DOCSLIB.ORG

TACO — a Toolkit for AMPL Control Optimization

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
TACO — a Toolkit for AMPL Control Optimization ARGONNE NATIONAL LABORATORY 9700 South Cass Avenue Argonne, Illinois 60439 TACO — A Toolkit for AMPL Control Optimization Christian Kirches and Sven Leyffer Mathematics and Computer Science Division Preprint ANL/MCS-P1948-0911 November 22, 2011 The research leading to these results has received funding from the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement no FP7-ICT-2009-4 248940. The first author acknowledges a travel grant by Heidelberg Graduate Academy, funded by the German Excellence Initiative. We thank Hans Georg Bock, Johannes P. Schlöder, and Sebastian Sager for permission to use the optimal control software package MUSCOD-II and the mixed-integer optimal control algorithm MS-MINTOC. This work was also supported by the Office of Advanced Scientific Computing Research, Office of Science, U.S. Department of Energy, under Contract DE-AC02-06CH11357, and by the Directorate for Computer and Information Science and Engineering of the National Science Foundation under award NSF-CCF-0830035. Contents 1 Introduction...................................................................1 1.1 The AMPL Modeling Language.....................................................3 1.2 Benefits of AMPL Extensions......................................................3 1.3 Related Efforts..............................................................3 1.4 Contributions and Structure........................................................3 2 AMPL Extensions for Optimal Control.....................................................4 2.1 Modeling Optimal Control Problem Variables..............................................5 2.2 Modeling Optimal Control Problem Constraints.............................................6 2.3 Modeling Optimal Control Problem Objectives.............................................7 2.4 Universal Header File for Optimal Control Problems..........................................7 3 Examples of Optimal Control Models in AMPL................................................8 3.1 Example from the COPS Library of Optimal Control Problems.....................................8 3.2 Mixed-Integer ODE-Constrained Example................................................9 3.3 AMPL Models of Optimal Control Problems in the mintoc.de Collection............................... 10 4 TACO Toolkit for AMPL Control Optimization................................................ 11 4.1 Implementations of the New User Functions............................................... 12 4.2 Consistency Checks............................................................ 13 4.3 User Diagnosis of the Detected Optimal Control Problem Structure................................... 14 4.4 Output of Time–Dependent Solution Variables.............................................. 15 5 Using TACO to Interface AMPL to MUSCOD-II and MS-MINTOC..................................... 16 5.1 Direct Multiple Shooting Method for Optimal Control.......................................... 16 5.2 Sensitivity Computation and Automatic Derivatives........................................... 17 5.3 Additional Information on Derivatives.................................................. 18 5.4 MUSCOD-II Solver Options and AMPL Suffixes............................................ 19 6 Limitations and Possible Further Extensions.................................................. 20 7 Conclusions and Outlook............................................................ 21 A TACO Data Structures Exposed to Optimal Control Problem Solvers..................................... 22 A.1 Data Structures Mapping from AMPL to Optimal Control........................................ 22 A.2 Data Structures Mapping from Optimal Control to AMPL........................................ 23 B TACO Functions Exposed to Optimal Control Problem Solvers........................................ 26 C TACO Toolkit Error Messages......................................................... 