@incollection{eprints2328,
           title = {Fixed-point implementation of a proximal Newton method for embedded model predictive control (I)},
            year = {2014},
           month = {August},
           pages = {2921--2926},
       booktitle = {Proceedings of the 19th IFAC World Congress},
            note = {19th IFAC World Congress, August 24-29, 2014, Cape Town, South Africa},
       publisher = {IFAC},
          author = {Alberto Guiggiani and Panagiotis Patrinos and Alberto Bemporad},
        keywords = {Identification and control methods; Cyber-Physical Systems},
             url = {http://eprints.imtlucca.it/2328/},
        abstract = {Extending the success of model predictive control (MPC) technologies in embedded applications heavily depends on the capability of improving quadratic programming (QP) solvers. Improvements can be done in two directions: better algorithms that reduce the number of arithmetic operations required to compute a solution, and more efficient architectures in terms of speed, power consumption, memory occupancy and cost. This paper proposes a fixed point implementation of a proximal Newton method to solve optimization problems arising in input-constrained MPC. The main advantages of the algorithm are its fast asymptotic convergence rate and its relatively low computational cost per iteration since it the solution of a small linear system is required. A detailed analysis on the effects of quantization errors is presented, showing the robustness of the algorithm with respect to finite-precision computations. A hardware implementation with specific optimizations to minimize computation times and memory footprint is also described, demonstrating the viability of low-cost, low-power controllers for high-bandwidth MPC applications. The algorithm is shown to be very effective for embedded MPC applications through a number of simulation experiments. }
}