Question 18: Suppose A is a \(4 \times 4\) matrix and B is a \(4 \times 2\) matrix, and let \({{\mathop{\rm u}\nolimits} _0},...,{{\mathop{\rm u}\nolimits} _3}\) represent a sequence of input vectors in \({\mathbb{R}^2}\).

  1. Set \({{\mathop{\rm x}\nolimits} _0} = 0\), compute \({{\mathop{\rm x}\nolimits} _1},...,{{\mathop{\rm x}\nolimits} _4}\) from equation (1), and write a formula for \({{\mathop{\rm x}\nolimits} _4}\) involving the controllability matrix \(M\) appearing in equation (2). (Note: The matrix \(M\) is constructed as a partitioned matrix. Its overall size here is \(4 \times 8\).)
  2. Suppose \(\left( {A,B} \right)\) is controllable and v is any vector in \({\mathbb{R}^4}\). Explain why there exists a control sequence \({{\mathop{\rm u}\nolimits} _0},...,{{\mathop{\rm u}\nolimits} _3}\) in \({\mathbb{R}^2}\) such that \({{\mathop{\rm x}\nolimits} _4} = {\mathop{\rm v}\nolimits} \).

Short Answer

Expert verified

a. The formula for \({{\mathop{\rm x}\nolimits} _4}\) is \({{\mathop{\rm x}\nolimits} _4} = M{\mathop{\rm u}\nolimits} \).

b. The control sequence \({{\mathop{\rm u}\nolimits} _0},...,{{\mathop{\rm u}\nolimits} _3}\) makes \({{\mathop{\rm x}\nolimits} _4} = M{\mathop{\rm u}\nolimits} \).

Step by step solution

01

State the difference equation in exercise 17

A state-space model of a control system includes a difference equation of the form

\({{\mathop{\rm x}\nolimits} _{k + 1}} = A{{\mathop{\rm x}\nolimits} _k} + B{{\mathop{\rm u}\nolimits} _k}\)for \(k = 0,1,...\) …(1)

The pair \(\left( {A,B} \right)\) is said to becontrollableif rank\(\left[ {\begin{array}{*{20}{c}}B&{AB}&{{A^2}B}& \cdots &{{A^{n - 1}}B}\end{array}} \right] = n\).

02

Write the formula for \({{\mathop{\rm x}\nolimits} _4}\) involving the controllability matrix

a)

Consider \(A\) as a \(m \times n\) matrix with rank\(r\) and \({{\mathop{\rm u}\nolimits} _0},...,{{\mathop{\rm u}\nolimits} _3}\) as a sequence of input vectors in \({\mathbb{R}^2}\).

Use the equation \({{\mathop{\rm x}\nolimits} _{k + 1}} = A{{\mathop{\rm x}\nolimits} _k} + B{{\mathop{\rm u}\nolimits} _k}\) for \(k = 0,1,...\) with \({{\mathop{\rm x}\nolimits} _0} = 0\) as shown below:

\(\begin{array}{c}{{\mathop{\rm x}\nolimits} _1} = A{{\mathop{\rm x}\nolimits} _0} + B{{\mathop{\rm u}\nolimits} _0}\\ = B{{\mathop{\rm u}\nolimits} _0}\\{{\mathop{\rm x}\nolimits} _2} = A{{\mathop{\rm x}\nolimits} _1} + B{{\mathop{\rm u}\nolimits} _1}\\ = AB{{\mathop{\rm u}\nolimits} _0} + B{{\mathop{\rm u}\nolimits} _1}\end{array}\)

\(\begin{array}{c}{{\mathop{\rm x}\nolimits} _3} = A{{\mathop{\rm x}\nolimits} _2} + B{{\mathop{\rm u}\nolimits} _2}\\ = A\left( {AB{{\mathop{\rm u}\nolimits} _0} + B{{\mathop{\rm u}\nolimits} _1}} \right) + B{{\mathop{\rm u}\nolimits} _2}\\ = {A^2}B{{\mathop{\rm u}\nolimits} _0} + AB{{\mathop{\rm u}\nolimits} _1} + B{{\mathop{\rm u}\nolimits} _2}\end{array}\)

\(\begin{array}{c}{{\mathop{\rm x}\nolimits} _4} = A{{\mathop{\rm x}\nolimits} _3} + B{{\mathop{\rm u}\nolimits} _3}\\ = A\left( {{A^2}B{{\mathop{\rm u}\nolimits} _0} + AB{{\mathop{\rm u}\nolimits} _1} + B{{\mathop{\rm u}\nolimits} _2}} \right) + B{{\mathop{\rm u}\nolimits} _3}\\ = {A^3}B{{\mathop{\rm u}\nolimits} _0} + {A^2}B{{\mathop{\rm u}\nolimits} _1} + AB{{\mathop{\rm u}\nolimits} _2} + B{{\mathop{\rm u}\nolimits} _3}\\ = \left( {\begin{array}{*{20}{c}}B&{AB}&{{A^2}B}&{{A^3}B}\end{array}} \right)\left( {\begin{array}{*{20}{c}}{{{\mathop{\rm u}\nolimits} _3}}\\{{{\mathop{\rm u}\nolimits} _2}}\\{{{\mathop{\rm u}\nolimits} _1}}\\{{{\mathop{\rm u}\nolimits} _0}}\end{array}} \right)\\ = M{\mathop{\rm u}\nolimits} \end{array}\)

