Applied Control Systems 3: UAV drone (3D Dynamics & control)

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Mark Misin Engineering Ltd

27:17:13

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1 - Drone architecture from Control Systems point of view

1 - Introduction.mp4

03:39

2 - UAV configuration inertial VS body frame.mp4

06:09

3 - Inputs and outputs of a 6 Degree of Freedom UAV drone.mp4

03:31

4 - Propeller rotation directions 1.mp4

02:06

5 - Propeller rotation directions 2 Helicopter example.mp4

03:26

6 - 1st control action Thrust.mp4

03:51

7 - 2nd control action Roll.mp4

02:40

8 - 3rd control action Pitch exercise.mp4

01:11

9 - 3rd control action Pitch solution 4th control action Yaw exercise.mp4

02:15

10 - 4th control action Yaw solution.mp4

01:33

11 - Rotation vector direction.mp4

03:59

12 - Clarification on measuring with respect to body or inertial frames.html

13 - Global view of the drones control architecture.mp4

03:32

14 - Follow up.mp4

00:58

2 - Fundamental kinematics & dynamics equations for a 6 DOF system Newton Euler

15 - Kinematics VS Dynamics.mp4

03:48

16 - Measuring the UAVs position exercise.mp4

02:31

17 - Measuring the UAVs position solution.mp4

03:55

18 - Intro to describing attitudes 1 exercise.mp4

03:36

19 - Intro to describing attitudes 2 solution new exercise.mp4

02:27

20 - 2D rotation matrix formulation solution new exercise.mp4

03:45

21 - From 2D to 3D rotations solution new exercise.mp4

02:25

22 - 3D rotation matrix formulation about the Z axis 1 solution.mp4

01:47

23 - 3D rotation matrix formulation about the Z axis 2 solution.mp4

02:02

24 - Projecting from 3D to 2D exercise.mp4

03:12

25 - Projecting from 3D to 2D solution constructing Rx and Ry matrices exercise.mp4

