# Master's Certification Program in Motion Control

This 12 month program equips the student with everything they need to know about motion control for autonomous vehicles

• Domain : CSE

### Program Outcomes

We are aware of how automation has started to take over the conventional methods by reducing human interventions. Motion controls which is a subfield of automation confines systems or sub-systems which involve moving parts. The term Motion control is not related to an individual component. Rather, it is related to a group of components that works together and creates controlled movements in a machine.

A Motion Control system includes components like a motion controller, an energy amplifier, prime movers/actuators which can be of an open-loop system or closed-loop system.

In an open-loop system, the controller sends the commands through an amplifier to prime movers/actuators but won’t know if it has actually achieved the desired motion. But in a closed-loop system that is not the case. Here, the input goes from the controller to the actuator and the final output from the actuator to the controller. So, based upon the input and output the controller tends to change the input without human intervention. Basically, they automate themselves.

We can see the usage of Motion Control in packaging, printing, textiles, and in the production and assembly industries. Also, they play an important role in robotics and CNC machine tools. In recent times, Motion Control technology is shifting towards electrical actuators such as DC/AC servo motors which is used in autonomous electric vehicles.

So, in motion control, the controller will contain the motion profile and target positions for the system and will plan the trajectory for the motor/actuator. Motion Controllers will often be closed-loop systems because they collect data from the output and act accordingly.

This program will provide complete knowledge on Motion control along with ADAS i.e Advanced Driver Assistance Systems. ADAS which is also a part of automation assists drivers in driving and parking functions. As automation has started to nose into automobiles, there are job openings for engineers in Automobile, Aerospace companies, etc. This program offers several courses that individually deal with the topics that are mentioned above. These courses are accompanied by projects where you will get hands-on experience that will help you stand out from the crowd.

List of Courses in the Program

1. Autonomous Vehicle Controls using MATLAB and Simulink
2. Automotive Systems and Controls
3. Model Predictive Controls
4. Robust Controls
5. Optimal Controls
6. Deep Reinforcement Learning and Control

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### List of courses in this program

#### 1Autonomous Vehicle Controls using MATLAB and Simulink

While Autonomous Vehicles are made up of many small parts that gives the car the credibility of being “autonomous” one aspect of it is it’s control system. Once the information is perceived the vehicle needs to respond to it. This is done by the controls of the vehicle, which mainly include the acceleration, brakes and steering wheel.

But how does the vehicle do this? If we want autonomous vehicles to be  a reality we will need the controls of the vehicle to be super efficient and responsive. To understand how we can make this happen and also gain knowledge on the controls of an autonomous vehicle, we at skill lync have introduced a course specifically dedicated to autonomous vehicle controls.

#### 2Automotive Systems and Controls

This course is designed in such a way that, the students get to know the fundamentals of control systems. Further, the course will provide a basic understanding of the building blocks of control theory, time and frequency-domain modeling, and design, transfer function, state-space, root-locus, bode plots, Nyquist plots, time response, frequency response, steady-state error analysis, and digital control systems.

• Modeling techniques
• System Analysis
• Design techniques
• Digital Control Systems

#### 3Model Predictive Controls

Model Predictive Controls or MPC controls a process and satisfies a set of constraints. The usage can be seen in industries like chemical plants and oil refineries.

In this course, the students will learn the basic motion planning concepts, the Non-holonomic model, Bicycle Model, and proceeds on to writing single-step, multi-step goal-reaching. This will help students get an understanding, intuition in convex optimization, Tools like CVX optimization and visualizing the motion of vehicles, and writing clean code in MATLAB. Further, the course implements MPC in CARLA for urban scenarios.

• Linear Algebra
• Control Analysis
• Motion Model and Kinematics
• Constraint-based MPC
• Multi-Stage MPC

#### 4Robust Controls

Robust controls explicitly deal with uncertain parameters of a system. They help in achieve robust performance within the boundary conditions. In this course, the students will be introduced to the fundamental and advanced concepts in robust control theory and optimal control for practical applications.

• Robust Control Fundamentals
• Implementation of the distillation column in MATLAB
• Develop a MATLAB/Simulink model for robust control

#### 5Optimal Controls

Optimal control theory is a branch of mathematical optimization that deals with the problem of finding a control law for a given system with the optimal criteria of the system.  This course helps develop theoretical and applied skills in optimal control and its applications in autonomous driving.

At the end of this course, students will be able to solve optimal control problems in a range of fields, especially those encountered in autonomous driving, aerospace, and mechanical engineering projects.

