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自适应控制——仿真实验三 用超稳定性理论设计模型参考自适应系统
2022-07-31 14:28:00 【lan 606】
一、问题描述
设控制对象的传递函数为
W p ( s ) = k 1 T 1 2 s 2 + 2 T 1 ξ 1 s + 1 (1) W_{p}(s)=\frac{k_{1}}{T_{1}^{2} s^{2}+2 T_{1} \xi_{1} s+1} \tag{1} Wp(s)=T12s2+2T1ξ1s+1k1(1)
参数 k 1 k_1 k1, T 1 T_{1} T1 和 ξ 1 \xi_{1} ξ1 随时间而变的变化规律为
k 1 ( t ) = 1.12 − 0.008 t , T 1 ( t ) = 0.036 + 0.004 t , ξ 1 ( t ) = 0.8 − 0.01 t (2) k_{1}(t)=1.12-0.008 t, \quad T_{1}(t)=0.036+0.004 t, \quad \xi_{1}(t)=0.8-0.01 t \tag{2} k1(t)=1.12−0.008t,T1(t)=0.036+0.004t,ξ1(t)=0.8−0.01t(2)
设参考模型的传递函数为
W m ( s ) = 1 0.0 8 2 s 2 + 2 × 0.08 × 0.75 s + 1 (3) W_{m}(s)=\frac{1}{0.08^{2} s^{2}+2 \times 0.08 \times 0.75 s+1} \tag{3} Wm(s)=0.082s2+2×0.08×0.75s+11(3)
用超稳定性理论设计模型参考自适应系统。
假定系统参考输入:
r ( t ) r(t) r(t) 是方波信号,周期为4s,振幅为 ± 2 \pm 2 ±2。
设计自适应规律,给出仿真结果。
二、问题建模
本次仿真实验主要针对的是带状态变量滤波器(情况1)下的并联模型参考自适应控制系统。设参考模型方程为:
( ∑ i = 0 n a m i p i ) y m = ( ∑ i = 0 m b m i p i ) r , a m n = 1 (4) \left(\sum_{i=0}^{n} a_{m i} p^{i}\right) y_{m}=\left(\sum_{i=0}^{m} b_{m i} p^{i}\right) r, a_{m n}=1 \tag{4} (i=0∑namipi)ym=(i=0∑mbmipi)r,amn=1(4)
接在参考模型输出端的状态变量滤波器的方程为:
( ∑ i = 0 n − 1 c i p i ) y m f = y m , c n − 1 = 1 (5) \left(\sum_{i=0}^{n-1} c_{i} p^{i}\right) y_{m f}=y_{m}, c_{n-1}=1 \tag{5} (i=0∑n−1cipi)ymf=ym,cn−1=1(5)
接在可调系统输入端的状态变量滤波器的方程为:
( ∑ i = 0 n − 1 c i p i ) r f = r , c n − 1 = 1 (6) \left(\sum_{i=0}^{n-1} c_{i} p^{i}\right) r_{f}=r, c_{n-1}=1 \tag{6} (i=0∑n−1cipi)rf=r,cn−1=1(6)
可调系统方程为:
( ∑ i = 0 n a s i ( v , t ) p i ) y s f = ( ∑ i = 0 m b s i ( v , t ) p i ) r f , a s n ( v , t ) = 1 (7) \left(\sum_{i=0}^{n} a_{s i}(v, t) p^{i}\right) y_{s f}=\left(\sum_{i=0}^{m} b_{s i}(v, t) p^{i}\right) r_{f}, a_{s n}(v, t)=1 \tag{7} (i=0∑nasi(v,t)pi)ysf=(i=0∑mbsi(v,t)pi)rf,asn(v,t)=1(7)
广义输出误差为:
ε f = y m f − y s f (8) \varepsilon_{f}=y_{m f}-y_{s f} \tag{8} εf=ymf−ysf(8)
为保证等价的前向方块严格正实,引入串联补偿器:
v = D ( p ) ε f = ( ∑ i = 0 n − 1 d i p i ) ε f (9) v=D(p) \varepsilon_{f}=\left(\sum_{i=0}^{n-1} d_{i} p^{i}\right) \varepsilon_{f} \tag{9} v=D(p)εf=(i=0∑n−1dipi)εf(9)
对于可调系统中的可调参数 a s i ( v , t ) a_{si}(v,t) asi(v,t) 及 b s i ( v , t ) b_{si}(v,t) bsi(v,t),采取PI控制,则自适应规律为
