示例#1
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def is_row_proper(A):
    """
    Test for row properness
    hdr must be full rank! wolovich page 27
    Returns True if A is row proper False otherwise
    
    Example:  TODO
    """
    """
    import matrix_coefficients as mc
    s=symbols('s')
    """
    return mc.highest_row_degree_matrix(A,s).rank() ==min(A.rows,A.cols) # vardu sel 5
示例#2
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def row_proper(A):
    """
    Reduction of a polynomial matrix to row proper polynomial matrix

    Implementation of the algorithm as shown in page 7 of 
    Vardulakis, A.I.G. (Antonis I.G). Linear Multivariable Control: Algebraic Analysis and Synthesis Methods. 
    Chichester,New York,Brisbane,Toronto,Singapore: John Wiley & Sons, 1991.

    INPUT:
        Polynomial Matrix A
    OUTPUT:
        T:An equivalent matrix in row proper form
        TL the unimodular transformation matrix
    Christos Tsolakis
    
    Example:  TODO
    T=Matrix([[1, s**2, 0], [0, s, 1]])
    is_row_proper(T)
    
    see also Wolovich Linear Multivariable Systems Springer
    """
    T=A.copy()
    Transfomation_Matrix=eye(T.rows)     #initialize transformation matrix
    while  not is_row_proper(T):
        #for i in range(3):
        Thr=mc.highest_row_degree_matrix(T,s)
        #pprint(Thr)
        x=symbols('x0:%d'%(Thr.rows+1))
        Thr=Thr.transpose()
        Thr0=Thr.col_insert(Thr.cols,zeros(Thr.rows,1))
        SOL=solve_linear_system(Thr0,*x)      # solve the system 
        a=Matrix(x)
        D={var:1 for var in x if var not in SOL.keys()}   # put free variables equal to 1
        D.update({var:SOL[var].subs(D) for var in x if var in SOL.keys()})
        a=a.subs(D)
        r0=mc.find_degree(T,s) 
        row_degrees=mc.row_degrees(T,s)                               
        i0=row_degrees.index(max(row_degrees))
        ast=Matrix(1,T.rows,[a[i]*s**(r0-row_degrees[i]) for i in range(T.rows)])
        #pprint (ast)
        TL=eye(T.rows)
        TL[i0*TL.cols]=ast
        #pprint (TL)
        T=TL*T
        T.simplify()
        Transfomation_Matrix=TL*Transfomation_Matrix
        Transfomation_Matrix.simplify()
        #pprint (T)
        #print('----------------------------')
            
    return  Transfomation_Matrix,T
示例#3
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def ALGO4(As,Bs,Cs,Ds,do_test):
    
    
    
#-------------------STEP 1------------------------------
    if not is_row_proper(As):
        Us,Ast=row_proper(As)
        
    else:
        Us,Ast=eye(As.rows),As
    
    Bst=Us*Bs
    Bst=expand(Bst)
    r=Ast.cols
    #-------------------STEP 2------------------------------
    K=simplify(Ast.inv()*Bst)  #very important 
    Ys=zeros(K.shape)
    for i,j in  product(range(K.rows),range(K.cols)):
        Ys[i,j],q=div(numer(K[i,j]),denom(K[i,j]))       
    
    B_hat=Bst-Ast*Ys
    
    #-------------------END STEP 2------------------------------
    #-------------------STEP 3------------------------------
    Psi=diag(*[[s**( mc.row_degrees(Ast,s)[j]  -i -1) for i in range( mc.row_degrees(Ast,s)[j])] for j in range(r)]).T
    S=diag(*[s**(rho) for rho in mc.row_degrees(Ast,s)])
    Ahr=mc.highest_row_degree_matrix(Ast,s)
    Help=Ast-S*Ahr
    
    SOL={}
    numvar=Psi.rows*Psi.cols
    alr=symbols('a0:%d'%numvar)
    Alr=Matrix(Psi.cols,Psi.rows,alr)
    RHS=Psi*Alr
    for i,j in  product(range(Help.rows),range(Help.cols)):                 #diagonal explain later
        SOL.update(solve_undetermined_coeffs(Eq(Help[i,j],RHS[i,j]),alr,s))
    
    Alr=Alr.subs(SOL)    #substitute(SOL)
    
    
    Aoc=Matrix(BlockDiagMatrix(*[Matrix(rho, rho, lambda i,j: KroneckerDelta(i+1,j))for rho in mc.row_degrees(Ast,s)]))
    Boc=eye(sum(mc.row_degrees(Ast,s)))
    Coc=Matrix(BlockDiagMatrix(*[SparseMatrix(1,rho,{(x,0):1 if x==0 else 0 for x in range(rho)}) for rho in mc.row_degrees(Ast,s)]))

    A0=Aoc-Alr*Ahr.inv()*Matrix(Coc)
    C0=Ahr.inv()*Coc



    SOL={}
    numvar=Psi.cols*Bst.cols
    b0=symbols('b0:%d'%numvar)
    B0=Matrix(Psi.cols,Bst.cols,b0)
    RHS=Psi*B0

    for i,j in  product(range(B_hat.rows),range(B_hat.cols)):                 #diagonal explain later
        SOL.update(solve_undetermined_coeffs(Eq(B_hat[i,j],RHS[i,j]),b0,s))
    B0=B0.subs(SOL)    #substitute(SOL)

    LHS_matrix=simplify(Cs*C0)                                        #left hand side of the equation (1)

    sI_A=s*eye(A0.cols)- A0
    max_degree=mc.find_degree(LHS_matrix,s)                   #get the degree of the matrix at the LHS
                                                  #which is also the maximum degree for the coefficients of Λ(s)
    #---------------------------Creating Matrices Λ(s) and C -------------------------------------

    Lamda=[]
    numvar=((max_degree))*A0.cols
    a=symbols('a0:%d'%numvar)
    for i in range(A0.cols):                                        # paratirisi den douleuei to prin giat;i otra oxi diagonios
        p=sum(a[n +i*(max_degree)]*s**n for n in range(max_degree))  # we want variables one degree lower because we are multiplying by first order monomials
        Lamda.append(p)
        
    Lamda=Matrix(Cs.rows,A0.cols,Lamda)                                #convert the list to Matrix
    
    c=symbols('c0:%d'%(Lamda.rows*Lamda.cols))
    C=Matrix(Lamda.rows,Lamda.cols,c)
    #-----------------------------------------
    
    RHS_matrix=Lamda*sI_A +C                            #right hand side of the equation (1)
     
    '''
    -----------Converting equation (1) to a system of linear -----------
    -----------equations, comparing the coefficients of the  -----------
    -----------polynomials in both sides of the equation (1) -----------
    '''
     EQ=[Eq(LHS_matrix[i,j],expand(RHS_matrix[i,j])) for i,j in product(range(LHS_matrix.rows),range(LHS_matrix.cols)) ]