Matrix \(M\) has four rows since \(B\) does, and \(M\) has eight columns since \(B\) and each of the matrices \({A^k}B\) has two columns. Vector \({\mathop{\rm u}\nolimits} \) in the final equation is in \({\mathbb{R}^8}\) since each \({{\mathop{\rm u}\nolimits} _k}\) is in \({\mathbb{R}^2}\).

03

Explain why there exists a control sequence \({{\mathop{\rm u}\nolimits} _0},...,{{\mathop{\rm u}\nolimits} _3}\) in \({\mathbb{R}^2}\) 

b)

If the matrix pair \(\left( {A,B} \right)\) is controllable, then the controllability matrix has rank 4, with a pivot in every row, and the columns of \(M\) span \({\mathbb{R}^4}\). Therefore, for any vector \({\mathop{\rm v}\nolimits} \) in \({\mathbb{R}^4}\), there exists a vector u in \({\mathbb{R}^8}\) such that \({\mathop{\rm v}\nolimits} = M{\mathop{\rm u}\nolimits} \).

According to part (a), \({{\mathop{\rm x}\nolimits} _4} = M{\mathop{\rm u}\nolimits} \) when \({\mathop{\rm u}\nolimits} \) is partitioned into a control sequence \({{\mathop{\rm u}\nolimits} _0},...,{{\mathop{\rm u}\nolimits} _3}\).

Thus, the control sequence \({{\mathop{\rm u}\nolimits} _0},...,{{\mathop{\rm u}\nolimits} _3}\) makes \({{\mathop{\rm x}\nolimits} _4} = M{\mathop{\rm u}\nolimits} \).

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Most popular questions from this chapter

Let be a basis of\({\mathbb{R}^n}\). .Produce a description of an \(B = \left\{ {{{\bf{b}}_{\bf{1}}},....,{{\bf{b}}_n}\,} \right\}\)matrix A that implements the coordinate mapping \({\bf{x}} \mapsto {\left( {\bf{x}} \right)_B}\). Find it. (Hint: Multiplication by A should transform a vector x into its coordinate vector \({\left( {\bf{x}} \right)_B}\)). (See Exercise 21.)

[M] Let \(A = \left[ {\begin{array}{*{20}{c}}7&{ - 9}&{ - 4}&5&3&{ - 3}&{ - 7}\\{ - 4}&6&7&{ - 2}&{ - 6}&{ - 5}&5\\5&{ - 7}&{ - 6}&5&{ - 6}&2&8\\{ - 3}&5&8&{ - 1}&{ - 7}&{ - 4}&8\\6&{ - 8}&{ - 5}&4&4&9&3\end{array}} \right]\).

  1. Construct matrices \(C\) and \(N\) whose columns are bases for \({\mathop{\rm Col}\nolimits} A\) and \({\mathop{\rm Nul}\nolimits} A\), respectively, and construct a matrix \(R\) whose rows form a basis for Row\(A\).
  2. Construct a matrix \(M\) whose columns form a basis for \({\mathop{\rm Nul}\nolimits} {A^T}\), form the matrices \(S = \left[ {\begin{array}{*{20}{c}}{{R^T}}&N\end{array}} \right]\) and \(T = \left[ {\begin{array}{*{20}{c}}C&M\end{array}} \right]\), and explain why \(S\) and \(T\) should be square. Verify that both \(S\) and \(T\) are invertible.

Question: Exercises 12-17 develop properties of rank that are sometimes needed in applications. Assume the matrix \(A\) is \(m \times n\).

  1. Show that if \(B\) is \(n \times p\), then rank\(AB \le {\mathop{\rm rank}\nolimits} A\). (Hint: Explain why every vector in the column space of \(AB\) is in the column space of \(A\).
  2. Show that if \(B\) is \(n \times p\), then rank\(AB \le {\mathop{\rm rank}\nolimits} B\). (Hint: Use part (a) to study rank\({\left( {AB} \right)^T}\).)

Question 11: Let\(S\)be a finite minimal spanning set of a vector space\(V\). That is,\(S\)has the property that if a vector is removed from\(S\), then the new set will no longer span\(V\). Prove that\(S\)must be a basis for\(V\).

Suppose \({{\bf{p}}_{\bf{1}}}\), \({{\bf{p}}_{\bf{2}}}\), \({{\bf{p}}_{\bf{3}}}\), and \({{\bf{p}}_{\bf{4}}}\) are specific polynomials that span a two-dimensional subspace H of \({P_{\bf{5}}}\). Describe how one can find a basis for H by examining the four polynomials and making almost no computations.

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