Example notebooks
Introductory examples
If you haven't already, check out the tutorials, which explain how to build and simulate models in Collimator.
Primitive blocks and composability
Shows how to build systems with primitive blocks and how to compose them into larger diagrams.
Bouncing ball
Shows hybrid dynamics modeling of a bouncing ball.
Linear Quadratic Regulator (LQR)
Demonstrates the LQR for a pendulum and a planar quadrotor model.
Energy shaping and LQR stabilization
Demonstrates energy shaping control to swing a pendulum to the vertically 'up' orientation and then stabilize it in the 'up' orientation via LQR.
Linear Model Predictive Control (MPC)
Demonstrates MPC on a linearized model of the Cessna Citation aircraft and a pendulum model.
Multi-layer perceptron (MLP)
Demonstrates training of a multi-layer perceptron (MLP), a class of feedforward artificial neural networks, for a regression task.
Interacting with the Dashboard
Demonstrates how to interact with Collimator's Dashboard to upload your local models, import Dashboard models and run simulations on the cloud.
Advanced examples
Trajectory optimization and stabilization
Shows trajectory optimization for the problem of swinging an Acrobot to the vertically 'up' orientation and then stabilizing the trajectory via finite-horizon LQR.
Automatic tuning of a PID controller
Demonstrates automatic differentiation and optimization capabilities of Collimator to automatically tune the gains of a discrete-time PID controller.
Finding limit cycles
Demonstrates how to find limit cycles and assess their stability by leveraging the automatic differentiation capabilities of Collimator.
Kalman Filters: linear and nonlinear extensions
Demonstrates the use of Kalman filters (finite and infinite-horizon) and nonlinear extensions (Extended Kalman Filter and Unscented Kalman Filter) for state estimation in a pendulum model. Where necessary, the nonlinear Pendulum plant is automatically linearized and discretized by Collimator for the construction of the filters.
Universal Differential Equations (UDEs) and symbolic regression (SR)
Demonstrates training a Universal Differential Equation (UDE) to fit the observations produced by the Lotka-Volterra predator-prey system. Subsequently, the UDE is symbolically regressed to learn a closed-form model.
Nonlinear MPC
See thematic series on modeling and control of 3D quadcopter below, which showcases trajectory tracking by nonlinear MPC.
Submodels
Demonstrates how to download a submodel defined in the Dashboard, incorporate it in a local model to run an optimization workflow and upload the result to the Dashboard.
Thematic examples
Battery modeling
- Equivalent circuit model (ECM) for a battery
- ECM parameter estimation: synthetic data
- ECM parameter estimation: experimental data
- Data-driven modeling: Dynamic Mode Decomposition (DMD)
- Data-driven modeling: Extended DMD
- Data-driven modeling: SINDy with control
- Data-driven modeling: Neural Networks