• Optimal controls for dynamic and Optimal Feedback control
• Dynamic Programming
• Optimal Filtering theory

#### 6Deep Reinforcement Learning and Control

Deep Reinforcement Learning and Control is a part of machine learning. Here, both reinforcement learning and deep learning are combined together and it makes decisions out of unstructured input data. The algorithms in Deep Reinforcement Learning and Control take in very large inputs and optimize them to provide the desired output. We can see the usage of Deep Reinforcement Learning and Control in fields like robotics, education, transportation, finance, healthcare, video games, natural language processing, computer vision, etc.

During the course period, the students get to work on computer languages like OpenAI, Python, C++. Further in the course, you will learn about:

• Implementing and experimenting with existing algorithms for learning control policies guided by reinforcement, demonstrations, and intrinsic curiosity.
• Evaluate the sample complexity, generalization, and generality
• Markov Decision processes
• Monte Carlo Learning
• OpenAI Gym Tool
• Deep NN for RL
• Deep Q Learning
• Model-Based Reinforcement Learning

### Some of the projects that you will work on

1. Analysis of DC Motor

Highlights

Key Highlights:

• Requires knowledge gained from the first 6 weeks to solve the project.
• Identifying and analyzing the second-order transfer functions
• Observing the system response and extract conclusions.
• Compute performance parameters.
• Obtain time domain relations from the given transfer function equivalents.
• Observe the effects of modifying the system transfer function on overall response of the system.
• Use of computer software to correlate the analytical results with simulated data.

Deliverables:

• Using second-order structure given, compute percent overshoot, rise time, peak time, and settling time.
• Using inverse Laplace transforms, obtain time domain expression for the given transfer function relating rotational speed to armature voltage.
• Plot the step response of the DC motor using MATLAB and note down the observations and articulate inferences.
• Evaluate the effect of adding an extra pole and a zero on the overall response of the system in MATLAB and provide insights.

2. Controller Design for an HEV

Highlights

Key Highlights:

• Use of time-domain analysis to observe the response of the system.
• Use of frequency-response techniques to identify system performance measures via analytical analysis and computer software.
• Observe the impact of modifying suggested parameters of the system using computer program.
• Use knowledge of controller design techniques to change system transfer function.
• Obtain inferences on the basis of observations made in with and without control scenarios.
• Use of obtained knowledge to provide suggestions as per the control system fundamentals.

Deliverables:

• Obtain root-locus of the given system transfer function in MATLAB.
• Observe the performance parameters in Bode diagrams and provide conclusions.
• Obtain MATLAB plots for the same.
• Record the impact of modifying input characteristics (as suggested) on frequency-response in MATLAB.
• Explain stepwise procedure on how to add an integral control to the system transfer function to obtain a desired transfer function.
• Modify the system response as per given system objectives in MATLAB and explain stepwise process.
• Compare the responses obtained with and without controller and provide insights on the same.

3. Mathematical model for active suspension of an automobile

Highlights

Key Highlights:

• Requires knowledge gained from the first 6 weeks to solve the project.
• Identifying and analyzing basic elements for deriving the mathematical model.
• Using mass-spring-damper as the fundamental element of design.
• Updating knowledge of active suspension models of an automobile using existing techniques in the literature.
• Identifying the parameters to define robust control.

Deliverables:

• Step-by-step explanation of using mass-spring-damper as the fundamental element of active suspension design of an automobile.
• Explanation of full, half and quarter car suspension models.
• Explanation of performance variables for a quarter car suspension model.
• Define parameters injecting uncertainties/disturbances to the system.
• Formulate a functional block diagram to implement robust control for the same.

4. Defining a control strategy for active suspension of automobile

Highlights

Key Highlights:

• Using the knowledge obtained in the previous discussions to implement H_{\infty} based control for the formulation in Project 1.
• Using computer software to test the formulation in simulation environment.
• Testing the model for defined disturbances.
• Providing inferences on the basis of observations recorded in computer simulations.

Deliverables:

• Step-by-step process explaining implementation of H_{\infty} control architecture.
• Using MATLAB to implement the same and visualize, record results.
• Observe the impact on the automobile performance (handling, user comfort and trade-off) due to the road disturbances and displacement, acceleration sensor noises using MATLAB.
• Compare the responses obtained with the controller designed for user comfort, handling and balanced design using MATLAB.

5. Analysis of a hybrid electric vehicle subsystem

Highlights

Key Highlights:

• Requires knowledge gained from the first 7 weeks to solve the project.
• Identifying and analyzing the second-order transfer functions.
• Observing the system response and extract conclusions.
• Compute performance parameters.
• Obtain time domain relations from the given transfer function equivalents.
• Observe the effects of modifying the system transfer function on overall response of the system.
• Use of computer software to correlate the analytical results with simulated data.

