Applied Control Systems for Engineers 2: autonomous vehicle | Mark Misin | Skillshare

Applied Control Systems for Engineers 2: autonomous vehicle

Mark Misin, Aerospace & Robotics Engineer

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105 Lessons (14h 11m) View My Notes
    • 1. Promo Video

      1:54
    • 2. Guide

      2:50
    • 3. PID VS Model Predictive Control (MPC) 1

      2:42
    • 4. Intro to MPC

      1:09
    • 5. Getting started with modelling a car 1

      2:44
    • 6. Getting started with modelling a car 2

      2:48
    • 7. Fundamentals of forces and moments 1

      8:43
    • 8. Fundamentals of forces and moments 2

      4:43
    • 9. Fundamentals of forces and moments 3

      11:52
    • 10. Setting stage for the car's lateral control 1

      6:07
    • 11. Setting stage for the car's lateral control 2

      10:13
    • 12. PID VS Model Predictive Control (MPC) 2

      1:15
    • 13. Setting stage for the car's lateral control 3

      10:41
    • 14. Setting stage for the car's lateral control 4

      1:42
    • 15. The general control structure for the vehicle's lateral control

      2:34
    • 16. Car model VS simplified bicycle model 1

      6:09
    • 17. Car model VS simplified bicycle model 2

      1:53
    • 18. Car model VS simplified bicycle model 3

      3:34
    • 19. Ackerman Steering

      1:52
    • 20. Longitudinal & lateral velocities of the bicycle model 1

      5:48
    • 21. Longitudinal & lateral velocities of the bicycle model 2

      4:14
    • 22. Equations of motion in the lateral direction

      3:39
    • 23. lateral & centripetal acceleration

      7:04
    • 24. centripetal acceleration intuition & mathematical derivation 1

      7:15
    • 25. Centripetal acceleration intuition & mathematical derivation 2

      8:58
    • 26. centripetal acceleration intuition & mathematical derivation 3

      20:51
    • 27. Modelling the front wheel of the vehicle 1

      4:40
    • 28. Rewriting lateral forces in terms of front wheel angles

      4:20
    • 29. Modelling the front wheel of the vehicle 2

      2:37
    • 30. Modelling the front wheel of the vehicle 3

      10:33
    • 31. Modelling the front wheel of the vehicle 4

      10:46
    • 32. From equations of motion to state-space equations 1

      1:35
    • 33. From equations of motion to state-space equations 2

      8:08
    • 34. From equations of motion to state-space equations 3

      5:57
    • 35. From equations of motion to state-space equations 4

      3:38
    • 36. The meaning of states 1

      5:20
    • 37. The meaning of states 2

      4:58
    • 38. Adding extra states to the system

      9:15
    • 39. Computing new states in the open loop system 1

      12:16
    • 40. Computing new states in the open loop system 2

      9:58
    • 41. Computing new states in the open loop system 3

      5:45
    • 42. Simplifying systems with small angle assumptions

      8:54
    • 43. Nonlinear VS Linear Time Invariant (LTI) models

      11:47
    • 44. Connecting LTI matrices with the vehicle's inputs

      6:29
    • 45. Getting LTI model using small angle approximation 1

      4:36
    • 46. Getting LTI model using small angle approximation 2

      9:17
    • 47. Getting LTI model using small angle approximation 3 + Recap

      8:18
    • 48. Model Predictive Control - Intro

      8:16
    • 49. Model Predictive Control - Thrust levels

      6:56
    • 50. Model Predictive Control - Cost function

      13:48
    • 51. Model Predictive Control - Cost function having several variables 1

      14:07
    • 52. Model Predictive Control - Cost function having several variables 2

      4:28
    • 53. Model Predictive Control - Cost function weights

      6:56
    • 54. Model Predictive Control - Horizon period

      10:52
    • 55. Model Predictive Control - measured VS predicted outputs (Kalman Filter)

