settings_convInterContr_car
algorithm settings for the car benchmark
Contents
Syntax
Opts = settings_convInterContr_car()
Description
Algorithm settings and parameter for the Convex Interpolation Control Algorithm for the car benchmark
Output Arguments
Opts
|
a structure containing following options
.controller
|
use exact convex interpolation control law or a
linear or quadratic approximation
[{'linear'} / 'quadratic' / 'exact']
|
.N
|
number of time-steps
[{10} / positive integer]
|
.Ninter
|
number of intermediate time steps during one
time step
[{4} / positive integer]
|
.reachSteps
|
number of reachability steps in one time step
[{20} / positive integer]
|
.Q
|
state weighting matrix for the cost function of
the optimal control problem
[{eye(nx)} / positive-definite square matrix]
|
.R
|
input weighting matrix for the cost function of
the optimal control problem
[{zeros(nu)} / positive-definite square matrix]
|
.parallel
|
use parallel computing
[{false} / true]
|
.approx.method
|
method used to determine the
approximated control law
[{'scaled'} / 'optimized' / 'center']
|
.approx.lambda
|
tradeoff betwen vertex inputs and
difference from the exact control law
[{0.5} / value between 0 and 1]
|
.polyZono.N
|
number of reference trajectory time
steps after which the polynomial
zontope is over-approximated with a
parallelotope
[{Opts.N/2} / positive integer]
|
.polyZono.orderDep
|
maximum zonotope order for the
dependent part of the polynomial
zonotope (for function restructure)
[{10} / positive integer]
|
.polyZono.order
|
maximum zonotope order for the
overall polynomial zonotope (for;
function restructure)
[{20} / positive integer]
|
.extHorizon.active
|
use extended optimization horizon for
optimal control problems
[{false} / true]
|
.extHorizon.horizon
|
length of the extended optimization
horizon in reference trajectory time
steps
[{'all'} / positive integer]
|
.extHorizon.decay
|
decay function for the objective
function of the optimization problem
with extended optimization horizon
[{'fall+End'} / 'uniform' / 'fall' /;
'fallLinear' / 'fallLinear+End' /;
'fallEqDiff' / 'fallEqDiff+End' /;
'rise' / 'quad' / 'riseLinear' /;
'riseEqDiff' / 'end']
|
.refTraj.Q
|
state weighting matrix for the cost function of
optimal control problem (dimension:[nx,nx])
|
.refTraj.R
|
input weighting matrix for the cost function of
optimal control problem (dimension:[nu,nu])
|
.refTraj.x
|
user provided reference trajectory
(dimension: [nx,Opts.N + 1])
|
-.refTraj.u inputs for the user provided reference
trajectory (dimension: [nu,Opts.N])
.cora.alg
|
reachability algorithm that is used
('lin' or 'poly')
|
.cora.tensorOrder
|
taylor order for the abstraction of
the nonlinear function (2 or 3)
|
.cora.taylorTerms
|
taylor order for computing e^At
[{20} / positive integer]
|
.cora.zonotopeOrder
|
upper bound for the zonotope order
[{100} / positive integer]
|
.cora.errorOrder
|
upper bound for the zonotope order
before comp. the abstraction error
[{5} / positive integer]
|
.cora.intermediateOrder
|
upper bound for the zonotope order
during internal computations
[{50} / positive integer]%
|
|
See Also
convexInterpolationControl, car
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