28 ii Mathematical Programming Computation manuscript No. (will be inserted by the editor) TACO — A Toolkit for AMPL Control Optimization Christian Kirches · Sven Leyffer November 22, 2011 Abstract We describe a set of extensions to the AMPL modeling language to conveniently model mixed-integer opti- mal control problems for ODE or DAE dynamic processes. These extensions are realized as AMPL user functions and suffixes and do not require intrusive changes to the AMPL language standard or implementation itself. We describe and provide TACO, a Toolkit for AMPL Control Optimization that reads AMPL stub.nl files and detects the structure of the optimal control problem. This toolkit is designed to facilitate the coupling of existing optimal control software packages to AMPL. We discuss requirements, capabilities, and the current implementation. Using the example of the multiple shooting code for optimal control MUSCOD-II, a direct and simultaneous method for DAE-constrained opti- mal control, we demonstrate how the problem information provided by the TACO toolkit is interfaced to the solver. In addition, we show how the MS-MINTOC algorithm for mixed-integer optimal control can be used to efficiently solve mixed-integer optimal control problems modeled in AMPL. We use the AMPL extensions to model three exemplary control problems and we discuss how those extensions affect the representation of optimal control problems. Solutions to these problems are obtained by using MUSCOD-II and MS-MINTOC inside the AMPL environment. A collection of further AMPL control models is provided on the web site mintoc.de. MUSCOD-II and MS-MINTOC are made available on the NEOS Server for Optimization, using the TACO toolkit to enable input of AMPL models. Keywords Mixed-Integer Optimal Control, Differential-Algebraic Equations, Domain Specific Languages, Mathematical Modeling 1 Introduction This paper describes a set of extensions to the AMPL modeling language that extends AMPL’s applicability to mathe- matical optimization problems including dynamic processes. We describe and implement AMPL syntax elements for convenient modeling of ordinary differential equation (ODE) and differential algebraic equation (DAE) constrained optimal control problems. C. Kirches Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Im Neuenheimer Feld 368, 69120 Heidelberg, GERMANY. Ph.: +49 (6221) 54-8895; Fax: +49 (6221) 54-5444; E-mail: [email protected] S. Leyffer Mathematics and Computer Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439, U.S.A. We consider the class (1) of mixed-integer optimal control problems on the time horizon [0;T]. Our goal is to minimize an objective function that includes an integral Lagrange term L on [0;T], a Mayer term E at the end point T of the time horizon, and a point least-squares term with Nlsq possibly nonlinear residuals li on a grid ftig1≤i≤Nlsq of time n n points. All objective terms depend on the trajectory (x(·);z(·)) : [0;T] ! R x × R z of a dynamic process described in n terms of a system of ODEs (1b) or DAEs (1b, 1c). This system is affected by continuous controls u(·) : [0;T] ! R u 1 nWw nw and integer-valued controls w(·) : [0;T] ! Ww from a finite discrete set Ww := fw ;:::;w g ⊂ R , jWwj = nWw < ¥ (1h). Both control profiles are subject to optimization. Moreover, we allow the end time T (1g), the continuous model np 1 nW n parameters p 2 R , and the discrete–valued model parameters r 2 Wr := fr ;:::;r r g ⊂ R r , jWr j = nr < ¥ (1i) to be subject to optimization as well. Control, parameters, and process trajectory may be constrained by inequality path eq in constraints c(·) (1d), equality constraints rc (·) and inequality constraints rc (·) coupled in time (1e), and decoupled eq in equality and inequality constraints ri (·), ri (·), 1 ≤ i ≤ nr (1f), imposed on a grid ftig1≤i≤Nr of time points on time horizon [0;T]. This constraint grid need not coincide with the point least-squares grid. Constraints (1d)–(1f) include initial values and boundary values; mixed state-control constraints; periodicity constraints; and simple bounds on states, controls, and parameters. The optimal control problem is conveniently expressed as follows: Z T minimize L(t;x(t);z(t);u(t);w(t); p;r) dt + E(T;x(T);z(T); p;r) (1a) x(·);u(·);w(·); t=0 p;r;T N + lsq jjl (t ;x(t );z(t );u(t );w(t ); p; )jj2 t 2 [ ;T]; ∑i=1 i i i i i i r 2, i 0 subject tox ˙(t) = f (t;x(t);z(t);u(t);w(t); p;r); t 2 [0;T]; (1b) 0 = g(t;x(t);z(t);u(t);w(t); p;r); t 2 [0;T]; (1c) 0 ≤ c(t;x(t);z(t);u(t);w(t); p;r); t 2 [0;T]; (1d) eq 0 = rc (x(0);z(0);x(T);z(T); p;r); (1e) in 0 ≤ rc (x(0);z(0);x(T);z(T); p;r); eq 0 = ri (x(ti);z(ti); p;r); ti 2 [0;T]; 1 ≤ i ≤ Nr; (1f) in 0 ≤ ri (x(ti);z(ti); p;r); Tmin ≤ T ≤ Tmax; (1g) w(t) 2 Ww; t 2 [0;T]; (1h) r 2 Wr : (1i) Direct and simultaneous methods for tackling the class of optimization problems (1) posed in function spaces are based on the first-discretize-then-optimize approach. A discretization scheme is chosen and applied to objective (1a), dynamics (1b, 1c), and path constraints (1d) of the optimization problem in order to obtain a finite-dimensional counterpart problem accessible to mathematical programming algorithms. This counterpart problem will usually be a high-dimensional and highly structured nonlinear problem (NLP) or mixed-integer nonlinear problem (MINLP). Thus, it readily falls into the domain of applicability of many mathematical optimization software packages already interfaced with AMPL, such as ALGENCAN [5,6], filterSQP
Load more

Recommended publications
  • Propt Product Sheet
    PROPT - The world’s fastest Optimal Control platform for MATLAB. PROPT - ONE OF A KIND, LIGHTNING FAST SOLUTIONS TO YOUR OPTIMAL CONTROL PROBLEMS! NOW WITH WELL OVER 100 TEST CASES! The PROPT software package is intended to When using PROPT, optimally coded analytical solve dynamic optimization problems. Such first and second order derivatives, including problems are usually described by: problem sparsity patterns are automatically generated, thereby making it the first MATLAB • A state-space model of package to be able to fully utilize a system. This can be NLP (and QP) solvers such as: either a set of ordinary KNITRO, CONOPT, SNOPT and differential equations CPLEX. (ODE) or differential PROPT currently uses Gauss or algebraic equations (PAE). Chebyshev-point collocation • Initial and/or final for solving optimal control conditions (sometimes problems. However, the code is also conditions at other written in a more general way, points). allowing for a DAE rather than an ODE formulation. Parameter • A cost functional, i.e. a estimation problems are also scalar value that depends possible to solve. on the state trajectories and the control function. PROPT has three main functions: • Sometimes, additional equations and variables • Computation of the that, for example, relate constant matrices used for the the initial and final differentiation and integration conditions to each other. of the polynomials used to approximate the solution to the trajectory optimization problem. The goal of PROPT is to make it possible to input such problem • Source transformation to turn descriptions as simply as user-supplied expressions into possible, without having to worry optimized MATLAB code for the about the mathematics of the cost function f and constraint actual solver.
    [Show full text]
  • Full Text (Pdf)
    A Toolchain for Solving Dynamic Optimization Problems Using Symbolic and Parallel Computing Evgeny Lazutkin Siegbert Hopfgarten Abebe Geletu Pu Li Group Simulation and Optimal Processes, Institute for Automation and Systems Engineering, Technische Universität Ilmenau, P.O. Box 10 05 65, 98684 Ilmenau, Germany. {evgeny.lazutkin,siegbert.hopfgarten,abebe.geletu,pu.li}@tu-ilmenau.de Abstract shown in Fig. 1. Based on the current process state x(k) obtained through the state observer or measurement, Significant progresses in developing approaches to dy- resp., the optimal control problem is solved in the opti- namic optimization have been made. However, its prac- mizer in each sample time. The resulting optimal control tical implementation poses a difficult task and its real- strategy in the first interval u(k) of the moving horizon time application such as in nonlinear model predictive is then realized through the local control system. There- control (NMPC) remains challenging. A toolchain is de- fore, an essential limitation of applying NMPC is due to veloped in this work to relieve the implementation bur- its long computation time taken to solve the NLP prob- den and, meanwhile, to speed up the computations for lem for each sample time, especially for the control of solving the dynamic optimization problem. To achieve fast systems (Wang and Boyd, 2010). In general, the these targets, symbolic computing is utilized for calcu- computation time should be much less than the sample lating the first and second order sensitivities on the one time of the NMPC scheme (Schäfer et al., 2007). Al- hand and parallel computing is used for separately ac- though powerful methods are available, e.g.
