Solving the Model Predictive Control Problem with Soft Constraints

This paper will demonstrate how the convexity and quadratic nature of the soft constrained model predictive control problem can be used to solve for its unique minimum in a finite number of steps. A mathematical formulation for this problem will be given that leads to a new convergent minimization algorithm. This algorithm will then be compared to a traditional method of steepest descent type algorithm in an example.