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