a s i ( v , t ) = ∫ 0 t φ 1 i ( v , t , τ ) d τ + φ 2 i ( v , t ) + a s i ( 0 ) , i = 0 , 1 , ⋯ , n − 1 b s i ( v , t ) = ∫ 0 t ψ 1 i ( v , t , τ ) d τ + ψ 2 i ( v , t ) + b s i ( 0 ) , i = 0 , 1 , ⋯ , m (10) \begin{aligned} a_{s i}(v, t)&=\int_{0}^{t} \varphi_{1 i}(v, t, \tau) d \tau+\varphi_{2 i}(v, t)+a_{s i}(0), i=0,1, \cdots, n-1 \\ b_{s i}(v, t)&=\int_{0}^{t} \psi_{1 i}(v, t, \tau) d \tau+\psi_{2 i}(v, t)+b_{s i}(0), i=0,1, \cdots, m \end{aligned} \tag{10} asi(v,t)bsi(v,t)=∫0tφ1i(v,t,τ)dτ+φ2i(v,t)+asi(0),i=0,1,⋯,n−1=∫0tψ1i(v,t,τ)dτ+ψ2i(v,t)+bsi(0),i=0,1,⋯,m(10)
将参考模型与状态变量滤波器互换位置,可得到形式同(7)式的参考模型方程如下:
( ∑ i = 0 n a m i p i ) y m f = ( ∑ i = 0 m b m i p i ) r f , a m n = 1 (11) \left(\sum_{i=0}^{n} a_{m i} p^{i}\right) y_{m f}=\left(\sum_{i=0}^{m} b_{m i} p^{i}\right) r_{f}, a_{m n}=1 \tag{11} (i=0∑namipi)ymf=(i=0∑mbmipi)rf,amn=1(11)
结合(7)式,(11)式和(8)式,可推导出
( ∑ i = 0 n a m i p i ) ε f = [ ∑ i = 0 n ( a s i − a m i ) p i ] y s f + [ ∑ i = 0 n ( b m i − b s i ) p i ] r f (12) \left(\sum_{i=0}^{n} a_{m i} p^{i}\right) \varepsilon_{f}=\left[\sum_{i=0}^{n}\left(a_{s i}-a_{m i}\right) p^{i}\right] y_{s f}+\left[\sum_{i=0}^{n}\left(b_{m i}-b_{s i}\right) p^{i}\right] r_{f} \tag{12} (i=0∑namipi)εf=[i=0∑n(asi−ami)pi]ysf+[i=0∑n(bmi−bsi)pi]rf(12)
令
ω 1 = [ ∑ i = 0 n ( a s i − a m i ) p i ] y s f + [ ∑ i = 0 n ( b m i − b s i ) p i ] r f (13) \omega_{1}=\left[\sum_{i=0}^{n}\left(a_{s i}-a_{m i}\right) p^{i}\right] y_{s f}+\left[\sum_{i=0}^{n}\left(b_{m i}-b_{s i}\right) p^{i}\right] r_{f} \tag{13} ω1=[i=0∑n(asi−ami)pi]ysf+[i=0∑n(bmi−bsi)pi]rf(13)
则(12)式变成
( ∑ i = 0 n a m i p i ) ε f = ω 13 (14) \left(\sum_{i=0}^{n} a_{m i} p^{i}\right) \varepsilon_{f}=\omega_{13} \tag{14} (i=0∑namipi)εf=ω13(14)
将可调系统中的可调参数选定的自适应规律(10)式代入(13)式中,可得反馈方框的输出量 ω \omega ω 的形式如下:
ω = − ω 1 = − { ∑ i = 0 n − 1 [ ∫ 0 t φ 1 i ( v , t , τ ) d τ + φ 2 i ( v , t ) + a s i ( 0 ) − a m i ] p i } y s f + { ∑ i = 0 n − 1 [ ∫ 0 t ψ 1 i ( v , t , τ ) d τ + ψ 2 i ( v , t ) + b s i ( 0 ) − b m i ] p i } r f (15) \begin{aligned} \omega=-\omega_{1}=&-\left\{\sum_{i=0}^{n-1}\right. {\left.\left[\int_{0}^{t} \varphi_{1 i}(v, t, \tau) d \tau+\varphi_{2 i}(v, t)+a_{s i}(0)-a_{m i}\right] p^{i}\right\} y_{s f} } \\ &+\left\{\sum_{i=0}^{n-1}\left[\int_{0}^{t} \psi_{1 i}(v, t, \tau) d \tau+\psi_{2 i}(v, t)+b_{s i}(0)-b_{m i}\right] p^{i}\right\} r_{f} \end{aligned} \tag{15} ω=−ω1=−{ i=0∑n−1[∫0tφ1i(v,t,τ)dτ+φ2i(v,t)+asi(0)−ami]pi}ysf+{ i=0∑n−1[∫0tψ1i(v,t,τ)dτ+ψ2i(v,t)+bsi(0)−bmi]pi}rf(15)
