Next: sysprop, Previous: blockdiag, Up: Control Theory
Solve the Algebraic Riccati Equation
a' * x + x * a - x * b * x + c = 0Inputs for identically dimensioned square matrices
- a
- n by n matrix;
- b
- n by n matrix or n by m matrix; in the latter case b is replaced by b:=b*b';
- c
- n by n matrix or p by m matrix; in the latter case c is replaced by c:=c'*c;
- opt
- (optional argument; default =
"B"
): String option passed tobalance
prior to ordered Schur decomposition.Output
- x
- solution of the ARE.
Method Laub's Schur method (IEEE Transactions on Automatic Control, 1979) is applied to the appropriate Hamiltonian matrix.
See also: balance, dare.
Return the solution, x of the discrete-time algebraic Riccati equation
a' x a - x + a' x b (r + b' x b)^(-1) b' x a + q = 0Inputs
- a
- n by n matrix;
- b
- n by m matrix;
- q
- n by n matrix, symmetric positive semidefinite, or a p by n matrix, In the latter case q:=q'*q is used;
- r
- m by m, symmetric positive definite (invertible);
- opt
- (optional argument; default =
"B"
): String option passed tobalance
prior to ordered QZ decomposition.Output
- x
- solution of DARE.
Method Generalized eigenvalue approach (Van Dooren; SIAM J. Sci. Stat. Comput., Vol 2) applied to the appropriate symplectic pencil.
See also: Ran and Rodman, Stable Hermitian Solutions of Discrete Algebraic Riccati Equations, Mathematics of Control, Signals and Systems, Vol 5, no 2 (1992), pp 165–194.
See also: balance, are.
Solve the differential Riccati equation
-d P/dt = A'P + P A - P B inv(R) B' P + Q P(tf) = Qffor the LTI system sys. Solution of standard LTI state feedback optimization
min int(t0, tf) ( x' Q x + u' R u ) dt + x(tf)' Qf x(tf)optimal input is
u = - inv(R) B' P(t) xInputs
Outputs
- sys
- continuous time system data structure
- q
- state integral penalty
- r
- input integral penalty
- qf
- state terminal penalty
- t0
- tf
- limits on the integral
- ptol
- tolerance (used to select time samples; see below); default = 0.1
- maxits
- number of refinement iterations (default=10)
tvals is selected so that:
- tvals
- time values at which p(t) is computed
- plist
- list values of p(t); plist { i } is p(tvals(i))
|| Plist{i} - Plist{i-1} || < Ptolfor every i between 2 and length(tvals).
Return controllability gramian of discrete time system
x(k+1) = a x(k) + b u(k)Inputs
- a
- n by n matrix
- b
- n by m matrix
Output
- m
- n by n matrix, satisfies
a m a' - m + b*b' = 0
Solve the discrete-time Lyapunov equation
Inputs
- a
- n by n matrix;
- b
- Matrix: n by n, n by m, or p by n.
Output
- x
- matrix satisfying appropriate discrete time Lyapunov equation.
Options:
- b is square: solve
a x a' - x + b = 0
- b is not square: x satisfies either
a x a' - x + b b' = 0or
a' x a - x + b' b = 0,whichever is appropriate.
Method Uses Schur decomposition method as in Kitagawa, An Algorithm for Solving the Matrix Equation X = F X F' + S, International Journal of Control, Volume 25, Number 5, pages 745–753 (1977).
Column-by-column solution method as suggested in Hammarling, Numerical Solution of the Stable, Non-Negative Definite Lyapunov Equation, IMA Journal of Numerical Analysis, Volume 2, pages 303–323 (1982).
Return controllability gramian m of the continuous time system dx/dt = a x + b u.
m satisfies a m + m a' + b b' = 0.
Solve the Lyapunov (or Sylvester) equation via the Bartels-Stewart algorithm (Communications of the ACM, 1972).
If a, b, and c are specified, then
lyap
returns the solution of the Sylvester equationa x + x b + c = 0If only
(a, b)
are specified, then lyap returns the solution of the Lyapunov equationa' x + x a + b = 0If b is not square, then
lyap
returns the solution of eithera' x + x a + b' b = 0or
a x + x a' + b b' = 0whichever is appropriate.
Solves by using the Bartels-Stewart algorithm (1972).
Compute generalized eigenvalues of the matrix pencil
(A - lambda B).a and b must be real matrices.
qzval
is obsolete; useqz
instead.
Compute product of zgep incidence matrix F with vector x. Used by zgepbal (in zgscal) as part of generalized conjugate gradient iteration.
Construct right hand side vector zz for the zero-computation generalized eigenvalue problem balancing procedure. Called by zgepbal.
Implementation of procedure REDUCE in (Emami-Naeini and Van Dooren, Automatica, # 1982).
Return nonz = number of rows of mat whose two norm exceeds meps, and zer = number of rows of mat whose two norm is less than meps.
Generalized conjugate gradient iteration to solve zero-computation generalized eigenvalue problem balancing equation fx=z; called by zgepbal.
Apply givens rotation c,s to row vectors a, b. No longer used in zero-balancing (__zgpbal__); kept for backward compatibility.
Apply householder vector based on e^(m) to column vector y. Called by zgfslv.
References