03:45

26 - Constructing Ry matrix solution.mp4

04:16

27 - Constructing Rx matrix solution.mp4

02:39

28 - Orthonormal matrices exercise.mp4

02:14

29 - Orthonormal matrices solution.mp4

01:07

30 - 3D rotation sequence 1 exercise.mp4

02:43

31 - 3D rotation sequence 2 solution.mp4

08:30

32 - 3D rotation sequence example exercise.mp4

12:36

33 - 3D rotation sequence example solution.mp4

05:28

34 - Intro to Euler angles rotation about moving body frames.mp4

01:20

35 - Intuition on different conventions.mp4

02:37

36 - Fixed VS Moving body frame rotations 1 exercise.mp4

01:00

37 - Fixed VS Moving body frame rotations 2 solution new exercise.mp4

03:30

38 - Fixed VS Moving body frame rotations 3 solution.mp4

07:42

39 - Rotation matrix conventions Intro.mp4

07:00

40 - Rotation matrix conventions RXYZ matrix product.mp4

09:16

41 - Rotation matrix conventions RZYX matrix product.mp4

06:36

42 - Rotation matrix conventions RXYX matrix product.mp4

04:44

43 - Rotation matrix conventions RXYZ vs RZYX example.mp4

14:22

44 - Rotation matrix conventions RXYZ vs RXYX example.mp4

14:46

45 - Rotation matrix application to the UAV 1.mp4

03:32

46 - Rotation matrix application to the UAV 2.mp4

08:20

47 - Why is a special Transfer matrix needed 1.mp4

15:09

48 - Why is a special Transfer matrix needed 2.mp4

08:05

49 - Why is a special Transfer matrix needed 3.mp4

07:14

50 - Transfer matrix derivation 1 exercise.mp4

07:12

51 - Transfer matrix derivation 2 solution new exercise.mp4

07:59

52 - Mathematical derivation of the Rzyx moving frame rotation matrix.mp4

03:51

53 - Transfer matrix derivation 4 solution.mp4

04:33

54 - Transfer matrix derivation 5.mp4

04:28

55 - Rotation & Transfer matrix application 1 Kinematics wrap up.mp4

05:14

56 - Rotation & Transfer matrix application 2 Kinematics wrap up.mp4

03:02

57 - Intro to Dynamics.mp4

02:38

58 - Dot product 1 Application.mp4

05:08

59 - Dot product 2 Application.mp4

04:04

60 - Dot product 3 Application exercise.mp4

02:47

61 - Dot product 4 Application solution.mp4

03:54

62 - Cross Product 1.mp4

04:06

63 - Cross Product 2 Exercise.mp4

04:53

64 - Cross Product 3 Solution.mp4

03:21

65 - Cross Product Application 1.mp4

07:19

66 - Cross Product Application 2 exercise.mp4

02:25

67 - Cross Product Application 2 Solution.mp4

03:44

68 - Mass moments of inertia & inertia tensor 1.mp4

05:18

69 - Mass moments of inertia & inertia tensor 2 exercise.mp4

04:36

70 - Mass moments of inertia & inertia tensor 3 solution.mp4

08:35

71 - Mathematical formulas of mass moments of inertia.mp4

07:44

72 - Mathematical formulas of products of inertia.mp4

03:09

73 - Principal axis.mp4

03:33

74 - Mass moment of inertia applied to the UAV.html

75 - Dynamics Translational Motion Inertial Frame.mp4

08:48

76 - Dynamics Translational Motion Body Frame 1.mp4

09:11

77 - Dynamics Translational Motion Body Frame 2.mp4

09:14

78 - Dynamics Translational Motion Body Frame 3.mp4

07:31

79 - Angular momentum VS angular velocity 1.mp4

07:39

80 - Angular momentum VS angular velocity 2.mp4

03:30

81 - Dynamics Rotational Motion Inertial frame.mp4

10:40

82 - Dynamics Rotational Motion Body frame 1.mp4