Deliverables:

• Using second-order structure given, compute percent overshoot, rise time, peak time, and settling time.
• Draw the root locus, Bode and Nyquist plots using MATLAB and provide inferences.
• Plot the step response of the HEV using MATLAB and note down the observations and articulate inferences.
• Evaluate the effect of adding an extra pole and a zero on the overall response of the system in MATLAB and provide insights.

6. Control design for futuristic drone operations

Highlights

Key Highlights:

• Use of time-domain analysis to observe the response of the system.
• Use of frequency-response techniques to identify system performance measures via analytical analysis and computer software.
• Observe the impact of modifying suggested parameters of the system using computer program.
• Use knowledge of controller design techniques to change system transfer function.
• Obtain inferences on the basis of observations made in with and without control scenarios.
• Use of obtained knowledge to provide suggestions as per the control system fundamentals.

Deliverables:

• Obtain step response of the given system transfer function with varying step amplitudes in MATLAB.
• Observe the performance parameters in root locus, Bode and Nyquist diagrams in MATLAB and provide conclusions.
• Design a controller for a reliable drone operation with given set of constraints in MATLAB.
• Evaluate the overall performance of the drone using the designed controller in MATLAB.
• Compare the responses obtained with and without controller and provide insights on the same.

7. Green Wave Traffic Assist

Highlights

Key Highlights:

• Use algorithmic approach of thinking
• Use design thinking define requirements and implement algorithm
• Use a simple kinematics concept to implement algorithms.
• Gain proficient experience in Matlab scripting
• Use matlab to generate simulation results.

Deliverables:

• Show a flowchart diagram for the algorithm’s pseudocode.
• Plot Green wave recommended velocity vs distance to traffic light.
• Comments and comparison of output graphs\
• Plot relevant flags and messages with respect to the scenario

8. Autonomous Vehicle Model

Highlights

In this project students will integrate the various features together to develop an integrated automated driving model. This model will include previously discussed highway assist (ACC + LCA) + Auto lane change + Predictive speed assist + Intelligent speed assist. Students will then go ahead and test different scenarios that cover all the control functionalities for every feature and provide plots to show the working of the model.
students will develop a new feature model for a Minimum risk maneuver as covered in week 11. The flow chart & pseudocode for the MRM will be provided. Students would first have to implement a function and perform unit testing on the model to show all the states are working. Finally, a scenario for MRM will be given where the student would have to integrate the MRM block.

Key Highlights:

• Complete lateral and longitudinal feature design implementation
• Feature model integration using matlab and simulink
• Unit testing for functional blocks
• New feature design requirements and implementation
• Complete simulation for lateral , longitudinal and MRM features.

Deliverables:

• Model implementation and executable
• Show suitable plots and simulation results for functional testing
• Show results for defined scenarios
• Flow chart for new feature implementation and simulink implementation
• Feature simulation

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# $203.55 Per month for 10 months • Job Assistance : Available • Access Duration : 9 Months • Mode of Delivery : Online • Project Portfolio : Available • Certification : Available • Individual Video Support : 8/Month • Group Video Support : 8/Month • Email Support : Available • Forum Support : Available Premium Lifetime Access #$339.25

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• Paid Internship : 3 Months

### 1. Autonomous Vehicle Controls using MATLAB and Simulink

#### 1Course Overview and Classical control

• Course overview:
1. Introduction to all the topics
2. Motivation and why to learn them
3. Overview of Automotive Systems engineering
4. Program Management – systems Engineering
• Classical controls theory overview
1. Stability pole zeros
2. Transient performance
3. Disturbance and tracking
4. PID systems
5. Gain selection and tuning
6. Examples comparing P,PI,PD,PID

#### 2Longitudinal Controller design

• Longitudinal dynamic model
• Aero drag and rolling resistance
• Linearizing longitudinal model
• Normal cruise control project
• Performance analysis using a step response

Design and develop ACC control algorithm and model in Simulink

• Feature overview: Implementation, Sensor sets etc.
• Speed control model
• Switching logic in state flow techniques
• Controller design and tuning
• Performance tuning using feed forward method

#### 4Advanced ACC – modifying the ACC feature to next level

• The CACC overview: Cooperative acc model
• Logic implementation
• Complete model with ego vehicle and target vehicle in Simulink
• Simulation scenarios and MIL

#### 5Lateral control for vehicle. – Geometric method

• Geometric control methods
• Pure pursuit controller
• Lane keep system using Pure pursuit.
• Stanley controller
• LKS using Stanley.

