Post Graduate Program in Motion Control by Skill Lync: Fee, Duration, How to Apply

Quick Facts

particular	details
Medium of instructions English	Mode of learning Self study	Mode of Delivery Video and Text Based

Course overview

Motion control applications are plenty, especially when it comes to production lines where efficiency, power, and accuracy of movements are a must. A sub-field of automation, motion control of autonomous vehicles facilitates effortless movement of heavy materials between workstations.

Do you want to have an in-depth understanding of this exciting field? Then, enrol yourself in the Master's Certification Program in Motion Control by Skill Lync. Within a duration of 12 months, this course seeks to impart detailed knowledge of the functioning of autonomous vehicles, including the role of ADAS or Advanced Driver Assistance Systems.

There are a total of six courses in the program, with each course covering different topics. As a part of this Masters Certification Program in Motion Control syllabus, you will work on four different projects.

The Master’s Certification Program in Motion Control fee options is flexible with basic, pro, and premium plans to choose from. The facilities and the access duration for the course materials depend upon the payment plan you choose. Course completion certificates will be offered to all, but only the top 5% of the class will receive a merit certificate.

Course and certificate fees

Flexible learning plans are available, and the course fee differs for each plan. You can choose the Basic plan, which offers 9 months of access to the course materials, the Pro plan which gives 18 months of access, or the Premium plan which provides lifetime access. The fee for each plan is not a one-time payment but must be paid every month for 10 months.

The facilities you receive will differ from plan to plan. To check them out, click here: https://skill-lync.com/computer-science-engineering-courses/masters-motion-control/about

Master’s Certification Program in Motion Control fee structure

Type of plan	Fee Amount (in INR) to be paid every month for 10 months
Basic	Rs. 17500
Pro	Rs. 22500
Premium	Rs. 27500

certificate availability

Yes

certificate providing authority

Skill Lync

Who it is for

The course is ideal for –

Undergraduates and graduates, preferably in fields like Electronics, Electrical, Mechanical, or Aerospace Engineering who wish to have a thorough understanding of autonomous vehicles’ motion control
PhD or Postgraduate learners of the same fields

Eligibility criteria

Though there are no specific Masters Certification Program in Motion Control eligibility criteria, it is preferred if you are an undergrad or graduate in electronics, electrical, mechanical, or aerospace engineering.

What you will learn

Knowledge of engineering

After completion of the Master’s Certification Program in Motion Control course, you will get a thorough understanding of the following–

Autonomous vehicle controls
Fundamentals of automotive systems and controls
Model predictive controls, including the bicycle model and non-holonomic model
Robust controls that deal with uncertain parameters of a system
Optimal controls
Deep reinforcement learning and control, which is a part of Machine Learning (ML)

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

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

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

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

AlphaZero
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

Admission details

Click here: https://skill-lync.com/computer-science-engineering-courses/masters-motion-control/about#pricing to visit the course website.
Opt for a one-on-one demo session to understand the course better.
Click on ‘Enrol Now’ and sign up or log into your Skill Lync account by giving simple details like your name, email address, etc.
Make the required fee payment based on the plan of your choice.
Secure a seat.

Filling the form

The online application must be filled to create an account with Skill Lync. You will need to enter details like your name, email address, and self-create password. Once that is done, just pay the fee, and you’re good to go.

Evaluation process

Skill Lync offers two kinds of Masters Certification Program in Motion Control certification. The certificate of completion will be offered to all learners. However, based on your performance in the different projects, you will be graded. The top 5% of the learners will be awarded a merit certificate.

How it helps

One major benefit of Skill Lync’s Masters Certification Program in Motion Control is that the curriculum is extensive. Within the 12-month period, you will receive information that is thorough and capable of landing you a dream Engineering job in the automotive or aerospace sector. Plus, since you will work on four different projects throughout the course duration, you will get the hands-on experience which is a major competitive advantage.

FAQs

What are the flexible payment options available?

Three different course plans are available – Basic, Pro, and Premium. The fee for each of the plans differs, which must be paid every month for 10 months.

What career options open up for me after the Master’s Certification Program in Motion Control completion?

After pursuing this course, you can build a career in engineering in automobile or aerospace companies. You can even join companies like consultancy services and Y-Combinator startups.

Does Skill Lync offer placement support?

Yes, job assistance facilities are available.

What projects will I work on?

You will work on four different projects, namely, DC motor analysis, HEV controller design, Mathematical model for active suspension of an automobile, and control strategy for active suspension of an automobile.

How can I know eligibility details regarding the Master’s Certification Program in Motion Control?

You can get on a call with one of the representatives by contacting Skill Lync at 8939850851.

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Post Graduate Program in Motion Control

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