horizon LQG
• Optimal feedback control in the presence of uncertainty

Week 12 – Tracking/disturbance rejection/Model Predictive Control (MPC)

• Introduction and fundamentals of model predictive control for autonomous vehicles
• Autonomous trajectory tracking and path following using MPC
• Vehicle Lateral and Longitudinal Control

Course 5: Deep Reinforcement Learning and Control

Week 01 – Introduction, Course Overview, and Reinforcement Learning Motivation

• Introduction to the course
• What to expect, and pre-requisites
• Motivation and fundamental framework of Reinforcement Learning
• Roots from behaviourism
• Different module definitions
• Terminology and Notation
• Mathematical description
• Goal of RL
• Real-world comparison

Week 02 – MDP

• Markov Decision Process
• Markov Property
• Rewards, decision and transition probability relationship
• Episodic Horizon and continuing tasks
• Intro to partially observable MDPs

Week 03 – RL Learning Process, Value Function and variants

• Bellman Equation
• State Value function (Math and applied)
• State-action value function (Q) (Math and applied)
• Off- and On-policy RL
• Exploration – exploitation tradeoff
• Exploration strategies
• e-greed algorithm

Week 04 – NN, Policy Gradients and Baselines

• Introduction to policy gradients
• Policy gradients and RL
• Likelihood ratio
• Dealing with Variance in PG
• Baselines
• Neural networks and PG (Python and Tensorflow/Pytorch)

Week 05- Monte Carlo, Temporal Difference, Actor-critic and Value Function Methods

• DQN, SARSA and REINFORCE algorithms
• IID assumptions and experience-replay
• Monte Carlo estimations
• Eligibility traces
• Advantage estimation
• Introduction to Monte Carlo tree search

Week 06- Deep Q-learning Algorithm, Application and Implementation

• DQN Algorithm
• Introduction to RL in MATLAB
• Problem formulation as MDP ( Theory and MATLAB)
• Implement DQN and different reward structures to see convergence and difference on a MATLAB grid-world environment

Week 07- RL in Continuous Space

• Traditional Algorithms in continuous MDP domains
• Episodic estimates and extensions to continuous domains
• Policy gradient variations
• Value-based extensions

Week 08- Policy-based Methods, Actor-critic and Algorithm

• Proximal Policy Optimization
• Trust-region policy optimization
• Extensions to Actor-critic algorithms
• Introduction to Human-level control learning paper
• Deep deterministic Policy gradient algorithm

Week 09- Model-based Reinforcement Learning

• Model definitions
• Tabular implementation
• DYNA architecture
• DYNA algorithmic implementations
• Dealing with uncertain and/or dynamic models

Week 10- Case Studies and Use Cases

• Alpha Zero
• AlphaGo
• Atari state-of-the-art implementations
• Industrial Robots
• Manufacturing Applications
• Real-world implementations
• Introduction to “ RL that matters” paper and corresponding literature
• Thought process and problem formulation for an autonomous vehicle problem

Week 11- Practical Implementation

• Continuous actor-critic algorithm formulation for an autonomous vehicle problem
• Programming in python
• Learning with different optimization techniques like RMSProp / ADAM
• Implementation using Tensorflow/Pytorch libraries

Week 12 – Extensions to RL

• Multi-agent RL
• Hierarchical Learning
• Transfer and curriculum Learning
• Distributed implementations
• Sim-2-real architectures

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Evaluation Process

Two different types of Masters Certification Programme in Motion Control certification are available from Skill Lync. The completion certificate will be made available to all students. However, you will receive a grade based on how well you execute in the various projects. An honour certificate will be given to the top 5% of students.

How It Helps

The broad curriculum of Skill Lync’s Post Graduate Programme in Motion Control is a key advantage. You will receive detailed information that can help you land a dream engineering job in the automotive or aerospace industries throughout the course of the next 12 months. Additionally, you will gain practical experience working on four distinct projects over the course of the course, which is a significant competitive advantage.

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Post Graduate Program in Motion Control FAQ’S

What are the different types of motion control systems?

There are two different types of motion control systems: open loop and closed loop systems. The basic difference between the two is that open loop systems do not use feedback; closed systems do use feedback.

What is motion control in engineering?

Motion control is an engineering technology that is highly utilized in the industrial sector. A motion control system is any system that entails the use of moving parts in a coordinated way.

What is applied electrical motion and control management?

Applied Electrical Motion and Control Management from Conestoga College is designed for graduates of engineering degree or diploma programs wanting to expand their knowledge into the applications of AC and DC power for motion and propulsion.

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