11. Examples¶

11.1. How to
- 11.1.1. Basic Example
- 11.1.2. How to Incorporate Preview Information in the MPC Problem
- 11.1.3. HOW TO: Implement an MPC Controller with a Time-Varying Dynamics
- 11.1.4. How to Implement 1-Norm and Infinity-Norm Cost Functions
- 11.1.5. HOW TO: Implement Rate Constraints
- 11.1.6. Binary MPC Example
  - 11.1.6.1. Simulation result
  - 11.1.6.2. Details on problem reformulation
11.2. Y2F interface: Basic example
- 11.2.1. Defining the problem data
- 11.2.2. Defining the MPC problem
- 11.2.3. Generating a solver
- 11.2.4. Calling the generated solver
- 11.2.5. Simulation
- 11.2.6. Results
- 11.2.7. Variation 1: Parametric cost
- 11.2.8. Variation 2: Time-varying dynamics
- 11.2.9. Variation 3: Time-varying constraints
11.3. Y2F interface: Trajectory Optimization for Quadrotor Flight
- 11.3.1. Defining the problem parameters
- 11.3.2. Defining the MPC problem
- 11.3.3. Generating a solver
- 11.3.4. Calling the generated solver
- 11.3.5. Results
11.4. Low-level interface: Active Suspension Control
- 11.4.1. Introduction
- 11.4.2. Disturbance Model: Speed Bump
- 11.4.3. Implementation of Preview Information
- 11.4.4. Comparison of Passive Vehicle and Active Suspension Control Using Preview Information
11.5. Low-level interface: Robust estimation (Kalman filter)
- 11.5.1. System Description
- 11.5.2. Robust Kalman filter
- 11.5.3. Simulation and Comparison
11.6. Low-level interface: Spacecraft Rendezvous
- 11.6.1. Introduction
- 11.6.2. Model
- 11.6.3. Constraints
- 11.6.4. Objective
- 11.6.5. Spacecraft Rendezvous Maneuvers with and without 1-Norm Cost
11.7. Low-level interface: DC/DC converter
- 11.7.1. Example Overview
- 11.7.2. Special Requirements
- 11.7.3. Introduction - Control of a DC/DC Converter
- 11.7.4. Control Objective by Using Model Predictive Control
- 11.7.5. Model Predictive Control Design via FORCESPRO MATLAB® Interface
- 11.7.6. Simulation of the PLECS® Model with Model Predictive Control
- 11.7.7. Comparison of Model Predictive Control and PI Control
11.8. High-level interface: Basic example
- 11.8.1. Defining the problem data
- 11.8.2. Defining the MPC problem
- 11.8.3. Generating a solver
- 11.8.4. Calling the generated solver
- 11.8.5. Simulation
- 11.8.6. Results
11.9. High-level interface: Obstacle avoidance (MATLAB & Python)
- 11.9.1. Defining the MPC Problem
- 11.9.2. Generating a solver
- 11.9.3. Calling the generated solver
- 11.9.4. Results
- 11.9.5. Variation: External functions
11.10. High-level interface: Indoor localization (MATLAB & Python)
- 11.10.1. Time of flight measurements
- 11.10.2. Estimation error
- 11.10.3. Minimize the error
- 11.10.4. Implementation
11.11. High-level interface: Path tracking example (MATLAB)
- 11.11.1. Defining the MPC Problem
- 11.11.2. Generating a solver
- 11.11.3. Calling the generated solver
  - 11.11.3.1. Choosing the Path
- 11.11.4. Results
- 11.11.5. Using the SQP Fast solver
11.12. High-level interface: Legacy path tracking example (MATLAB & Python)
- 11.12.1. Defining the MPC Problem
- 11.12.2. Generating a solver
- 11.12.3. Calling the generated solver
- 11.12.4. Results
11.13. High-level interface: Rate Constraints
- 11.13.1. Implementation in MATLAB
- 11.13.2. Results
11.14. High-level interface: Soft Constraints
- 11.14.1. Implementation in MATLAB
- 11.14.2. Results
11.15. Controlling a crane using a FORCESPRO NLP solver
- 11.15.1. Defining the MPC problem
- 11.15.2. Generating a FORCESPRO interior point NLP solver
- 11.15.3. Calling the crane solver
- 11.15.4. Results
11.16. Real-time SQP Solver: Robotic Arm Manipulator (MATLAB & Python)
- 11.16.1. Defining the MPC problem
- 11.16.2. Generating a real-time SQP solver
- 11.16.3. Calling the generated SQP solver
- 11.16.4. Calling the dynamics used in the model directly in MATLAB/Python
- 11.16.5. Results
11.17. Controlling a DC motor using a FORCESPRO SQP solver
- 11.17.1. Defining the MPC problem
11.18. Mixed-integer nonlinear solver: F8 Crusader aircraft
- 11.18.1. Defining the problem data
- 11.18.2. Defining the MPC problem
- 11.18.3. Generating an MINLP solver
- 11.18.4. Calling the generated MINLP solver
- 11.18.5. Providing an initial guess at run-time
- 11.18.6. Changing the parallelization strategy at run-time
- 11.18.7. Results
11.19. High-level interface: Optimal EV charging and speed profile example (MATLAB & PYTHON)
- 11.19.1. Problem Overview
- 11.19.2. Defining the MPC Problem
11.20. High-level interface: Extended optimal EV charging and speed profile example using a 2D motor efficiency map (MATLAB & PYTHON)
- 11.20.1. Problem Overview
- 11.20.2. Defining the MPC Problem