A dual gradient projection quadratic programming algorithm tailored for model predictive control

The objective of this work is to derive a QP algorithm tailored for MPC. More specifically, the primary target application is MPC for discrete-time hybrid systems. A desired property of the algorithm is that warm starts should be possible to perform efficiently. This property is very important for on-line linear MPC, and it is crucial in branch and bound for hybrid MPC. In this paper, a dual active set-like QP method was chosen because of its warm start properties. A drawback with classical active set methods is that they often require many iterations in order to find the active set in optimum. Gradient projection methods are methods known to be able to identify this active set very fast and such a method was therefore chosen in this work. The gradient projection method was applied to the dual QP problem and it was tailored for the MPC application. Results from numerical experiments indicate that the performance of the new algorithm is very good, both for linear MPC as well as for hybrid MPC. It is also noticed that the number of QP iterations is significantly reduced compared to classical active set methods.