09:02

83 - Dynamics Rotational Motion Body frame 2.mp4

03:28

84 - Autonomous vehicle lateral acceleration through new lenses.mp4

20:03

85 - Dynamics Rotational Motion Body frame alternative form exercise.mp4

03:53

86 - Dynamics Rotational Motion Body frame alternative form solution.mp4

04:46

3 - Specific UAV plant model

87 - From 6 DOF NewtonEuler to statespace exercise.mp4

09:33

88 - From 6 DOF NewtonEuler to statespace solution.mp4

00:58

89 - Applying Force of gravity to the UAV exercise.mp4

11:29

90 - Applying Force of gravity to the UAV solution.mp4

09:02

91 - Applying control inputs to the UAV exercise.mp4

01:25

92 - Gyroscopic effect intuition control inputs solution.mp4

09:55

93 - Gyroscopic effect on a UAV intuition 1 exercise.mp4

04:06

94 - Gyroscopic effect on a UAV intuition 2 solution.mp4

03:24

95 - Gyroscopic effect on a UAV Math 1 exercise.mp4

08:17

96 - Gyroscopic effect on a UAV Math 2 solution.mp4

05:06

97 - Gyroscopic effect on a UAV Math 3.mp4

07:07

98 - Gyroscopic effect on a UAV Math 4.mp4

02:45

99 - From 6 DOF NewtonEuler to statespace Math 1 exercise.mp4

03:23

100 - From 6 DOF NewtonEuler to statespace Math 2 solution.mp4

12:46

101 - UAV plant model schematics 1 exercise.mp4

13:02

102 - UAV plant model schematics 2 solution.mp4

09:04

103 - Euler state integrator.mp4

09:49

104 - Runge Kutta integrator 1.mp4

08:17

105 - Runge Kutta integrator 2.mp4

11:02

106 - Runge Kutta integrator 3.mp4

08:49

107 - Runge Kutta integrator 4.mp4

08:37

108 - Runge Kutta integrator 5.mp4

07:33

109 - Runge Kutta integrator 6.mp4

03:03

110 - Runge Kutta integrator 7.mp4

02:47

111 - Runge Kutta integrator 8.mp4

05:22

112 - From control inputs to rotor angular velocities blade element theory 1.mp4

07:15

113 - From control inputs to rotor angular velocities blade element theory 2.mp4

09:58

114 - From control inputs to rotor angular velocities blade element theory 3.mp4

08:06

115 - From control inputs to rotor angular velocities blade element theory 4.mp4

14:06

116 - From control inputs to rotor angular velocities blade element theory 5.mp4

10:20

117 - From control inputs to rotor angular velocities blade element theory 6.mp4

13:29

118 - From control inputs to rotor angular velocities blade element theory 7.mp4

10:59

119 - From control inputs to rotor angular velocities blade element theory 8.mp4

03:40

120 - From control inputs to rotor angular velocities blade element theory 9.mp4

08:58

121 - From control inputs to rotor angular velocities blade element theory 10.mp4

09:23

122 - From control inputs to rotor angular velocities blade element theory 11.mp4

10:00

123 - From control inputs to rotor angular velocities blade element theory 12.mp4

04:35

124 - From control inputs to rotor angular velocities blade element theory 13.mp4

08:52

4 - Recap of Applied Control Systems 1 autonomous cars Math PID MPC

125 - Detailed recap 1 car & bicycle lateral equations of motion.mp4

03:03

126 - Detailed recap 2 LTI state space equations.mp4

03:06

127 - Detailed recap 3 continuous VS discrete LTI.mp4

03:23

128 - Detailed recap 4 system input calculation using Model Predictive Control.mp4