#### 6Lateral controller model for Vehicles- dynamic modeling

• Lateral control model elements and overview
• Bicycle model
• Tire model
• State equation for lateral control model
• Introduction to MPC
• Controller design using MPC
• Integration and modelling in MATLAB

#### 7Lane Centering Assist

• Develop the level 2 model for lane cantering assist
• Lane centre assist logic
• Feature boundary diagram and functions
• Steering path polynomial
• Mode manager and fault manager design
• Switching logic for scenarios

• ### Intro to electronic horizon, HD maps

#### 9LCA modification: Assisted lance biasing and Assisted lane change

• Assisted lane biasing logic and implementation
• Assisted lane change logic
• Path planning for ALC
• Implement path planning function with LCA model

#### 10Combined Controller – 5 DOF

• Introduce a combined model of lateral + longitudinal control
• Vehicle dynamic derivation for state matrices
• State space mathematics for 5 DOF system
• Implement a single controller system in simulink

#### 11Advanced topics in controls for autonomous driving- part 1

• Predictive speed assist
• Introduction to predictive speed assist and intelligent speed assist
• Curve speed control derivation
• Pseudo code for PSA and ISA
• Integration of PSA with velocity control logic.
• Control for round about scenarios
• Minimum risk manuevers

#### 12Advanced topics in controls for autonomous driving - part 2

• AV special applications
• Off roach mining
• Logistics and supply chain
• Agricultural activities
• Smart mobility
• AV special ODs
• Toll gates
• Other control techniques
• Non linear mpc
• Sliding mode control
• Future topics for research
• Deep reinforcement learning
• machine learning applications in AVs

### 2. Automotive Systems and Controls

#### 1Introduction to Modelling techniques – Part I

Introduction & modelling techniques:

• Motivation
• History of control systems
• Preliminaries
• Modeling in frequency domain
• Laplace transform
• Transfer function
• Examples
• Nonlinearities
• Linearization
• Case study

#### 2Modelling techniques – Part II

Modelling techniques:

• Modeling in time domain
• General state-space representation
• Application of the state-space representation
• Conversion to transfer functions
• Conversion from transfer functions
• Linearization
• Case study

#### 3System analysis – Part I

System analysis:

• Time response
• Poles, zeros and system response
• First-order systems
• Second-order systems - General
• Underdamped second-order systems
• System response with additional poles
• System response with zeros
• Effects of nonlinearities
• Laplace transform solutions
• Time domain solutions
• Case study

#### 4System analysis – Part II

System analysis:

• Reduction of multiple subsystems
• Block diagrams
• Analysis and design of feedback systems
• Signal-flow graphs (SFGs)
• Mason’s rule
• SFGs of state-equations
• Alternative representations in state-space
• Similarity transformations
• Case study

#### 5System analysis – Part III

System analysis:

• Stability
• Routh-Hurwitz criterion
• Special cases of Routh-Hurwitz criterion
• Stability in state-space
• Case Study

#### 6System analysis – Part IV

System analysis:

• Steady-state errors for unity feedback systems
• Static error constants and system type
• Error specifications
• Error for disturbances
• Steady-state errors for nonunity feedback systems
• Sensitivity
• Steady-state errors for systems in state-space
• Case study

#### 7Design techniques – Part I

Design techniques:

• Root locus techniques
• Definition of root locus
• Properties of root locus
• Sketching the root locus
• Refining the sketch
• Transient response design via gain adjustment
• Generalized root locus
• Root locus for positive-feedback systems
• Pole sensitivity
• Case study

#### 8Design techniques – Part II

Design techniques:

• Design via root locus
• Improving transient response via cascade compensation
• Improving steady-state error and transient response
• Feedback compensation
• Physical realization of compensation
• Case study

#### 9Design techniques – Part III

Design techniques:

• Frequency response techniques
• Asymptotic approximations: Bode plots
• Nyquist criterion
• Sketching Nyquist plot
• Stability analysis
• Gain margin and phase margin via Nyquist plot
• Stability, gain margin and phase margin via Bode plots
• Relation between closed-loop transient and frequency responses
• Relation between closed-loop and open-loop frequency responses
• Relation between closed-loop transient and open-loop frequency  responses
• Steady-state error characteristics from frequency response
• Systems with time delay
• Obtaining transfer functions experimentally
• Case study

#### 10Design techniques – Part IV

Design techniques:

• Design via frequency response
• Transient response via gain adjustment
• Lag compensation
• Case study

#### 11Design techniques – Part V

Design techniques:

• Design via state-space
• Controller design
• Controllability
• Alternative approaches to controller design
• Observer design
• Observability
• Alternative approaches to observer design
• Steady-state error design via integral control
• Case study

#### 12Digital control systems

Digital control systems:

• Modeling the digital computer
• The z-transform
• Transfer functions
• Block diagram reduction
• Stability