Autoplay
Autocomplete
Previous Lesson
Complete and Continue
autonomous vehicle - control
Proportional & PID Controller
guide (2:50)
Intro to Control - how to control systems with a controller 1 (6:52)
Intro to Control - how to control systems with a controller 2 (6:34)
Open VS Closed Loop System (6:36)
Controlling the water tank in a Python simulation (2:52)
Intro to a proportional controller (4:44)
Modelling the water tank 1 (1:45)
Modelling the water tank 2 (12:13)
Numerical integration applied to the water tank model (9:52)
Combining math with the control structure (7:07)
Water tank simulation - proportional controller (2:28)
Intro to a PID simulation (2:26)
PID: Modelling the train with forces 1 (6:27)
PID: Modelling the train with forces 2 (9:36)
PID: Going from system input to system output using numerical integration (10:00)
PID: Magnetic train simulation - proportional controller (1:59)
PID: Proportional controller overshoot explanation 1 (4:39)
PID: Proportional controller overshoot explanation 2 (6:28)
PID: Proportional controller overshoot explanation 3 (3:40)
PID: Intro to Derivative Control (10:24)
PID: Tuning the controller (6:11)
PID: Proportional & Derivative controller & magnetic train simulation in Python (9:01)
PID: Intro to Integral Control (4:35)
PID: Python magnetic train simulation at an inclination angle (1:49)
PID: Mathematical modelling of the train with the inclination angle 1 (3:43)
PID: Mathematical modelling of the train with the inclination angle 2 (5:39)
PID: Proportional, Derivative, Integral Control combined (15:15)
PID: Magnetic train simulation (inclination angle & PID) (2:26)
Python installation instructions - Ubuntu (6:45)
Python installation instructions - Windows 10 (6:34)
PID train code explanation 1 (17:56)
PID train code explanation 2 (11:15)
PID train code explanation 3 (11:18)
Basic intro to Python animation tools (12:24)
Quick code & animation explanation (water tanks) (28:30)
Codes_PID
Fundamentals of forces, moments, mass moment of inertia and reference frames
PID VS Model Predictive Control (MPC) 1 (2:42)
Intro to MPC (1:10)
Getting started with modelling a car (5:06)
Fundamentals of forces and moments 1 (12:10)
Fundamentals of forces and moments 2 (9:58)
Setting stage for the car's lateral control 1 (5:09)
Setting stage for the car's lateral control 2 (8:50)
PID VS Model Predictive Control (MPC) 2 (1:15)
Setting stage for the car's lateral control 3 (8:25)
Setting stage for the car's lateral control 4 (1:27)
Moment Calculation exercise
Vehicle modelling for lateral control using equations of motion
The general control structure for the vehicle's lateral control (2:31)
Car model VS simplified bicycle model 1 (4:59)
Car model VS simplified bicycle model 2 (1:54)
Car model VS simplified bicycle model 3 (3:35)
Ackerman Steering (1:52)
Longitudinal & lateral velocities of the bicycle model 1 (5:49)
Longitudinal & lateral velocities of the bicycle model 2 (4:15)
Equations of motion in the lateral direction (3:40)
Lateral & centripetal acceleration (7:05)
Centripetal acceleration intuition & mathematical derivation 1 (7:16)
Centripetal acceleration intuition & mathematical derivation 2 (8:58)
Extra explanation on rotating frames
Centripetal acceleration intuition & mathematical derivation 3 (20:51)
Modelling the front wheel of the vehicle 1 (4:41)
Rewriting lateral forces in terms of front wheel angles (4:21)
Modelling the front wheel of the vehicle 2 (2:38)
Modelling the front wheel of the vehicle 3 (10:34)
Modelling the front wheel of the vehicle 4 (10:47)
Vehicle's state-space & Linear Time Invariant (LTI) model for lateral control
From equations of motion to state-space equations 1 (1:36)
From equations of motion to state-space equations 2 (8:09)
From equations of motion to state-space equations 3 (5:58)
From equations of motion to state-space equations 4 (3:39)
The meaning of states 1 (5:21)
The meaning of states 2 (4:59)
Adding extra states to the system (9:15)
Computing new states in the open loop system 1 (12:16)
