Reduced Memory Footprint in Multiparametric Quadratic Programming by Exploiting Low Rank Structure

In multiparametric programming an optimization problem which is dependent on a parameter vector is solved parametrically. In control, multiparametric quadratic programming (mp-QP) problems have become increasingly important since the optimization problem arising in Model Predictive Control (MPC) can be cast as an mp-QP problem, which is referred to as explicit MPC. One of the main limitations with mp-QP and explicit MPC is the amount of memory required to store the parametric solution and the critical regions. In this paper, a method for exploiting low rank structure in the parametric solution of an mp-QP problem in order to reduce the required memory is introduced. The method is based on ideas similar to what is done to exploit low rank modifications in generic QP solvers, but is here applied to mp-QP problems to save memory. The proposed method has been evaluated experimentally, and for some examples of relevant problems the relative memory reduction is an order of magnitude compared to storing the full parametric solution and critical regions

Fast Non-Parametric Learning to Accelerate Mixed-Integer Programming for Online Hybrid Model Predictive Control

Today's fast linear algebra and numerical optimization tools have pushed the frontier of model predictive control (MPC) forward, to the efficient control of highly nonlinear and hybrid systems. The field of hybrid MPC has demonstrated that exact optimal control law can be computed, e.g., by mixed-integer programming (MIP) under piecewise-affine (PWA) system models. Despite the elegant theory, online solving hybrid MPC is still out of reach for many applications. We aim to speed up MIP by combining geometric insights from hybrid MPC, a simple-yet-effective learning algorithm, and MIP warm start techniques. Following a line of work in approximate explicit MPC, the proposed learning-control algorithm, LNMS, gains computational advantage over MIP at little cost and is straightforward for practitioners to implement

High temperature electrolyzer/fuel cell power cycle: Preliminary design considerations

A model of a high temperature electrolyzer/fuel cell, hydrogen/oxygen, thermally regenerative power cycle is developed and used to simulate system performance for varying system parameters. Initial estimates of system efficiency, weight, and volume are provided for a one KWe module assuming specific electrolyzer and fuel cell characteristics, both current and future. Specific interest is placed on examining the system responses to changes in device voltage versus current density operating curves, and the associated optimum operating ranges. The performance of a solar-powered, space based system in low earth orbit is examined in terms of the light-dark periods requiring storage. The storage design tradeoffs between thermal energy, electrical energy, and hydrogen/oxygen mass storage are examined. The current technology module is based on the 1000 C solid oxide electrolyzer cell and the alkaline fuel cell. The Future Technology system examines benefits involved with developing a 1800K electrolyzer operating with an advanced fuel cell