    [Show full text]
  • An Algorithm for Bang–Bang Control of Fixed-Head Hydroplants
    International Journal of Computer Mathematics Vol. 88, No. 9, June 2011, 1949–1959 An algorithm for bang–bang control of fixed-head hydroplants L. Bayón*, J.M. Grau, M.M. Ruiz and P.M. Suárez Department of Mathematics, University of Oviedo, Oviedo, Spain (Received 31 August 2009; revised version received 10 March 2010; second revision received 1 June 2010; accepted 12 June 2010) This paper deals with the optimal control (OC) problem that arise when a hydraulic system with fixed-head hydroplants is considered. In the frame of a deregulated electricity market, the resulting Hamiltonian for such OC problems is linear in the control variable and results in an optimal singular/bang–bang control policy. To avoid difficulties associated with the computation of optimal singular/bang–bang controls, an efficient and simple optimization algorithm is proposed. The computational technique is illustrated on one example. Keywords: optimal control; singular/bang–bang problems; hydroplants 2000 AMS Subject Classification: 49J30 1. Introduction The computation of optimal singular/bang–bang controls is of particular interest to researchers because of the difficulty in obtaining the optimal solution. Several engineering control problems, Downloaded By: [Bayón, L.] At: 08:38 25 May 2011 such as the chemical reactor start-up or hydrothermal optimization problems, are known to have optimal singular/bang–bang controls. This paper deals with the optimal control (OC) problem that arises when addressing the new short-term problems that are faced by a generation company in a deregulated electricity market. Our model of the spot market explicitly represents the price of electricity as a known exogenous variable and we consider a system with fixed-head hydroplants.
    [Show full text]
  • Basic Implementation of Multiple-Interval Pseudospectral Methods to Solve Optimal Control Problems
    Basic Implementation of Multiple-Interval Pseudospectral Methods to Solve Optimal Control Problems Technical Report UIUC-ESDL-2015-01 Daniel R. Herber∗ Engineering System Design Lab University of Illinois at Urbana-Champaign June 4, 2015 Abstract A short discussion of optimal control methods is presented including in- direct, direct shooting, and direct transcription methods. Next the basics of multiple-interval pseudospectral methods are given independent of the nu- merical scheme to highlight the fundamentals. The two numerical schemes discussed are the Legendre pseudospectral method with LGL nodes and the Chebyshev pseudospectral method with CGL nodes. A brief comparison be- tween time-marching direct transcription methods and pseudospectral direct transcription is presented. The canonical Bryson-Denham state-constrained double integrator optimal control problem is used as a test optimal control problem. The results from the case study demonstrate the effect of user's choice in mesh parameters and little difference between the two numerical pseudospectral schemes. ∗Ph.D pre-candidate in Systems and Entrepreneurial Engineering, Department of In- dustrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, [email protected] c 2015 Daniel R. Herber 1 Contents 1 Optimal Control and Direct Transcription 3 2 Basics of Pseudospectral Methods 4 2.1 Foundation . 4 2.2 Multiple Intervals . 7 2.3 Legendre Pseudospectral Method with LGL Nodes . 9 2.4 Chebyshev Pseudospectral Method with CGL Nodes . 10 2.5 Brief Comparison to Time-Marching Direct Transcription Methods 11 3 Numeric Case Study 14 3.1 Test Problem Description . 14 3.2 Implementation and Analysis Details . 14 3.3 Summary of Case Study Results .
    [Show full text]
  • Nature-Inspired Metaheuristic Algorithms for Finding Efficient
    Nature-Inspired Metaheuristic Algorithms for Finding Efficient Experimental Designs Weng Kee Wong Department of Biostatistics Fielding School of Public Health October 19, 2012 DAE 2012 Department of Statistics University of Georgia October 17-20th 2012 Weng Kee Wong (Department of Biostatistics [email protected] School of Public Health ) October19,2012 1/50 Two Upcoming Statistics Conferences at UCLA Western Northern American Region (WNAR 2013) June 16-19 2013 http://www.wnar.org Weng Kee Wong (Dept. of Biostatistics [email protected] of Public Health ) October19,2012 2/50 Two Upcoming Statistics Conferences at UCLA Western Northern American Region (WNAR 2013) June 16-19 2013 http://www.wnar.org The 2013 Spring Research Conference (SRC) on Statistics in Industry and Technology June 20-22 2013 http://www.stat.ucla.edu/ hqxu/src2013 Weng Kee Wong (Dept. of Biostatistics [email protected] of Public Health ) October19,2012 2/50 Two Upcoming Statistics Conferences at UCLA Western Northern American Region (WNAR 2013) June 16-19 2013 http://www.wnar.org The 2013 Spring Research Conference (SRC) on Statistics in Industry and Technology June 20-22 2013 http://www.stat.ucla.edu/ hqxu/src2013 Beverly Hills, Brentwood, Bel Air, Westwood, Wilshire Corridor Weng Kee Wong (Dept. of Biostatistics [email protected] of Public Health ) October19,2012 2/50 Two Upcoming Statistics Conferences at UCLA Western Northern American Region (WNAR 2013) June 16-19 2013 http://www.wnar.org The 2013 Spring Research Conference (SRC) on Statistics in Industry and Technology June 20-22 2013 http://www.stat.ucla.edu/ hqxu/src2013 Beverly Hills, Brentwood, Bel Air, Westwood, Wilshire Corridor Close to the Pacific Ocean Weng Kee Wong (Dept.