Computing new states in the open loop system 2 (9:58)
Computing new states in the open loop system 3 (5:46)
Simplifying systems with small angle assumptions (8:54)
Nonlinear VS Linear Time Invariant (LTI) models (11:47)
Connecting LTI matrices with the vehicle's inputs (6:30)
Getting LTI model using small angle approximation 1 (4:36)
Getting LTI model using small angle approximation 2 (9:18)
Getting LTI model using small angle approximation 3 + Recap (8:19)
Model Predictive Control - Intuition - Rocket example
Model Predictive Control - Intro (8:17)
Model Predictive Control - Thrust levels (6:56)
Model Predictive Control - Cost function (13:48)
Model Predictive Control - Cost function having several variables 1 (14:07)
Model Predictive Control - Cost function having several variables 2 (4:28)
Model Predictive Control - Cost function weights (6:56)
Model Predictive Control - Horizon period (10:52)
Model Predictive Control - measured VS predicted outputs (Kalman Filter) (9:28)
Model Predictive Control - Quadratic VS other cost functions 1 (6:37)
Model Predictive Control - Quadratic VS other cost functions 2 (8:28)
Model Predictive Control - Quadratic VS other cost functions 3 (6:10)
Model Predictive Control - Quadratic VS other cost functions 4 (8:21)
Model Predictive Control - Mathematical Derivation - autonomous vehicle example
Model Predictive Control - Math - 1 (6:47)
Model Predictive Control - Math - 2 (11:37)
Model Predictive Control - Math - 2 (Extra)
Model Predictive Control - Math - 3 (14:12)
Model Predictive Control - Math - 4 (19:50)
Model Predictive Control - Math - 5 (12:26)
Model Predictive Control - Math - 6 (8:25)
Model Predictive Control - Math - 7 (8:29)
Model Predictive Control - Math - 8 (10:26)
MPC - extra intuition (10:04)
Model Predictive Control - Math - 9 (2:37)
Model Predictive Control - Math - 10 (9:09)
Model Predictive Control - Math - 11 (16:18)
Model Predictive Control - Math - 12 (5:20)
Model Predictive Control - Math - 13 (14:29)
Model Predictive Control - Math - 14 (3:58)
Model Predictive Control - Math - 15 (8:15)
Model Predictive Control - Math - 16 (7:38)
Model Predictive Control - Math - 17 (0:55)
Model Predictive Control - Math - 18 (6:40)
Model Predictive Control - Math - 19 (8:21)
Model Predictive Control - Math - 20 (6:51)
Model Predictive Control - Math - 21 (9:49)
Derivation of the gradient of a quadratic vector-matrix form 1 (9:31)
Derivation of the gradient of a quadratic vector-matrix form 2 (5:06)
Derivation of the gradient of a quadratic vector-matrix form 3 (6:23)
Derivation of the gradient of a quadratic vector-matrix form 4 (9:46)
Derivation of the gradient of a quadratic vector-matrix form 5 (11:44)
Model Predictive Control - Python Simulation - autonomous vehicle
Python Simulation Intro (1:02)
Python installation instructions - Ubuntu (6:45)
Python installation instructions - Windows 10 (6:34)
Intro to the simulator (9:17)
Recap of the course (6:15)
Code explanation 1 - general overview (9:49)
Code explanation 2 - a function for storing the initial variables (14:01)
Code explanation 3 - a function for generating trajectories (18:31)
Code explanation 4 - a function for discrete state space matrices (6:01)
Code explanation 5 - a function for generating the MPC cost function matrices (16:23)
Code explanation 6 - a function for calculating new states (16:44)
Code explanation 7 - the MAIN file 1 (15:47)
Code explanation 8 - the MAIN file 2 (10:55)
Code explanation 9 - the MAIN file 3 (11:37)
Code explanation 10 - the MAIN file 4 (3:44)
Code explanation 11 - Basic intro into Python animations & Plotting (19:00)
Discussing the simulation results (10:57)
Aligning yourself with a fixed reference line SMOOTHLY (0:20)
SOLUTION: Aligning yourself with a fixed reference line SMOOTHLY (0:29)
PID VS Model Predictive Control (MPC) 3 (9:22)
The summary material
Codes_MPC
PID: Mathematical modelling of the train with the inclination angle 1
Lesson content locked
If you're already enrolled,
you'll need to login
.
Enroll in Course to Unlock