由波波夫积分不等式,及引理1和引理2,可得自适应规律
φ 1 i = − k a i ( t − τ ) v ( τ ) p i y s f ( τ ) , τ ≤ t , i = 0 , 1 , ⋯ , n − 1 φ 2 i = − k a i ′ ( t ) v ( t ) p i y s f ( t ) , i = 0 , 1 , ⋯ , n − 1 ψ 1 i = k b i ( t − τ ) v ( τ ) p i r f ( τ ) , τ ≤ t , i = 0 , 1 , ⋯ , m ψ 2 i = k b i ′ ( t ) v ( t ) p i r f ( t ) , i = 0 , 1 , ⋯ , m (16) \begin{aligned} \varphi_{1 i}&=-k_{a i}(t-\tau) v(\tau) p^{i} y_{s f}(\tau), \quad \tau \leq t, i=0,1, \cdots, n-1 \\ \varphi_{2 i}&=-k_{a i}^{\prime}(t) v(t) p^{i} y_{s f}(t), \quad i=0,1, \cdots, n-1 \\ \psi_{1 i}&=k_{b i}(t-\tau) v(\tau) p^{i} r_{f}(\tau), \quad \tau \leq t, i=0,1, \cdots, m \\ \psi_{2 i}&=k_{b i}^{\prime}(t) v(t) p^{i} r_{f}(t), \quad i=0,1, \cdots, m \end{aligned} \tag{16} φ1iφ2iψ1iψ2i=−kai(t−τ)v(τ)piysf(τ),τ≤t,i=0,1,⋯,n−1=−kai′(t)v(t)piysf(t),i=0,1,⋯,n−1=kbi(t−τ)v(τ)pirf(τ),τ≤t,i=0,1,⋯,m=kbi′(t)v(t)pirf(t),i=0,1,⋯,m(16)
式中, k a i ( t − τ ) k_{a i}(t-\tau) kai(t−τ) 和 k b i ( t − τ ) k_{b i}(t-\tau) kbi(t−τ) 是正定标量积分核,它们的拉普拉斯变化式为在 s = 0 s=0 s=0 处有一极点的正实传递函数; k a i ′ k_{a i}^{\prime} kai′ 和 k b i ′ k_{b i}^{\prime} kbi′ 在 t ≥ 0 t\ge0 t≥0 时为非负标量增益。
三、问题求解
将原题中给定的参考模型和可调系统的传递函数写成输入输出方程的形式:
( 0.0 8 2 p 2 + 2 × 0.08 × 0.75 p + 1 ) y m = r ( T 1 2 p 2 + 2 T 1 ξ 1 p + 1 ) y s f = k 1 r f (17) \begin{gathered} &\left(0.08^{2} p^{2}+2 \times 0.08 \times 0.75 p+1\right) y_{m}=r \\ &\left(T_{1}^{2} p^{2}+2 T_{1} \xi_{1} p+1\right) y_{s f}=k_{1} r_{f} \end{gathered} \tag{17} (0.082p2+2×0.08×0.75p+1)ym=r(T12p2+2T1ξ1p+1)ysf=k1rf(17)
再将上式写成首一古尔维兹多项式的形式:
( p 2 + 2 × 0.08 × 0.75 0.0 8 2 p + 1 0.0 8 2 ) y m = 1 0.0 8 2 r ( p 2 + 2 T 1 ξ 1 T 1 2 + 1 T 1 2 ) y s f = k 1 T 1 2 r f (18) \begin{gathered} \left(p^{2}+\frac{2 \times 0.08 \times 0.75}{0.08^{2}} p+\frac{1}{0.08^{2}}\right) y_{m}=\frac{1}{0.08^{2}} r \\ \left(p^{2}+\frac{2 T_{1} \xi_{1}}{T_{1}^{2}}+\frac{1}{T_{1}^{2}}\right) y_{s f}=\frac{k_{1}}{T_{1}^{2}} r_{f} \end{gathered} \tag{18} (p2+0.0822×0.08×0.75p+0.0821)ym=0.0821r(p2+T122T1ξ1+T121)ysf=T12k1rf(18)
对照(7)式和(11)式,可知相关参数如下:
a m 1 = 2 × 0.08 × 0.75 0.0 8 2 = 18.75 a m 0 = 1 0.0 8 2 = 156.25 b m 0 = 1 0.0 8 2 = 156.25 a s 1 ( v , t ) = 2 T 1 ( t ) ξ 1 ( t ) T 1 2 ( t ) = 2 ( 0.8 − 0.01 t ) ( 0.036 + 0.004 t ) a s 0 ( v , t ) = 1 T 1 2 ( t ) = 1 ( 0.036 + 0.004 t ) 2 b s 0 ( v , t ) = k 1 ( t ) T 1 2 ( t ) = 1.12 − 0.008 t ( 0.036 + 0.004 t ) 2 (19) \begin{aligned} &a_{m 1}=\frac{2 \times 0.08 \times 0.75}{0.08^{2}}=18.75 \\ &a_{m 0}=\frac{1}{0.08^{2}}=156.25 \\ &b_{m 0}=\frac{1}{0.08^{2}}=156.25 \\ &a_{s 1}(v, t)=\frac{2 T_{1}(t) \xi_{1}(t)}{T_{1}^{2}(t)}=\frac{2(0.8-0.01 t)}{(0.036+0.004 t)} \\ &a_{s 0}(v, t)=\frac{1}{T_{1}^{2}(t)}=\frac{1}{(0.036+0.004 t)^{2}} \\ &b_{s 0}(v, t)=\frac{k_{1}(t)}{T_{1}^{2}(t)}=\frac{1.12-0.008 t}{(0.036+0.004 t)^{2}} \end{aligned} \tag{19} am1=0.0822×0.08×0.75=18.75am0=0.0821=156.25bm0=0.0821=156.25as1(v,t)=T12(t)2T1(t)ξ1(t)=(0.036+0.004t)2(0.8−0.01t)as0(v,t)=T12(t)1=(0.036+0.004t)21bs0(v,t)=T12(t)k1(t)=(0.036+0.004t)21.12−0.008t(19)
进而可知, a s 1 ( 0 ) ≈ 44.4 a_{s 1}(0) \approx 44.4 as1(0)≈44.4, a s 0 ( 0 ) ≈ 771.6 a_{s 0}(0) \approx 771.6 as0(0)≈771.6, b s 0 ( 0 ) ≈ 864.2 b_{s 0}(0) \approx 864.2 bs0(0)≈864.2。
设输出的广义误差为
ε f = y m f − y s f (20) \varepsilon_{f}=y_{m f}-y_{s f} \tag{20} εf=ymf−ysf(20)
串联补偿器方程为
v = D ( p ) ε f = ( d 1 p + d 0 ) ε f (21) v=D(p) \varepsilon_{f}=\left(d_{1} p+d_{0}\right) \varepsilon_{f} \tag{21} v=D(p)εf=(d1p+d0)εf(21)
选取的自适应规律如下
a s i ( v , t ) = ∫ 0 t φ 1 i ( v , t , τ ) d τ + φ 2 i ( v , t ) + a s i ( 0 ) , i = 0 , 1 b s 0 ( v , t ) = ∫ 0 t ψ 10 ( v , t , τ ) d τ + ψ 20 ( v , t ) + b s 0 ( 0 ) (22) \begin{aligned} a_{s i}(v, t)&=\int_{0}^{t} \varphi_{1 i}(v, t, \tau) d \tau+\varphi_{2 i}(v, t)+a_{s i}(0), i=0,1 \\ b_{s 0}(v, t)&=\int_{0}^{t} \psi_{10}(v, t, \tau) d \tau+\psi_{20}(v, t)+b_{s 0}(0) \end{aligned} \tag{22} asi(v,t)bs0(v,t)=∫0tφ1i(v,t,τ)dτ+φ2i(v,t)+asi(0),i=0,1=∫0tψ10(v,t,τ)dτ+ψ20(v,t)+bs0(0)(22)
参考(16)式的形式,可得可调参数的自适应规律如下:
φ 10 = − k a 0 ( t − τ ) v ( τ ) y s f ( τ ) , τ ≤ t φ 20 = − k a 0 ′ ( t ) v ( t ) y s f ( t ) φ 11 = − k a 1 ( t − τ ) v ( τ ) p y s f ( τ ) , τ ≤ t φ 21 = − k a 1 ′ ( t ) v ( t ) p y s f ( t ) ψ 10 = k b 0 ( t − τ ) v ( τ ) r f ( τ ) , τ ⩽ t ψ 20 = k b 0 ′ ( t ) v ( t ) r f ( t ) (23) \begin{aligned} \varphi_{1 0}&=-k_{a 0}(t-\tau) v(\tau) y_{sf}(\tau), \quad \tau \le t \\ \varphi_{2 0}&=-k_{a 