PID Controllers - Intro to Control Design

Explore the elevator control problem with a physics refresher, applying Newton's second law and a 20 Hz controller to move between floors using sensors, actuators, and counterweights.

Explore simulator physics by adjusting gravity, friction, and elevator and counterweight masses, and learn how controller outputs are scaled by output gain and power electronics to achieve precise set points.

Explore a free step-by-step Python installation guide for Mac and Windows, including navigating directories with terminal or Anaconda prompt and downloading assignments from the elevator GitHub repository.

Create and activate a Conda environment for this course to ensure matching Python and package versions, verify installation, and manage environments (activate, deactivate, remove) to guarantee consistent simulator results.

Set up the simulator and Python environment, install numpy and matplotlib, clone the repository, and run a basic physics test to validate the elevator model before designing your first controller.

Design your first controller in this pid controllers course module, using a pi setup and a 15 set point; explore why a logic controller fails due to sensor input x.

Explore how a logic controller evolves toward a fuzzy-logic approach by smoothing the set point, adding a zero-output buffer, and analyzing elevator behavior to diagnose design flaws.

Design an acceleration-based controller for elevator position by converting acceleration to velocity with a linear controller, then applying fuzzy logic to reach the setpoint.

Formalizes closed loop control by contrasting open loop control, defining the controller, plant, input, output, reference, and error, and explaining positive and negative feedback using shower and elevator examples.

Learn how proportional control computes the output as Kp times the error in a closed-loop system. See how tuning and friction shape elevator behavior and system response.

Learn how derivative control slows approaching motion by using the error derivative in a PD controller, tuning kP and kD to decelerate before reaching the setpoint.

Explore how assignment 2 solutions adjust Kp and gains for an elevator control problem, using maximum output to manage acceleration, saturation, and friction while reducing steady-state error.

Apply integral control to overcome steady-state errors in a PD elevator model by accumulating past errors, managing wind-up with a capped integral, and testing varying masses to reach the setpoint.

Walks through assignment three of the pid controller, showing a max integrator of 5 and output cap of 2.5, and explains integrator wind-up and performance under different masses.

Explore advanced controllers beyond pid, including model predictive control with a physics model and cost function, and nonlinear control; study p, i, d choices and windup and constraints.