mpc-horizon-tuning

To tune MPC prediction horizon and cost matrices for tension control, you provide desired tension and velocity references to select the optimal horizon N, configure state cost Q and control cost R matrices, and apply an LQR terminal cost for stability.

To balance tracking speed and actuator effort in MPC web handling, configure the state cost Q and control cost R matrices to prioritize tension tracking versus actuator effort, allowing you to analyze trade-offs and minimize steady-state error.

Terminal cost ensures stability in MPC systems for robotic arms by using an LQR solution to define the terminal penalty, which guarantees closed-loop stability when predicting tension control dynamics over the selected prediction horizon.

You can use this MPC tuning approach for robotic systems requiring precise web handling, as it optimizes the prediction horizon and cost matrices specifically for tension control tasks like a robotic arm maintaining exact tension references.

Selecting the right prediction horizon matters for MPC tension control because it balances system performance against computational cost, directly impacting the controller's ability to track velocity and tension references accurately without excessive actuator effort.