    [Show full text]
  • OPTIMAL CONTROL of NONHOLONOMIC MECHANICAL SYSTEMS By
    OPTIMAL CONTROL OF NONHOLONOMIC MECHANICAL SYSTEMS by Stuart Marcus Rogers A thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Applied Mathematics Department of Mathematical and Statistical Sciences University of Alberta ⃝c Stuart Marcus Rogers, 2017 Abstract This thesis investigates the optimal control of two nonholonomic mechanical systems, Suslov's problem and the rolling ball. Suslov's problem is a nonholonomic variation of the classical rotating free rigid body problem, in which the body angular velocity Ω(t) must always be orthogonal to a prescribed, time-varying body frame vector ξ(t), i.e. hΩ(t); ξ(t)i = 0. The motion of the rigid body in Suslov's problem is actuated via ξ(t), while the motion of the rolling ball is actuated via internal point masses that move along rails fixed within the ball. First, by applying Lagrange-d'Alembert's principle with Euler-Poincar´e'smethod, the uncontrolled equations of motion are derived. Then, by applying Pontryagin's minimum principle, the controlled equations of motion are derived, a solution of which obeys the uncontrolled equations of motion, satisfies prescribed initial and final conditions, and minimizes a prescribed performance index. Finally, the controlled equations of motion are solved numerically by a continuation method, starting from an initial solution obtained analytically (in the case of Suslov's problem) or via a direct method (in the case of the rolling ball). ii Preface This thesis contains material that has appeared in a pair of papers, one on Suslov's problem and the other on rolling ball robots, co-authored with my supervisor, Vakhtang Putkaradze.
    [Show full text]
  • Real-Time Trajectory Planning to Enable Safe and Performant Automated Vehicles Operating in Unknown Dynamic Environments
    Real-time Trajectory Planning to Enable Safe and Performant Automated Vehicles Operating in Unknown Dynamic Environments by Huckleberry Febbo A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Mechanical Engineering) at The University of Michigan 2019 Doctoral Committee: Associate Research Scientist Tulga Ersal, Co-Chair Professor Jeffrey L. Stein, Co-Chair Professor Brent Gillespie Professor Ilya Kolmanovsky Huckleberry Febbo [email protected] ORCID iD: 0000-0002-0268-3672 c Huckleberry Febbo 2019 All Rights Reserved For my mother and father ii ACKNOWLEDGEMENTS I want to thank my advisors, Prof. Jeffrey L. Stein and Dr. Tulga Ersal for their strong guidance during my graduate studies. Each of you has played pivotal and complementary roles in my life that have greatly enhanced my ability to communicate. I want to thank Prof. Peter Ifju for telling me to go to graduate school. Your confidence in me has pushed me farther than I knew I could go. I want to thank Prof. Elizabeth Hildinger for improving my ability to write well and providing me with the potential to assert myself on paper. I want to thank my friends, family, classmates, and colleges for their continual support. In particular, I would like to thank John Guittar and Srdjan Cvjeticanin for providing me feedback on my articulation of research questions and ideas. I will always look back upon the 2018 Summer that we spent together in the Rackham Reading Room with fondness. Rackham . oh Rackham . I want to thank Virgil Febbo for helping me through my last month in Michigan.
