Applied Control Systems for Engineers 2: autonomous vehicle

Name: Applied Control Systems for Engineers 2: autonomous vehicle
Rating: 3 (1 reviews)
Author: Mark Misin

Mark Misin, Aerospace & Robotics Engineer

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

Applied Control Systems for Engineers 2: autonomous vehicle

Mark Misin, Aerospace & Robotics Engineer

1. Promo Video

2. Guide

3. PID VS Model Predictive Control (MPC) 1

4. Intro to MPC

5. Getting started with modelling a car 1

6. Getting started with modelling a car 2

7. Fundamentals of forces and moments 1

8. Fundamentals of forces and moments 2

9. Fundamentals of forces and moments 3

10. Setting stage for the car's lateral control 1

11. Setting stage for the car's lateral control 2

12. PID VS Model Predictive Control (MPC) 2

13. Setting stage for the car's lateral control 3

14. Setting stage for the car's lateral control 4

15. The general control structure for the vehicle's lateral control

16. Car model VS simplified bicycle model 1

17. Car model VS simplified bicycle model 2

18. Car model VS simplified bicycle model 3

19. Ackerman Steering

20. Longitudinal & lateral velocities of the bicycle model 1

21. Longitudinal & lateral velocities of the bicycle model 2

22. Equations of motion in the lateral direction

23. lateral & centripetal acceleration

24. centripetal acceleration intuition & mathematical derivation 1

25. Centripetal acceleration intuition & mathematical derivation 2

26. centripetal acceleration intuition & mathematical derivation 3

27. Modelling the front wheel of the vehicle 1

28. Rewriting lateral forces in terms of front wheel angles

29. Modelling the front wheel of the vehicle 2

30. Modelling the front wheel of the vehicle 3

31. Modelling the front wheel of the vehicle 4

32. From equations of motion to state-space equations 1

33. From equations of motion to state-space equations 2

34. From equations of motion to state-space equations 3

35. From equations of motion to state-space equations 4

36. The meaning of states 1

37. The meaning of states 2

38. Adding extra states to the system

39. Computing new states in the open loop system 1

40. Computing new states in the open loop system 2

41. Computing new states in the open loop system 3

42. Simplifying systems with small angle assumptions

43. Nonlinear VS Linear Time Invariant (LTI) models

44. Connecting LTI matrices with the vehicle's inputs

45. Getting LTI model using small angle approximation 1

46. Getting LTI model using small angle approximation 2

47. Getting LTI model using small angle approximation 3 + Recap

48. Model Predictive Control - Intro

49. Model Predictive Control - Thrust levels

50. Model Predictive Control - Cost function

51. Model Predictive Control - Cost function having several variables 1

52. Model Predictive Control - Cost function having several variables 2

53. Model Predictive Control - Cost function weights

54. Model Predictive Control - Horizon period

55. Model Predictive Control - measured VS predicted outputs (Kalman Filter)

56. Model Predictive Control - Quadratic VS other cost functions 1

57. Model Predictive Control - Quadratic VS other cost functions 2

58. Model Predictive Control - Quadratic VS other cost functions 3

59. Model Predictive Control - Quadratic VS other cost functions 4

60. Model Predictive Control - Math - 1

61. Model Predictive Control - Math - 2

62. Model Predictive Control - Math - 3

63. Model Predictive Control - Math - 4

64. Model Predictive Control - Math - 5

65. Model Predictive Control - Math - 6

66. Model Predictive Control - Math - 7

67. Model Predictive Control - Math - 8

68. MPC - extra intuition

69. Model Predictive Control - Math - 9

70. Model Predictive Control - Math - 10

71. Model Predictive Control - Math - 11

72. Model Predictive Control - Math - 12

73. Model Predictive Control - Math - 13

74. Model Predictive Control - Math - 14

75. Model Predictive Control - Math - 15

76. Model Predictive Control - Math - 16

77. Model Predictive Control - Math - 17

78. Model Predictive Control - Math - 18

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