非线性系统的线性化与泰勒级数

线性系统与非线性系统的区别

我们在读论文的时候经常会遇到这两个系统,线性系统与非线性系统,这两者之间有什么区别呢?

线性指量与量之间按比例、成直线的关系,在空间和时间上代表规则和光滑的运动;非线性则指不按比例、不成直线的关系代表不规则的运动和突变。

在判断是否是线性系统上,主要看叠加原理(Superposition)!!!

系统的方程为 x ˙ = f ( x ) \dot x = f(x) x˙=f(x) ,如果 x 1 , x 2 x_1,x_2 x1,x2 是方程的解,有 x 3 = k 1 x 1 + k 2 x 2 ( k 1 , k 2 ∈ R ) x_3 = k_1 x_1 + k_2 x_2(k_1,k_2 \in \R) x3=k1x1+k2x2(k1,k2R) ,且 x 3 x_3 x3 也是方程的解,则系统符合叠加原理,为线性系统。

举个例子:
x ¨ + 2 x ˙ + 2 x = 0 ( 线性系统 ) x ¨ + 2 x ˙ + 2 x 2 = 0 ( 非线性系统 ) x ¨ + 2 s i n ( x ˙ ) + 2 x = 0 ( 非线性系统 ) \ddot x + 2 \dot x + \sqrt{2} x = 0 (线性系统) \\ \ddot x + 2 \dot x + \sqrt{2} x ^ 2 = 0 (非线性系统) \\ \ddot x + 2 sin(\dot x) + \sqrt{2} x = 0 (非线性系统) \\ x¨+2x˙+2 x=0(线性系统)x¨+2x˙+2 x2=0(非线性系统)x¨+2sin(x˙)+2 x=0(非线性系统)

线性化方法

泰勒级数(Taylor Series)
f ( x ) = f ( x 0 ) + f ′ ( x 0 ) 1 ! ( x − x 0 ) + f ′ ′ ( x 0 ) 2 ! ( x − x 0 ) 2 + . . . + f n ( x 0 ) n ! ( x − x 0 ) n f(x) = f(x_0) + \frac{f'(x_0)}{1 !}(x-x_0) + \frac{f''(x_0)}{2 !}(x-x_0)^2 + ...+\frac{f^{n}(x_0)}{n !}(x-x_0)^n f(x)=f(x0)+1!f(x0)(xx0)+2!f′′(x0)(xx0)2+...+n!fn(x0)(xx0)n
如果 x − x 0 → 0 x-x_0 \to 0 xx00,则 ( x − x 0 ) 2 → 0 (x-x_0)^2 \to 0 (xx0)20 ,则 ( x − x 0 ) n → 0 (x-x_0)^n \to 0 (xx0)n0 f ( x ) = f ( x 0 ) + f ′ ( x 0 ) ( x − x 0 ) = k 2 x + b f(x) = f(x_0) + f'(x_0)(x-x_0)=k_2x+b f(x)=f(x0)+f(x0)(xx0)=k2x+b,其中 k 2 = f ′ ( x 0 ) , b = f ( x 0 ) − f ′ ( x 0 ) x 0 k_2 = f'(x_0),b = f(x_0) - f'(x_0)x_0 k2=f(x0),b=f(x0)f(x0)x0

线性化是在某一点附近的线性化,而不是全局的线性化。

一维系统,举个例子

x ¨ + x ˙ + 1 x = 1 \ddot x + \dot x + \frac{1}{x} = 1 x¨+x˙+x1=1

在平衡点(Fixed Point)附近线性化。
x ¨ = x ˙ = 0 ⇒ 1 x = 1 ⇒ x 0 = 1 \ddot x = \dot x = 0 \Rightarrow \frac{1}{x} = 1 \Rightarrow x_0 = 1 x¨=x˙=0x1=1x0=1
所以平衡点为 x 0 = 1 x_0 = 1 x0=1 。在 x 0 x_0 x0 附近 x δ = x 0 + x d x_{\delta} = x_0 + x_d xδ=x0+xd , 所以有
x ¨ δ + x ˙ δ + 1 x δ = 1 \ddot x_{\delta} + \dot x_{\delta} + \frac{1}{x_{\delta} } = 1 x¨δ+x˙δ+xδ1=1
运用上面的泰勒级数,先线性化 1 x δ \frac{1}{x_\delta} xδ1
f ( x δ ) = f ( x 0 ) + f ′ ( x 0 ) ( x δ − x 0 ) f(x_{\delta}) = f(x_{0}) + f'(x_0)(x_{\delta}-x_0) f(xδ)=f(x0)+f(x0)(xδx0)

1 x δ = 1 x 0 + ( − 1 x 0 2 ) ( x δ − x 0 ) = 1 x 0 − x d x 0 2 = 1 − x d \frac{1}{x_\delta} = \frac{1}{x_0} + (-\frac{1}{x_0^2})(x_\delta - x_0) = \frac{1}{x_0} -\frac{x_d}{x_0^2} = 1-x_d xδ1=x01+(x021)(xδx0)=x01x02xd=1xd