0}^{\prime}(t) v(t) y_{s f}(t) \\ \varphi_{1 1}&=-k_{a 1}(t-\tau) v(\tau) p y_{sf}(\tau), \quad \tau \le t \\ \varphi_{2 1}&=-k_{a 1}^{\prime}(t) v(t) p y_{s f}(t) \\ \psi_{1 0}&=k_{b 0}(t-\tau) v(\tau) r_{f}(\tau), \quad \tau \leqslant t \\ \psi_{2 0}&=k_{b 0}^{\prime}(t) v(t) r_{f}(t) \end{aligned} \tag{23} φ10φ20φ11φ21ψ10ψ20=−ka0(t−τ)v(τ)ysf(τ),τ≤t=−ka0′(t)v(t)ysf(t)=−ka1(t−τ)v(τ)pysf(τ),τ≤t=−ka1′(t)v(t)pysf(t)=kb0(t−τ)v(τ)rf(τ),τ⩽t=kb0′(t)v(t)rf(t)(23)
式中, k a 0 ( t − τ ) k_{a 0}(t-\tau) ka0(t−τ)、 k a 1 ( t − τ ) k_{a 1}(t-\tau) ka1(t−τ) 和 k b 0 ( t − τ ) k_{b 0}(t-\tau) kb0(t−τ) 为正定积分核, k a 0 ′ ( t ) k_{a 0}^{\prime}(t) ka0′(t)、 k a 1 ′ ( t ) k_{a 1}^{\prime}(t) ka1′(t) 和 k b 0 ′ ( t ) k_{b 0}^{\prime}(t) kb0′(t) 对 ∀ t ≥ 0 \forall t \ge 0 ∀t≥0 均为非负标量增益。
下面再讨论一下引入的串联补偿器中的参数 d 0 d_0 d0 和 d 1 d_1 d1 的取值范围,系统的等价前向线性方块传递函数为:
h ( s ) = d 1 ( s ) + d 0 s 2 + a m 1 s + a m 0 (24) h(s)=\frac {d_1(s)+d_0} {s^2+a_{m1}s+a_{m0}} \tag{24} h(s)=s2+am1s+am0d1(s)+d0(24)
其对应的能控标准型如下:
e ˙ = A m e + b ω 1 v = d T e (25) \begin{aligned} \boldsymbol{\dot e} &= \boldsymbol{A_m} \boldsymbol{e} + b \omega_1 \\ v &= d^T \boldsymbol{e} \end{aligned} \tag{25} e˙v=Ame+bω1=dTe(25)
式中, e = [ ε ε ˙ ] \boldsymbol{e}=\left[ \begin{matrix} \varepsilon \\ \dot \varepsilon \end{matrix} \right] e=[εε˙], A m = [ 0 1 − a m 0 − a m 1 ] \boldsymbol{A_m}=\left[ \begin{matrix} 0 & 1 \\ -a_{m0} & -a_{m1} \end{matrix} \right] Am=[0−am01−am1], b = [ 0 1 ] b=\left[ \begin{matrix} 0 \\ 1 \end{matrix} \right] b=[01], d = [ d 0 d 1 ] d=\left[ \begin{matrix} d_0 \\ d_1 \end{matrix} \right] d=[d0d1]。
要求 h ( s ) h(s) h(s) 是一个严格正实传递函数,则必定存在正定对称矩阵 P P P 和 Q Q Q,使方程式(26)成立:
{ P A m + A m T P = − Q P b = d (26) \left\{ \begin{aligned} &P A_m + A_m^T P = -Q\\ &P b = d \end{aligned} \right. \tag{26} { PAm+AmTP=−QPb=d(26)
由此可解得:
d 0 > 0 , d 1 d 0 > 1 a m 1 = 0.053 (27) d_0 > 0, \quad \frac {d_1} {d_0} > \frac {1} {a_{m_1}} =0.053 \tag{27} d0>0,d0d1>am11=0.053(27)
最终搭建的仿真模型框图如 图1 所示:

具体的 Simulink 仿真文件我已上传至百度网盘中,链接如下:experiment_3.slx_免费高速下载|百度网盘-分享无限制 (baidu.com)
输入信号与广义输出误差信号如 图2 所示:

输入信号与增益信号如 图3 所示:

输入信号与可调参数1的变化曲线如 图4 所示:

输入信号与可调参数2的变化曲线如 图5 所示:

参考书目
李言俊, 张科. 自适应控制理论及应用[M]. 西北工业大学出版社, 2005.
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