05:10

5 - The UAVs global control architecture

129 - The global control architecture scheme Intro.mp4

05:53

130 - The elements of the sequentialcascaded controller.mp4

03:17

131 - Different tasks of each subcontroller.mp4

05:23

132 - The Planner.mp4

08:10

133 - Stronger VS weaker dynamics 1.mp4

04:33

134 - Stronger VS weaker dynamics 2.mp4

12:28

135 - Reference trajectory equations in the planner.mp4

13:22

136 - The affect of the control inputs on future states.mp4

10:07

6 - The MPC attitude controller

137 - Review of the global control structure.mp4

03:12

138 - Review of the state space equations of the autonomous vehicle.mp4

08:06

139 - The UAVs dynamics and kinematics equations revisited.mp4

02:13

140 - Zero angle roll and pitch assumption 1.mp4

10:52

141 - Zero angle roll and pitch assumption 2.mp4

06:20

142 - Putting the state space equations in the Linear format 1.mp4

03:31

143 - Putting the state space equations in the Linear format 2.mp4

04:20

144 - Putting the state space equations in the Linear format 3.mp4

04:36

145 - Putting the state space equations in the Linear format 4.mp4

10:16

146 - Linear Parameter Varying form 1.mp4

10:18

147 - Linear Parameter Varying form 2.mp4

04:34

148 - Review of the steps from the equations of motion to the plant.mp4

04:56

149 - The dimensions of the state space equation matrices.mp4

04:30

150 - Future state prediction formula 1 simplified LPVMPC.mp4

04:45

151 - Future state prediction formula 2 simplified LPVMPC.mp4

07:39

152 - Future state prediction formula 3 nonsimplified LPVMPC.mp4

13:22

153 - Future state prediction formula 4 nonsimplified LPVMPC.mp4

10:50

154 - Future state prediction formula 5 nonsimplified LPVMPC.mp4

08:07

155 - Cost function 1.mp4

11:46

156 - Cost function 2.mp4

07:17

157 - Cost function 3.mp4

10:12

158 - Cost function 4.mp4

07:41

159 - Cost function 5.mp4

03:24

160 - Cost function 6.mp4

03:43

161 - Cost function 7.mp4

06:25

162 - Cost function 8.mp4

03:53

163 - Cost function 9.mp4

05:29

164 - Cost function 10.mp4

15:49

165 - Cost function 11.mp4

08:43

7 - Feedback Linearization Controller

166 - Equations of motion for position control inertial frame exercise.mp4

07:22

167 - Equations of motion for position control inertial frame solution.mp4

12:45

168 - General feedback control architecture.mp4

04:05

169 - Feedback Linearization Controller schematics Part 1.mp4

05:51

170 - Differential Equations intro.mp4

08:56

171 - Differential Equations & the control law.mp4

04:52

172 - Solving differential equations real roots 1.mp4

12:15

173 - Solving differential equations real roots 2.mp4

10:07

174 - Solving differential equations real roots 3.mp4

06:27

175 - Solving differential equations complex roots 1.mp4

09:32

176 - Solving differential equations complex roots 2.mp4

09:08

177 - Solving differential equations complex roots 3.mp4

08:53

178 - Solving differential equations complex roots 4.mp4

04:53

179 - Using the exponent for controlling a system exercise.mp4

08:45

180 - Using the exponent for controlling a system solution.mp4

11:16

181 - Poles & Laplace domain.mp4

08:18

182 - From poles to differential equation constants exercise.mp4

05:36

183 - From poles to differential equation constants solution.mp4

05:16

184 - From differential equations to statespace representation.mp4

05:23

185 - Eigenvalues in control engineering & Determinants.mp4

17:28

186 - Computing eigenvectors.mp4

14:51

187 - Laplace VS Fourier frequency domain.mp4

06:42

188 - Moving poles.mp4

12:54

189 - Feedback Linearization Controller schematics Part 2.mp4

03:55

190 - Simulation results with real & complex poles 1.mp4

08:08

191 - Simulation results with real & complex poles 2.mp4

15:47

192 - Simulation results with real & complex poles 3.mp4

15:18

193 - Feedback Linearization Controller schematics Part 3.mp4

16:08

194 - Final Stretch computing the final control inputs Part 1.mp4

11:51

195 - Final Stretch computing the final control inputs Part 2.mp4

14:49

196 - Recommended reading Great article about Kalman Filters.html

8 - The simulation code explanation

197 - Intro to Linux & macOS Terminal & Windows Command Prompt.mp4

12:54

198 - Python installation instructions.mp4

01:02

199 - Python installation instructions Windows 10.mp4

05:46

200 - Python installation instructions Ubuntu.mp4

04:43

201 - Python installation instructions macOS.mp4

08:04

202 - Simulation analysis & code explanation 1.mp4

09:33

203 - Simulation analysis & code explanation 2.mp4

06:42

204 - Simulation analysis & code explanation 3.mp4

11:12

205 - Simulation analysis & code explanation 4.mp4

09:40

206 - Simulation analysis & code explanation 5.mp4

07:18

207 - Simulation analysis & code explanation 6.mp4

10:51

208 - Simulation analysis & code explanation 7.mp4

05:49

209 - Simulation analysis & code explanation 8.mp4

11:10

210 - Simulation analysis & code explanation 9.mp4

07:29

211 - Simulation analysis & code explanation 10.mp4

11:21

212 - Simulation analysis & code explanation 11.mp4

07:53

213 - Simulation analysis & code explanation 12.mp4

18:31

214 - Simulation analysis & code explanation 13.mp4

02:25

215 - Simulation analysis & code explanation 14.mp4

07:45

216 - Simulation analysis & code explanation 15.mp4

16:25

217 - Basic intro to Python animations tools.mp4

12:12

218 - Final-Thesis.pdf

218 - MATLAB-version-All-files.zip

218 - Simulation codes & course summary document.html