{ x ¨ δ = x ¨ 0 + x ¨ d x ˙ δ = x ˙ 0 + x ˙ d 1 x δ = 1 − x d ⇒ x ¨ 0 + x ¨ d + x ˙ 0 + x ˙ d + 1 − x d = 1 ⇒ x ¨ d + x ˙ d − x d = 0 \begin{cases} \ddot x_{\delta} = \ddot x_{0} + \ddot x_{d} \\ \dot x_{\delta} = \dot x_{0} + \dot x_{d} \\ \frac{1}{x_{\delta}} = 1 - x_d \\ \end{cases} \Rightarrow \ddot x_{0} + \ddot x_{d} + \dot x_{0} + \dot x_{d} + 1 - x_d = 1 \Rightarrow \ddot x_{d} + \dot x_{d} - x_d = 0 x¨δ=x¨0+x¨dx˙δ=x˙0+x˙dxδ1=1xdx¨0+x¨d+x˙0+x˙d+1xd=1x¨d+x˙dxd=0

二维系统,举个例子

2维空间中,在平衡点附近

{ x ˙ 1 = f 1 ( x 1 , x 2 ) x ˙ 2 = f 2 ( x 1 , x 2 ) ⇒ [ x ˙ 1 d x ˙ 2 d ] = [ ∂ f 1 x 1 ∂ f 1 x 2 ∂ f 2 x 1 ∂ f 2 x 2 ] x = x 0 [ x 1 d x 2 d ] \begin{cases} \dot x_1 = f_1 (x_1,x_2) \\ \dot x_2 = f_2 (x_1,x_2) \\ \end{cases} \Rightarrow \begin{bmatrix} \dot x_{1d} \\ \dot x_{2d} \end{bmatrix} = \begin{bmatrix} \frac{\partial f_1}{x_1} & \frac{\partial f_1}{x_2} \\ \frac{\partial f_2}{x_1} & \frac{\partial f_2}{x_2} \\ \end{bmatrix} _ {x = x_0} \begin{bmatrix} x_{1d} \\ x_{2d} \end{bmatrix} { x˙1=f1(x1,x2)x˙2=f2(x1,x2)[x˙1dx˙2d]=[x1f1x1f2x2f1x2f2]x=x0[x1dx2d]

x ¨ + x ˙ + 1 x = 1 \ddot x + \dot x + \frac{1}{x} = 1 x¨+x˙+x1=1

let x 1 = x , x 2 = x ˙ x_1 = x,x_2 = \dot x x1=x,x2=x˙ ,that has

{ x ˙ 1 = x 2 x ˙ 2 = x ¨ = 1 − x ˙ − 1 x = 1 − x 2 − 1 x 1 \begin{cases} \dot x_1 = x_2 \\ \dot x_2 = \ddot x = 1- \dot x - \frac{1}{x} = 1- x_2 - \frac{1}{x_1} \end{cases} { x˙1=x2x˙2=x¨=1x˙x1=1x2x11

寻找平衡点,令 x ˙ 1 = 0 , x ˙ 2 = 0 \dot x_1 = 0,\dot x_2 = 0 x˙1=0,x˙2=0 ,则有平衡点 x 10 = 1 , x 20 = 0 x_{10} = 1,x_{20} = 0 x10=1,x20=0

[ x ˙ 1 d x ˙ 2 d ] = [ 0 1 − ( − 1 x 1 2 ) − 1 ] x 0 [ x 1 d x 2 d ] = [ 0 1 1 − 1 ] [ x 1 d x 2 d ] \begin{bmatrix} \dot x_{1d} \\ \dot x_{2d} \\ \end{bmatrix} = \begin{bmatrix} 0 & 1 \\ -(-\frac{1}{x_1^2}) & -1 \\ \end{bmatrix} _ {x_0} \begin{bmatrix} x_{1d} \\ x_{2d} \\ \end{bmatrix} = \begin{bmatrix} 0 & 1 \\ 1 & -1 \\ \end{bmatrix} \begin{bmatrix} x_{1d} \\ x_{2d} \end{bmatrix} [x˙1dx˙2d]=[0(x121)11]x0[x1dx2d]=[0111][x1dx2d]

{ x ˙ 1 d = x 2 d x ˙ 2 d = x 1 d − x 2 d \begin{cases} \dot x_{1d} = x_{2d} \\ \dot x_{2d} = x_{1d} - x_{2d} \end{cases} { x˙1d=x2dx˙2d=x1dx2d
其实我们只需要下半部分
x ˙ 2 d = x 1 d − x 2 d ⇒ x ¨ d = x d − x ˙ d ⇒ x ¨ d + x ˙ d − x d = 0 \dot x_{2d} = x_{1d} - x_{2d} \Rightarrow \ddot x_d = x_d - \dot x_d \Rightarrow \ddot x_d + \dot x_d - x_d = 0 x˙2d=x1dx2dx¨d=xdx˙dx¨d+x˙dxd=0
和上面的一维系统的等式是一样的。

总结

线性化公式, x − x 0 → 0 x-x_0 \to 0 xx00
f ( x ) = f ( x 0 ) + f ′ ( x 0 ) ( x − x 0 ) f(x) = f(x_0) + f'(x_0)(x-x_0) f(x)=f(x0)+f(x0)(xx0)

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转载自blog.csdn.net/weixin_43903639/article/details/130816850
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