linSysMPC

Model Predictive Control for linear systems

Syntax
Description
Input Arguments
Output Arguments
See Also
References

Syntax

res = linSysMPC(benchmark,Param,Opts)

Description

Implementation of the Model Predictive Control algorithm for linear sampled-data systems described in [1]

Input Arguments

benchmark

name of the considered benchmark model (see; "aroc/benchmarks/dynamics/...")

Param

a structure containing the benchmark parameters

.x0

initial state

.xf

goal state

.U

set of admissible control inputs (class:; interval)

.W

set of uncertain disturbances (class: interval; or zonotope)

.V

set of measurement errors (class: interval or; zonotope)

.X

set of state constraints (class: mptPolytope)

Opts

a structure containing the algorithm settings

.tOpt

final time for the optimization

.N

number of time steps [{10} / positive integer]

.termReg

terminal region around the steady state xf (class: mptPolytope or terminalRegion)

.Q

state weighting matrix for the cost function of optimal control problem (reference trajectory)

.R

input weighting matrix for the cost function of optimal control problem (reference trajectory)

.K

feedback matrix for the feedback controller

.Qlqr

state weighting matrix for the cost function of the LQR approach (feedback controller)

.Rlqr

input weighting matrix for the cost function of the LQR approach (feedback controller)

.realTime

flag specifying if real time computation time constraints are considered (Opts.realTime = 1) or not (Opts.realTime = 0) [{true} / boolean]

.alpha

contraction rate for the contraction constraint [{0.1} / alpha > 0]

.cora.taylorTerms

taylor order for computing e^At [{10} / positive integer]

.cora.zonotopeOrder

upper bound for the zonotope order [{50} / positive integer]

Output Arguments

res

results object storing the computed reachable set and the center trajectory

References

*[1] F. Gruber and M. Althoff, “Scalable robust model predictive control for linear sampled-data systems," CDC 2019