      9:28
    • 56. Model Predictive Control - Quadratic VS other cost functions 1

      6:36
    • 57. Model Predictive Control - Quadratic VS other cost functions 2

      6:09
    • 58. Model Predictive Control - Quadratic VS other cost functions 3

      8:27
    • 59. Model Predictive Control - Quadratic VS other cost functions 4

      8:20
    • 60. Model Predictive Control - Math - 1

      6:46
    • 61. Model Predictive Control - Math - 2

      11:37
    • 62. Model Predictive Control - Math - 3

      14:12
    • 63. Model Predictive Control - Math - 4

      19:50
    • 64. Model Predictive Control - Math - 5

      12:26
    • 65. Model Predictive Control - Math - 6

      8:25
    • 66. Model Predictive Control - Math - 7

      8:29
    • 67. Model Predictive Control - Math - 8

      10:26
    • 68. MPC - extra intuition

      10:04
    • 69. Model Predictive Control - Math - 9

      2:37
    • 70. Model Predictive Control - Math - 10

      9:09
    • 71. Model Predictive Control - Math - 11

      16:18
    • 72. Model Predictive Control - Math - 12

      5:20
    • 73. Model Predictive Control - Math - 13

      14:29
    • 74. Model Predictive Control - Math - 14

      3:58
    • 75. Model Predictive Control - Math - 15

      8:15
    • 76. Model Predictive Control - Math - 16

      7:38
    • 77. Model Predictive Control - Math - 17

      0:55
    • 78. Model Predictive Control - Math - 18

      6:40
    • 79. Model Predictive Control - Math - 19

      8:21
    • 80. Model Predictive Control - Math - 20

      6:51
    • 81. Model Predictive Control - Math - 21

      9:49
    • 82. Derivation of the gradient of a quadratic vector-matrix form 1

      9:31
    • 83. Derivation of the gradient of a quadratic vector-matrix form 2

      5:06
    • 84. Derivation of the gradient of a quadratic vector-matrix form 3

      6:23
    • 85. Derivation of the gradient of a quadratic vector-matrix form 4

      9:46
    • 86. Derivation of the gradient of a quadratic vector-matrix form 5

      11:44
    • 87. Intro to (Linux & macOS Terminal) & (Windows Command Prompt)

      12:50
    • 88. Python Simulation Intro

      1:02
    • 89. Python installation instructions - Ubuntu

      6:45
    • 90. Python installation instructions - Windows 10

      6:34
    • 91. Python installation instructions - macOS

      8:13
    • 92. Intro to the simulator

      8:26
    • 93. Recap of the course

      6:15
    • 94. Code explanation 1 - general overview

      9:49
    • 95. Code explanation 2 - a function for storing the initial variables

      14:01
    • 96. Code explanation 3 - a function for generating trajectories

      18:31
    • 97. Code explanation 4 - a function for discrete state space matrices

      6:01
    • 98. Code explanation 5 - a function for generating the MPC cost function matrices

      16:23
    • 99. Code explanation 6 - a function for calculating new states

      16:44
    • 100. Code explanation 7 - the MAIN file 1

      15:47
    • 101. Code explanation 8 - the MAIN file 2

      10:55
    • 102. Code explanation 9 - the MAIN file 3

      11:37
    • 103. Code explanation 10 - Basic intro into Python animations & Plotting

      19:00
    • 104. Discussing the simulation results

      10:57
    • 105. PID VS Model Predictive Control (MPC) 3

      9:22

About This Class

If you have never been exposed to Control Engineering, please take my other course first, which is about introduction to Control:

Applied Systems Control for Engineers: Modelling + PID + MPC: Part 1

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The world is changing! The technology is changing! The advent of automation in our societies is spreading faster than anyone could have anticipated. At the forefront of our technological progress is autonomy in autonomous vehicles.

Welcome! In this course, you will be exposed to one of the most POWERFUL techniques there is that is able to guide and control systems precisely and reliably.

You are going to DESIGN, MASTER and APPLY a Model Predictive Controller (MPC) to an autonomous vehicle to avoid obstacles on a straight road at a constant forward speed.

You will LEARN the fundamentals and the logic of MPC that will allow you to apply it to other systems you might encounter in the future.

You need 3 things when solving an Engineering problem: INTUITION, MATHEMATICS, CODING! You can't choose - you really need them all. After this course, you will master MPC in all these 3 ways. That's a promise!

I'm very excited to have you in my course and I can't wait to teach you what I know.

Let's get started!