model predictive control

Sample Average Approximation and Model Predictive Control for Multistage Stochastic Optimization

  • Dominic S. T. Keehan
  • Andrew B. Philpott
  • Edward Anderson
    • Categories Robust Optimization, Stochastic Programming Tags , , ,

    Sample average approximation-based stochastic dynamic programming and model predictive control are two different methods of approaching multistage stochastic optimization. Model predictive control—despite a lack of theoretical backing—is often used instead of stochastic dynamic programming due to computational necessity. For settings where the stage reward is a convex function of the random terms, the stage dynamics … Read more

    Optimization and Simulation for the Daily Operation of Renewable Energy Communities

  • Nathalie Frieß
  • Ulrich Pferschy
  • Joachim Schauer
  • Elias Feiner
  • Thomas Strametz
    • Categories Applications - Science and Engineering, Energy Tags , , ,

    Renewable Energy Communities (RECs) are an important building block for the decarbonization of the energy sector. The concept of RECs allows individual consumers to join together in local communities to generate, store, consume and sell renewable energy. A major benefit of this collective approach is a better match between supply and demand profiles, and thus, … Read more

    Sample average approximation and model predictive control for inventory optimization

  • Dominic S. T. Keehan
  • Andrew B. Philpott
  • Edward Anderson
    • Categories Dynamic Programming, Stochastic Programming Tags , , ,

    We study multistage stochastic optimization problems using sample average approximation (SAA) and model predictive control (MPC) as solution approaches. MPC is frequently employed when the size of the problem renders stochastic dynamic programming intractable, but it is unclear how this choice affects out-of-sample performance. To compare SAA and MPC out-of-sample, we formulate and solve an … Read more

    Experimental operation of a solar-driven climate system with thermal energy storages using mixed-integer nonlinear MPC

  • Adrian Bürger
  • Daniel Bull
  • Parantapa Sawant
  • Markus Bohlayer
  • Angelika Altmann-Dieses
  • Marco Braun
  • Moritz Diehl
    • Categories (Mixed) Integer Nonlinear Programming, Control Applications, Systems governed by Differential Equations Optimization Tags , , , ,

    This work presents the results of experimental operation of a solar-driven climate system using mixed-integer nonlinear Model Predictive Control (MPC). The system is installed in a university building and consists of two solar thermal collector fields, an adsorption cooling machine with different operation modes, a stratified hot water storage with multiple inlets and outlets as … Read more

    Design, Implementation and Simulation of an MPC algorithm for Switched Nonlinear Systems under Combinatorial Constraints

  • Angelika Altmann-Dieses
  • Moritz Diehl
  • Sebastian Sager
  • Adrian Bürger
  • Clemens Zeile
    • Categories (Mixed) Integer Nonlinear Programming, Systems governed by Differential Equations Optimization Tags , , , ,

    Within this work, we present a warm-started algorithm for Model Predictive Control (MPC) of switched nonlinear systems under combinatorial constraints based on Combinatorial Integral Approximation (CIA). To facilitate high-speed solutions, we introduce a preprocessing step for complexity reduction of CIA problems, and include this approach within a new toolbox for solution of CIA problems with … Read more

    Efficient Convex Optimization for Linear MPC

  • Stephen Wright
    • Categories Control Applications, Quadratic Programming Tags ,

    Model predictive control (MPC) formulations with linear dynamics and quadratic objectives can be solved efficiently by using a primal-dual interior-point framework, with complexity proportional to the length of the horizon. An alternative, which is more able to exploit the similarity of the problems that are solved at each decision point of linear MPC, is to … Read more

    A Condensing Algorithm for Nonlinear MPC with a Quadratic Runtime in Horizon Length

  • Joel A E Andersson
  • Moritz Diehl
  • Janick V Frasch
  • Milan Vukov
    • Categories Optimization of Simulated Systems, Quadratic Programming Tags , , , ,

    A large number of practical algorithms for Optimal Control Problems (OCP) relies on a so-called condensing procedure to exploit the given structure in the quadratic programming (QP) subproblems. While the established structure-exploiting condensing algorithm is of cubic complexity in the horizon length, in this technical note we propose a novel algorithm that is only of … Read more

    SQP Methods for Parametric Nonlinear Optimization

  • Moritz Diehl
  • Vyacheslav Kungurtsev
    • Categories Nonlinear Optimization Tags , , , , , , ,

    Sequential quadratic programming (SQP) methods are known to be effi- cient for solving a series of related nonlinear optimization problems because of desirable hot and warm start properties–a solution for one problem is a good estimate of the solution of the next. However, standard SQP solvers contain elements to enforce global convergence that can interfere … Read more

    A Parallel Quadratic Programming Method for Dynamic Optimization Problems

  • Moritz Diehl
  • Sebastian Sager
  • Janick V Frasch
    • Categories Control Applications, Quadratic Programming Tags , , ,

    Quadratic programming problems (QPs) that arise from dynamic optimization problems typically exhibit a very particular structure. We address the ubiquitous case where these QPs are strictly convex and propose a dual Newton strategy that exploits the block-bandedness similarly to an interior-point method. Still, the proposed method features warmstarting capabilities of active-set methods. We give details … Read more

    Distributed Optimization With Local Domain: Applications in MPC and Network Flows

  • Pedro Aguiar
  • João Mota
  • Markus Püschel
  • João Xavier
    • Categories Control Applications, Network Optimization Tags , , , , ,

    In this paper we consider a network with P nodes, where each node has exclusive access to a local cost function. Our contribution is a communication-efficient distributed algorithm that finds a vector x* minimizing the sum of all the functions. We make the additional assumption that the functions have intersecting local domains, i.e., each function … Read more