218 - main-lpv-mpc-drone.zip

218 - modified-pages.pdf

218 - support-files-drone.zip

9 - Extra MPC constraints applied to the UAV

219 - Recap of MPC constraints in autonomous cars 1.mp4

06:43

220 - Recap of MPC constraints in autonomous cars 2.mp4

05:33

221 - Recap of MPC constraints in autonomous cars 3.mp4

08:19

222 - Recap of MPC constraints in autonomous cars 4.mp4

06:39

223 - Recap of MPC constraints in autonomous cars 5.mp4

08:27

224 - Application of MPC constraints to UAV drone 1.mp4

07:53

225 - Application of MPC constraints to UAV drone 2.mp4

07:10

226 - Application of MPC constraints to UAV drone 3.mp4

05:23

227 - Application of MPC constraints to UAV drone 4.mp4

04:57

228 - Application of MPC constraints to UAV drone 5.mp4

10:05

229 - No solution example autonomous cars 1.mp4

09:20

230 - No solution example autonomous cars 2.mp4

12:55

231 - Installation of solver libraries Intro.mp4

00:44

232 - Installation of solver libraries Windows 10.mp4

04:44

233 - Installation of solver libraries Ubuntu.mp4

02:40

234 - Installation of solver libraries MacOS.mp4

03:23

235 - MATLAB-version-All-files.zip

235 - UAV drone Python files WITH MPC constraints.html

235 - main-lpv-mpc-drone-constraints.zip

235 - support-files-drone-constraints.zip

236 - MPC constraints for UAV drone code explanation 1.mp4

10:25

237 - MPC constraints for UAV drone code explanation 2.mp4

08:25

238 - MPC constraints for UAV drone code explanation 3.mp4

05:15

239 - MPC constraints for UAV drone analysis of simulation results.mp4

11:59

10 - Last Words

240 - Thank You.mp4

00:45

241 - Well done Youve done it But dont stop here Keep going forward.html

Description

Modeling + state space systems + Model Predictive Control + feedback control + Python simulation: UAV quadcopter drone

What You'll Learn?

mathematical modelling of a UAV quadcopter drone
obtaining kinematic equations: Rotation & Transfer matrices
obtaining Newton-Euler 6 DOF dynamic equations of motion with rotating frames
going from equations of motion to a UAV specific state-space equations
understanding the gyroscopic effect & applying it to the UAV model
understanding the Runge-Kutta integrator and applying it to the UAV model
mastering & applying Model Predictive Control algorithm to the UAV
mastering & applying a feedback linearization controller to the UAV
combining Model Predictive Control and feedback linearization in one global controller
simulating the drone's trajectory tracking in Python using the MPC and feedback linearization controller

Who is this for?

Science and Engineering students

Working Scientists and Engineers

Control Engineering enthusiasts

What You Need to Know?

Basic Calculus: Functions, Derivatives, Integrals

Vector-Matrix multiplication

Udemy course: Applied Control Systems 1: autonomous cars (Math + PID + MPC)

More details

Description
One of the greatest transformations that we will see in the next couple of decades is going to be the advent of autonomous drones. While being used extensively already, the applications of quadcopters will only grow in time. Drones will be used in delivery services, entertainment, medicine, military, rescue, structural quality inspection - places that people cannot reach easily, and in many other fields.
In many cases, there will be a predefined trajectory in a 3D space that the UAVÂ needs to follow without human help. In fact, humans might simply give a simple command for the drone to go somewhere, and then, a specific trajectory will be generated by a computer in that direction and the UAV's control algorithms will need to determine EXACTLY how fast each rotor should turn in order to make the drone follow that trajectory with high-degree precision.
And that's what this course is all about - its about DESIGNING, MASTERING, and APPLYINGÂ these control algorithms together with deriving the dynamics equations for the quadcopter.
In this course, you will receive a full package when it comes to learning about how to model and control a UAV drone and make it follow a trajectory in a 3D environment. Not only you will learn how to model a UAV system mathematically by deriving the equations of motion using the principles of 3D Dynamics, but you will also be exposed to some of the most powerful control techniques out there such as Model Predictive Control and feedback linearization.
In 3D dynamics, you will learn the fundamental math and physics behind the UAV quadcopter drone modelling. You will learn how to describe the position and orientation of a UAV quadcopter drone in a 3D space using rotation and transfer matrices, Newton - Euler 6 Degree of Freedom equations of motion, widely used Runge - Kutta integrator in engineering and propeller dynamics.
In the end of the course, I will also explain to you the code in the Python simulator.
Understanding the material in this course fundamentally, being able to quantify it mathematically, and knowing how to apply it using coding - that will give you an advantage in your engineering career that you cannot even imagine yet. It will give you a competitive edge that you need in the labor market.
I'm very excited to start working with you. Take a look at some of my free preview videos, and if you like what you see, then ENROLL in the course, and let's get started right now!
Who this course is for:
Science and Engineering students
Working Scientists and Engineers
Control Engineering enthusiasts

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