    [Show full text]
  • Mad Product Sheet
    MAD - MATLAB Automatic Differential package. MATLAB Automatic Differentiation (MAD) is a professionally maintained and developed automatic differentiation tool for MATLAB. Many new features are added continuously since the development and additions are made in close cooperation with the user base. Automatic differentiation is generally defined as (after Andreas Griewank, Evaluating Derivatives - Principles and Techniques of Algorithmic Differentiation, SIAM 2000): “Algorithmic, or automatic, differentiation (AD) is concerned with the accurate and efficient evaluation of derivatives for functions defined by computer programs. No truncation errors are incurred, and the resulting numerical derivative values can be used for all scientific computations that are based on linear, quadratic, or even higher order approximations to nonlinear scalar or vector functions.” MAD is a MATLAB library of functions and utilities for the automatic differentiation of MATLAB functions and statements. MAD utilizes an optimized class library “derivvec”, for the linear combination of derivative vectors. Key Features MAD Example A variety of trigonometric functions. >> x = fmad(3,1); FFT functions available (FFT, IFFT). >> y = x^2 + x^3; >> getderivs(y) interp1 and interp2 optimized for differentiation. ans = 33 Delivers floating point precision derivatives (better robustness). A new MATLAB class fmad which overloads the builtin MATLAB arithmetic and some intrinsic functions. More than 100 built-in MATLAB operators have been implemented. The classes allow for the use of MATLAB’s sparse matrix representation to exploit sparsity in the derivative calculation at runtime. Normally faster or equally fast as numerical differentiation. Complete integration with all TOMLAB solvers. Possible to use as plug-in for modeling class tomSym and optimal control package PROPT.
    [Show full text]
  • CME 338 Large-Scale Numerical Optimization Notes 2
    Stanford University, ICME CME 338 Large-Scale Numerical Optimization Instructor: Michael Saunders Spring 2019 Notes 2: Overview of Optimization Software 1 Optimization problems We study optimization problems involving linear and nonlinear constraints: NP minimize φ(x) n x2R 0 x 1 subject to ` ≤ @ Ax A ≤ u; c(x) where φ(x) is a linear or nonlinear objective function, A is a sparse matrix, c(x) is a vector of nonlinear constraint functions ci(x), and ` and u are vectors of lower and upper bounds. We assume the functions φ(x) and ci(x) are smooth: they are continuous and have continuous first derivatives (gradients). Sometimes gradients are not available (or too expensive) and we use finite difference approximations. Sometimes we need second derivatives. We study algorithms that find a local optimum for problem NP. Some examples follow. If there are many local optima, the starting point is important. x LP Linear Programming min cTx subject to ` ≤ ≤ u Ax MINOS, SNOPT, SQOPT LSSOL, QPOPT, NPSOL (dense) CPLEX, Gurobi, LOQO, HOPDM, MOSEK, XPRESS CLP, lp solve, SoPlex (open source solvers [7, 34, 57]) x QP Quadratic Programming min cTx + 1 xTHx subject to ` ≤ ≤ u 2 Ax MINOS, SQOPT, SNOPT, QPBLUR LSSOL (H = BTB, least squares), QPOPT (H indefinite) CLP, CPLEX, Gurobi, LANCELOT, LOQO, MOSEK BC Bound Constraints min φ(x) subject to ` ≤ x ≤ u MINOS, SNOPT LANCELOT, L-BFGS-B x LC Linear Constraints min φ(x) subject to ` ≤ ≤ u Ax MINOS, SNOPT, NPSOL 0 x 1 NC Nonlinear Constraints min φ(x) subject to ` ≤ @ Ax A ≤ u MINOS, SNOPT, NPSOL c(x) CONOPT, LANCELOT Filter, KNITRO, LOQO (second derivatives) IPOPT (open source solver [30]) Algorithms for finding local optima are used to construct algorithms for more complex optimization problems: stochastic, nonsmooth, global, mixed integer.
