Presentation is loading. Please wait.

Presentation is loading. Please wait.

Controllability and Observability of Linear Dynamical Equations

Similar presentations


Presentation on theme: "Controllability and Observability of Linear Dynamical Equations"— Presentation transcript:

1 Controllability and Observability of Linear Dynamical Equations
Chapter 2 Controllability and Observability of Linear Dynamical Equations

2 Introduction 1. Assumption and admissible control
Consider linear system where and are and matrices, respectively. We assume that A(t),B(t)和u的连续和分段连续性保证了解的唯一性。 黄琳:稳定性理论p.110:变系数线性系统无论什么系统性质,例如稳定性或可控性,都与初始时刻的选取密切相关, 因而使一致性问题显得突出起来。 1). A(t), B(t), C(t), D(t) are continuous on ; 2). u(t) is a continuous or piecewise continuous functions on , and is called admissible control.

3 where and are and matrices, respectively.

4 Whether the states of the system can be controlled by the input u?
2. Concepts for controllability Whether the states of the system can be controlled by the input u? Example: Consider the following state equation 给定线性系统(2-1), 它由两个方程来组成:状态方程和输出方程。既然系统由这两个方程来描述, 系统的状态能否由u 来控制?这个问题的意义是显而易见的。既然是控制系统,若系统的状态不能由u来控制, 则这样的系统一般来说是不能正常工作的。 The state x2 can not be moved by the input u.

5

6 §2-1 linear Independence of Time Functions
1. Linearly dependent of a set of functions on some interval 1.Scalar case: Consider a set of continuous complex-value functions over the interval Definition 2-1 Complex-valued functions is said to be linearly dependent on the interval over the field of complex numbers if there exist complex numbers not all zero, such that Otherwise, the set of functions is said to be linearly independent on over the field of complex numbers.

7 Remark: Unlike the linear dependence or independence in linear algebra, the interval on which the variables are defined is important in examining the linear dependence of a set of variables. 2) are complex-value constants. Without loss of generality, we assume that t1>t2. Also, it is assumed that fi (t) are continuous on the interval.

8 Example:Determine the dependence of the two functions f1(t)=t and f2(t)=t2 defined on the interval [0, 1]. Let Obviously, there does not exist a nonzero constant  satisfying the equation on the interval. Therefore, functions f1 and f2 are linearly independent on [0, 1].

9 Example 2-1 Consider the linear dependence of two functions and defined by

10 Consider the equation f1(t)=f2(t).
-1 Consider the equation f1(t)=f2(t). It is clear that the functions f1(t) and f2(t) are linearly dependent on [0, 1], if we choose =1; The functions f1(t) and f2(t) are linearly dependent on [-1, 0], if we choose =-1;

11 f1(t) and f2(t) are linearly independent on [1, 1], since such a constant  does not exist.

12 Proof: By contradiction.
-1 1 f1 f2 [ ] -ε ε Example: If a set of continuous functions f1(t), f2(t),, fn(t) are linearly independent on some interval [t1, t2], then they are linearly independent on any interval [ta, tb] satisfying [ta , tb] [t1, t2] Proof: By contradiction.

13 2. Vector case: The concept of linear independence can be extended to vector-valued functions. Let f1, f2 , …, fn be 1×p complex-valued functions of t; then the 1×p complex-valued functions. f1, f2 , …, fn are linearly dependent on [t1, t2], if there exist complex numbers , not all zero, such that fi(t)=[fi1(t),fi2(t),….,fin(t)]

14 1×p complex-valued functions f1, f2 , …, fn are linearly independent on [t1, t2], if and only if

15 2. Gram matrix Definition 2-2 Let f1, f2 , …, fn be 1×p complex-valued functions on [t1,t2]. Let F be the n×p matrix with fi as its ith row. Define Gram matrix where F* is the complex conjugate of F. Note that for given t1 and t2, W(t1, t2) is a constant matrix.

16 Theorem 2-1 f1, f2 , …, fn are linearly independent on [t1, t2], if and only if W(t1, t2) is nonsingular. Proof:Sufficiency. By contradiction. In fact, if fi are linearly dependent, then there exists a non-zero 1×n row vector  such that Hence, we have which means that the rows of W(t1, t2) are linearly dependent ,which contradicts the assumption.

17 Necessity. By contradiction
Necessity. By contradiction. Let fi be linearly independent on [t1,t2], but W(t1,t2) is singular. Then, there exists a nonzero 1×n row vector  satisfies or Since the integrand is a continuous function and is nonnegative for all t in [t1,t2], the above equation implies This contradicts the assumption . Q.E.D

18 Example:Consider the linear dependence of the functions f1(t)=t and f2(t)=t2 defined over [0, 1].
Let Then, This example indicates that the Gram matrix is not only nonsingular, but also a positive definite matrix.

19 2. Some useful criteria Theorem 2-2 Assume that the 1×p complex-valued functions f1, f2 , …, fn have continuous derivatives up to order (n-1) on the interval [t1, t2]. Let F be the n×p matrix with fi as its row, and let F(k) be the kth derivative of F. If there exists some t0 in [t1, t2] such that the n×np matrix then f1, f2 , …, fn are linearly independent on [t1, t2] over the field of complex numbers. 存在一个t0就是要找一个t0,这一点有时可能不容易。

20 Proof The proof is by contradiction. Suppose that (A
Proof The proof is by contradiction. Suppose that (A.1) holds and f1, f2 , …, fn are linearly dependent on [t1,t2]. Then from the definition there exists a nonzero 1×n row vector such that which implies that Hence we have which implies that all the n rows of are linearly dependent on the interval. Q.E.D

21 The condition of theorem 2-2 is sufficient but not necessary for a set of functions to be linearly independent. This can be seen from the following example. Example 2-2 Consider the two functions f1(t)= t3, f2(t)= , defined over [1, 1]. They are linearly independent on [1,1]; however

22

23 Theorem 2-3 Assume that for each i, fi is analytic on [t1,t2]
Theorem 2-3 Assume that for each i, fi is analytic on [t1,t2]. Let t0 be any fixed point in [t1,t2]. Then the fi’s are linearly independent on [t1,t2] if and only if Proof: The sufficiency of the theorem can be proved as in Theorem 2-2. Necessity: we prove the necessity of the theorem by contradiction. Suppose that fi on [t1,t2] are linearly independent, and such that

24 The fi,s are analytic on [t1,t2]
which contradicts the hypothesis that fi,s are linearly independent on [t1,t2]. Q.E.D

25 Corollary 1: Assume that, fi are analytic and linearly independent on [t1, t2]. Then
For all Corollary 2: Assume that fi,s are analytic and linearly independent on [t1, t2]. Then fi are linearly independent on any subinterval of [t1, t2]. Remark: (1).(2-6) is an infinite matrix and (2). t is any fixed point in [t1, t2] . Example 2-3: Let

26 It is easy to see that, rank[F(t) F(1)(t)] <2, if
But we have the conclusion as follows Theorem: Assume fi (i=1,2,n) are analytic on [t1, t2]. fi,s are linearly independent on [t1, t2] if and only if for almost all t in [t1, t2]. Proof: omitted.

27 Example: Consider the functions
Check their linear independence over Because the three functions are analytic on , the above theorem can be used. Define the vector Consider

28 Replacing t with yields
Therefore, the three functions are linearly independent on However, if we choose t=0, the above matrix is singular. .


Download ppt "Controllability and Observability of Linear Dynamical Equations"

Similar presentations


Ads by Google