Approximately solve constant-matrix, upper bound µ-synthesis problem
[QOPT,BND] = cmsclsyn(R,U,V,BlockStructure); [QOPT,BND] = cmsclsyn(R,U,V,BlockStructure,opt); [QOPT,BND] = cmsclsyn(R,U,V,BlockStructure,opt,qinit); [QOPT,BND] = cmsclsyn(R,U,V,BlockStructure,opt,'random',N)
for given matrices R ∊ Cnxm, U ∊ Cnxr, V ∊ Ctxm, and a set Δ ⊂ Cmxn. This applies to constant matrix data in R, U, and V.
[QOPT,BND] = cmsclsyn(R,U,V,BlockStructure) minimizes, by choice of Q. QOPT is the optimum value of Q, the upper bound of mussv(R+U*Q*V,BLK), BND. The matrices R,U and V are constant matrices of the appropriate dimension. BlockStructure is a matrix specifying the perturbation blockstructure as defined for mussv.
[QOPT,BND] = cmsclsyn(R,U,V,BlockStructure,OPT) uses the options specified by OPT in the calls to mussv. See mussv for more information. The default value for OPT is 'cUsw'.
[QOPT,BND] = cmsclsyn(R,U,V,BlockStructure,OPT,QINIT) initializes the iterative computation from Q = QINIT. Because of the nonconvexity of the overall problem, different starting points often yield different final answers. If QINIT is an N-D array, then the iterative computation is performed multiple times - the i'th optimization is initialized at Q = QINIT(:,:,i). The output arguments are associated with the best solution obtained in this brute force approach.
[QOPT,BND] = cmsclsyn(R,U,V,BlockStructure,OPT,'random',N) initializes the iterative computation from N random instances of QINIT. If NCU is the number of columns of U, and NRV is the number of rows of V, then the approximation to solving the constant matrix µ synthesis problem is two-fold: only the upper bound for µ is minimized, and the minimization is not convex, hence the optimum is generally not found. If U is full column rank, or V is full row rank, then the problem can (and is) cast as a convex problem, [Packard, Zhou, Pandey and Becker], and the global optimizer (for the upper bound for µ) is calculated.
The cmsclsyn algorithm is iterative, alternatively holding Q fixed, and computing the mussv upper bound, followed by holding the upper bound multipliers fixed, and minimizing the bound implied by choice of Q. If U or V is square and invertible, then the optimization is reformulated (exactly) as an linear matrix inequality, and solved directly, without resorting to the iteration.
Packard, A.K., K. Zhou, P. Pandey, and G. Becker, "A collection of robust control problems leading to LMI's," 30th IEEE Conference on Decision and Control, Brighton, UK, 1991, p. 1245–1250.