Software:PROPT
Developer(s) | Tomlab Optimization Inc. |
---|---|
Stable release | 7.8
/ December 16, 2011 |
Operating system | TOMLAB - OS Support |
Type | Technical computing |
License | Proprietary |
Website | PROPT product page |
The PROPT[1] MATLAB Optimal Control Software is a new generation platform for solving applied optimal control (with ODE or DAE formulation) and parameters estimation problems.
The platform was developed by MATLAB Programming Contest Winner, Per Rutquist in 2008. The most recent version has support for binary and integer variables as well as an automated scaling module.
Description
PROPT is a combined modeling, compilation and solver engine, built upon the TomSym modeling class, for generation of highly complex optimal control problems. PROPT uses a pseudospectral Collocation method (with Gauss or Chebyshev points) for solving optimal control problems. This means that the solution takes the form of a Polynomial, and this polynomial satisfies the DAE and the path constraints at the collocation points.
In general PROPT has the following main functions:
- Computation of the constant matrices used for the differentiation and integration of the polynomials used to approximate the solution to the Trajectory optimization problem.
- Source transformation to turn user-supplied expressions into MATLAB code for the cost function [math]\displaystyle{ f }[/math] and constraint function [math]\displaystyle{ c }[/math] that are passed to a Nonlinear programming solver in TOMLAB. The source transformation package TomSym automatically generates first and second order derivatives.
- Functionality for plotting and computing a variety of information for the solution to the problem.
- Automatic detection of the following:
- Linear and quadratic objective.
- Simple bounds, linear and nonlinear constraints.
- Non-optimized expressions.
- Integrated support for non-smooth[2] (hybrid) optimal control problems.
- Module for automatic scaling of difficult space related problem.
- Support for binary and integer variables, controls or states.
Modeling
The PROPT system uses the TomSym symbolic source transformation engine to model optimal control problems. It is possible to define independent variables, dependent functions, scalars and constant parameters:
toms tf toms t p = tomPhase('p', t, 0, tf, 30); x0 = {tf == 20}; cbox = {10 <= tf <= 40}; toms z1 cbox = {cbox; 0 <= z1 <= 500}; x0 = {x0; z1 == 0}; ki0 = [1e3; 1e7; 10; 1e-3];
States and controls
States and controls only differ in the sense that states need be continuous between phases.
tomStates x1 x0 = {icollocate({x1 == 0})}; tomControls u1 cbox = {-2 <= collocate(u1) <= 1}; x0 = {x0; collocate(u1 == -0.01)};
Boundary, path, event and integral constraints
A variety of boundary, path, event and integral constraints are shown below:
cbnd = initial(x1 == 1); % Starting point for x1 cbnd = final(x1 == 1); % End point for x1 cbnd = final(x2 == 2); % End point for x2 pathc = collocate(x3 >= 0.5); % Path constraint for x3 intc = {integrate(x2) == 1}; % Integral constraint for x2 cbnd = final(x3 >= 0.5); % Final event constraint for x3 cbnd = initial(x1 <= 2.0); % Initial event constraint x1
Single-phase optimal control example
Van der Pol Oscillator [3]
Minimize:
[math]\displaystyle{ \begin{matrix} J_{x,t} & = & x_3(t_f) \\ \end{matrix} }[/math]
Subject to:
[math]\displaystyle{ \begin{cases} \frac{dx_1}{dt} = (1-x_2^2)*x_1-x_2+u \\ \frac{dx_2}{dt} = x_1 \\ \frac{dx_3}{dt} = x_1^2+x_2^2+u^2 \\ x(t_0) = [0 \ 1 \ 0] \\ t_f = 5 \\ -0.3 \le u \le 1.0 \\ \end{cases} }[/math]
To solve the problem with PROPT the following code can be used (with 60 collocation points):
toms t p = tomPhase('p', t, 0, 5, 60); setPhase(p); tomStates x1 x2 x3 tomControls u % Initial guess x0 = {icollocate({x1 == 0; x2 == 1; x3 == 0}) collocate(u == -0.01)}; % Box constraints cbox = {-10 <= icollocate(x1) <= 10 -10 <= icollocate(x2) <= 10 -10 <= icollocate(x3) <= 10 -0.3 <= collocate(u) <= 1}; % Boundary constraints cbnd = initial({x1 == 0; x2 == 1; x3 == 0}); % ODEs and path constraints ceq = collocate({dot(x1) == (1-x2.^2).*x1-x2+u dot(x2) == x1; dot(x3) == x1.^2+x2.^2+u.^2}); % Objective objective = final(x3); % Solve the problem options = struct; options.name = 'Van Der Pol'; solution = ezsolve(objective, {cbox, cbnd, ceq}, x0, options);
Multi-phase optimal control example
One-dimensional rocket [4] with free end time and undetermined phase shift
Minimize:
[math]\displaystyle{ \begin{matrix} J_{x,t} & = & tCut \\ \end{matrix} }[/math]
Subject to:
[math]\displaystyle{ \begin{cases} \frac{dx_1}{dt} = x_2 \\ \frac{dx_2}{dt} = a-g \ (0 \lt t \lt = tCut) \\ \frac{dx_2}{dt} = -g \ (tCut \lt t \lt t_f) \\ x(t_0) = [0 \ 0] \\ g = 1 \\ a = 2 \\ x_1(t_f) = 100 \\ \end{cases} }[/math]
The problem is solved with PROPT by creating two phases and connecting them:
toms t toms tCut tp2 p1 = tomPhase('p1', t, 0, tCut, 20); p2 = tomPhase('p2', t, tCut, tp2, 20); tf = tCut+tp2; x1p1 = tomState(p1,'x1p1'); x2p1 = tomState(p1,'x2p1'); x1p2 = tomState(p2,'x1p2'); x2p2 = tomState(p2,'x2p2'); % Initial guess x0 = {tCut==10 tf==15 icollocate(p1,{x1p1 == 50*tCut/10;x2p1 == 0;}) icollocate(p2,{x1p2 == 50+50*t/100;x2p2 == 0;})}; % Box constraints cbox = { 1 <= tCut <= tf-0.00001 tf <= 100 0 <= icollocate(p1,x1p1) 0 <= icollocate(p1,x2p1) 0 <= icollocate(p2,x1p2) 0 <= icollocate(p2,x2p2)}; % Boundary constraints cbnd = {initial(p1,{x1p1 == 0;x2p1 == 0;}) final(p2,x1p2 == 100)}; % ODEs and path constraints a = 2; g = 1; ceq = {collocate(p1,{ dot(p1,x1p1) == x2p1 dot(p1,x2p1) == a-g}) collocate(p2,{ dot(p2,x1p2) == x2p2 dot(p2,x2p2) == -g})}; % Objective objective = tCut; % Link phase link = {final(p1,x1p1) == initial(p2,x1p2) final(p1,x2p1) == initial(p2,x2p2)}; %% Solve the problem options = struct; options.name = 'One Dim Rocket'; constr = {cbox, cbnd, ceq, link}; solution = ezsolve(objective, constr, x0, options);
Parameter estimation example
Parameter estimation problem [5]
Minimize:
[math]\displaystyle{ \begin{matrix} J_{p} & = & \sum_{i=1,2,3,5}{(x_1(t_i) - x_1^m(t_i))^2} \\ \end{matrix} }[/math]
Subject to:
[math]\displaystyle{ \begin{cases} \frac{dx_1}{dt} = x_2 \\ \frac{dx_2}{dt} = 1-2*x_2-x_1 \\ x_0 = [p_1 \ p_2] \\ t_i = [1 \ 2 \ 3 \ 5] \\ x_1^m(t_i) = [0.264 \ 0.594 \ 0.801 \ 0.959] \\ |p_{1:2}| \lt = 1.5 \\ \end{cases} }[/math]
In the code below the problem is solved with a fine grid (10 collocation points). This solution is subsequently fine-tuned using 40 collocation points:
toms t p1 p2 x1meas = [0.264;0.594;0.801;0.959]; tmeas = [1;2;3;5]; % Box constraints cbox = {-1.5 <= p1 <= 1.5 -1.5 <= p2 <= 1.5}; %% Solve the problem, using a successively larger number collocation points for n=[10 40] p = tomPhase('p', t, 0, 6, n); setPhase(p); tomStates x1 x2 % Initial guess if n == 10 x0 = {p1 == 0; p2 == 0}; else x0 = {p1 == p1opt; p2 == p2opt icollocate({x1 == x1opt; x2 == x2opt})}; end % Boundary constraints cbnd = initial({x1 == p1; x2 == p2}); % ODEs and path constraints x1err = sum((atPoints(tmeas,x1) - x1meas).^2); ceq = collocate({dot(x1) == x2; dot(x2) == 1-2*x2-x1}); % Objective objective = x1err; %% Solve the problem options = struct; options.name = 'Parameter Estimation'; options.solver = 'snopt'; solution = ezsolve(objective, {cbox, cbnd, ceq}, x0, options); % Optimal x, p for starting point x1opt = subs(x1, solution); x2opt = subs(x2, solution); p1opt = subs(p1, solution); p2opt = subs(p2, solution); end
Optimal control problems supported
- Aerodynamic trajectory control[6]
- Bang-bang control[7]
- Chemical engineering[8]
- Dynamic systems[9]
- General optimal control
- Large-scale linear control[10]
- Multi-phase system control[11]
- Mechanical engineering design[12]
- Nondifferentiable control[13]
- Parameters estimation for dynamic systems[14]
- Singular control
References
- ↑ Rutquist, Per; M. M. Edvall (June 2008). PROPT - Matlab Optimal Control Software. Pullman, WA: Tomlab Optimization Inc.. http://tomopt.com/docs/TOMLAB_PROPT.pdf.
- ↑ Banga, J. R.; Balsa-Canto, E.; Moles, C. G.; Alonso, A. A. (2003). Dynamic optimization of bioprocesses: efficient and robust numerical strategies. Journal of Biotechnology.
- ↑ "Van Der Pol Oscillator - Matlab Solution", PROPT Home Page June, 2008.
- ↑ "One Dimensional Rocket Launch (2 Free Time)", PROPT Home Page June, 2008.
- ↑ "Matlab Dynamic Parameter Estimation with PROPT", PROPT Home Page June, 2008.
- ↑ Betts, J. (2007). SOCS Release 6.5.0. THE BOEING COMPANY.
- ↑ Liang, J.; Meng, M.; Chen, Y.; Fullmer, R. (2003). Solving Tough Optimal Control Problems by Network Enabled Optimization Server (NEOS). School of Engineering, Utah State University USA, Chinene University of Hong Kong China.
- ↑ Carrasco, E. F.; Banga, J. R. (September 1998). A HYBRID METHOD FOR THE OPTIMAL CONTROL OF CHEMICAL PROCESSES. University of Wales, Swansea, UK: UKACC International Conference on CONTROL 98.
- ↑ Vassiliadis, V. S.; Banga, J. R.; Balsa-Canto, E. (1999). Second-order sensitivities of general dynamic systems with application to optimal control problems. 54. Chemical Engineering Science. pp. 3851–3860.
- ↑ Luus, R. (2002). Iterative dynamic programming. Chapman and Hall/CRC.
- ↑ Fabien, B. C. (1998). A Java Application for the Solution of Optimal Control Problems. Stevens Way, Box 352600 Seattle, WA 98195, USA: Mechanical Engineering, University of Washington.
- ↑ Jennings, L. S.; Fisher, M. E. (2002). MISER3: Optimal Control Toolbox User Manual, Matlab Beta Version 2.0. Nedlands, WA 6907, Australia: Department of Mathematics, The University of Western Australia.
- ↑ Banga, J. R.; Seider, W. D. (1996). Floudas, C. A.; Pardalos, P. M.. eds. Global Optimization of Chemical Processes using Stochastic Algorithms - State of the Art in Global Optimization: Computational Methods and Applications. Dordrecht, The Netherlands: Kluwer Academic Publishers. pp. 563–583. ISBN 0-7923-3838-3.
- ↑ Dolan, E. D.; More, J. J. (January 2001). Benchmarking Optimization Software with COPS. 9700 South Cass Avenue, Argonne, Illinois 60439: ARGONNE NATIONAL LABORATORY.
External links
- TOMLAB - Developer and distributor of the software.
- TomSym - Source transformation engine used in software.
- PROPT - Home page for PROPT.
- SNOPT - Default solver used in PROPT.
Original source: https://en.wikipedia.org/wiki/PROPT.
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