    [Show full text]
  • Tomlab Product Sheet
    TOMLAB® - For fast and robust large- scale optimization in MATLAB® The TOMLAB Optimization Environment is a powerful optimization and modeling package for solving applied optimization problems in MATLAB. TOMLAB provides a wide range of features, tools and services for your solution process: • A uniform approach to solving optimization problem. • A modeling class, tomSym, for lightning fast source transformation. • Automatic gateway routines for format mapping to different solver types. • Over 100 different algorithms for linear, discrete, global and nonlinear optimization. • A large number of fully integrated Fortran and C solvers. • Full integration with the MAD toolbox for automatic differentiation. • 6 different methods for numerical differentiation. • Unique features, like costly global black-box optimization and semi-definite programming with bilinear inequalities. • Demo licenses with no solver limitations. • Call compatibility with MathWorks’ Optimization Toolbox. • Very extensive example problem sets, more than 700 models. • Advanced support by our team of developers in Sweden and the USA. • TOMLAB is available for all MATLAB R2007b+ users. • Continuous solver upgrades and customized implementations. • Embedded solvers and stand-alone applications. • Easy installation for all supported platforms, Windows (32/64-bit) and Linux/OS X 64-bit For more information, see http://tomopt.com or e-mail [email protected]. Modeling Environment: http://tomsym.com. Dedicated Optimal Control Page: http://tomdyn.com. Tomlab Optimization Inc. Tomlab
    [Show full text]
  • Optimal Control of Isometric Muscle Dynamics
    12 J. Math.Fund.Sci. Vol. 47, No. 1, 2015, 12-30 Optimal Control of Isometric Muscle Dynamics Robert Rockenfeller &Thomas Götz Mathematical Institute, University Koblenz-Landau, Campus Koblenz Universitätsstr. 1, 56070 Koblenz, Germany Email: [email protected] Abstract. We use an indirect optimal control approach to calculate the optimal neural stimulation needed to obtain measured isometric muscle forces. The neural stimulation of the nerve system is hereby considered to be a control function (input) of the system ’muscle’ that solely determines the muscle force (output). We use a well-established muscle model and experimental data of isometric contractions. The model consists of coupled activation and contraction dynamics described by ordinary differential equations. To validate our results, we perform a comparison with commercial optimal control software. Keywords: biomechanics; inverse dynamics; muscle model; optimal control, stimulation. 1 Introduction0B Mathematical models for everyday phenomena often ask for a control or input such that a system reacts in an optimal or at least in a desired way. Whether finding the optimal rotation of a stick for cooking potatoes on the open fire such that the potato has a desired temperature, see [1], or computing the optimal neural stimulation of a muscle such that the force output is as close as possible to experimentally measured data. Typical examples for biomechanical optimal control problems occur in the calculation of goal directed movements, see [2- 4] and in robotics [5]. Concerning huge musculoskeletal systems, the load sharing problem of muscle force distribution has to be solved using optimal control [6, 7]. The most common application for solving the load sharing problem is the inverse dynamics of multi-body systems (MBS) as in [8,9].
    [Show full text]
  • PROPT - Matlab Optimal Control Software
    PROPT - Matlab Optimal Control Software - ONE OF A KIND, LIGHTNING FAST SOLUTIONS TO YOUR OPTIMAL CONTROL PROBLEMS! Per E. Rutquist1 and Marcus M. Edvall2 April 26, 2010 1Tomlab Optimization AB, V¨aster˚asTechnology Park, Trefasgatan 4, SE-721 30 V¨aster˚as,Sweden. 2Tomlab Optimization Inc., 1260 SE Bishop Blvd Ste E, Pullman, WA, USA. 1 Contents Contents 2 1 PROPT Guide Overview 22 1.1 Installation................................................. 22 1.2 Foreword to the software.......................................... 22 1.3 Initial remarks............................................... 22 2 Introduction to PROPT 24 2.1 Overview of PROPT syntax........................................ 24 2.2 Vector representation........................................... 25 2.3 Global optimality............................................. 25 3 Modeling optimal control problems 27 3.1 A simple example.............................................. 27 3.2 Code generation.............................................. 28 3.3 Modeling.................................................. 29 3.3.1 Modeling notes........................................... 31 3.4 Independent variables, scalars and constants............................... 33 3.5 State and control variables......................................... 33 3.6 Boundary, path, event and integral constraints............................. 34 4 Multi-phase optimal control 34 5 Scaling of optimal control problems 34 6 Setting solver and options 35 7 Solving optimal control problems 36 7.1 Standard functions
    [Show full text]