SISTEMAS DE CONTROLE I
A transformada de Laplace da função
f (t) = te-3t + e-2t cos(2t)
é a função F(s) indicada em uma das alternativa
seguintes. Marque a alternativa correta.
`
![1/(s+3)^2+(s+2)/((s+2)^2+2)](/tinyMCE3.4.4/cgi-bin/mimetex.cgi?%5Cdisplaystyle%5Cfrac%7B%7B1%7D%7D%7B%7B%7B%5Cleft(%7Bs%7D%2B%7B3%7D%5Cright)%7D%7D%5E%7B%7B2%7D%7D%7D%2B%5Cfrac%7B%7B%7Bs%7D%2B%7B2%7D%7D%7D%7B%7B%7B%7B%5Cleft(%7Bs%7D%2B%7B2%7D%5Cright)%7D%7D%5E%7B%7B2%7D%7D%2B%7B2%7D%7D%7D)
![1/(s+3)+(s+2)/((s+2)^2+4](/tinyMCE3.4.4/cgi-bin/mimetex.cgi?%5Cdisplaystyle%5Cfrac%7B%7B1%7D%7D%7B%7B%7Bs%7D%2B%7B3%7D%7D%7D%2B%5Cfrac%7B%7B%7Bs%7D%2B%7B2%7D%7D%7D%7B%7B%7B%7B%5Cleft(%7Bs%7D%2B%7B2%7D%5Cright)%7D%7D%5E%7B%7B2%7D%7D%2B%7B4%7D%7D%7D)
![2/(s+32)^2+s/((s+2)^2+4)](/tinyMCE3.4.4/cgi-bin/mimetex.cgi?%5Cdisplaystyle%5Cfrac%7B%7B2%7D%7D%7B%7B%7B%5Cleft(%7Bs%7D%2B%7B32%7D%5Cright)%7D%7D%5E%7B%7B2%7D%7D%7D%2B%5Cfrac%7B%7Bs%7D%7D%7B%7B%7B%7B%5Cleft(%7Bs%7D%2B%7B2%7D%5Cright)%7D%7D%5E%7B%7B2%7D%7D%2B%7B4%7D%7D%7D)
`
Deseja-se controlar o sistema abaixo por malha fechada, dessa forma, qual será a variável manipulada (MV) e a variável controlada (PV).
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)
PV : qi(t) e MV : h(t).
PV : qo(t) e MV : qi(t).
PV : h(t) e MV : qo(t).
PV : qi(t) e MV : qo(t).
PV : h(t) e MV : qi(t).
Nas equações apresentas abaixo, c(t) representa possíveis respostas de sistemas de segunda ordem, sujeitos a entrada do tipo degrau unitário.
![eq_ord_2 eq_ord_2](http://sga.uniube.br/images/uploads/5061/equacao_resposta_2a_ordem.png)
As equações (1), (2) e (3) correspondem, respectivamente, a respostas de sistemas:
Criticamente amortecido, sobreamortecido e subamortecido.
Criticamente amortecido, subamortecido e sobreamortecido.
Sobreamortecido, criticamente amortecido e subamortecido.
Subamortecido, sobreamortecido e criticamente amortecido.
Sobreamortecido, subamortecido e criticamente amortecido.
Considere o lugar das raízes de um sistema de controle com realimentação unitária, e controlador com ganho variável G dado na figura abaixo.
![lr1 lr1](http://sga.uniube.br/images/uploads/5061/lugar_raizes_3.png)
A partir da análise da informações contidas no gráfico, avalie a veracidade das afirmativas a seguir:
I - A equação caraterística do sistema é 1 + G. (s+1)/(s2 -3.s)=0
II - A Função de transferência de malha fechada do sistema de controle é: G(s+1)/(s2 + (G-3).s + G)
III - O lugar das raízes começa nos pólos s=0 e s=3, e terminos zeros s=-1 e s=-∞
IV - Dependendo do valor do parâmetro G, o sistema poderá ser estável ou instável
V - O sistema poderá responder de forma oscilatória ou monótona, dependendo do valor de G
São verdadeiras as afirmativas:
I, II, III, IV e V
II, III e V
II, III e IV
I, III, IV e V
II, III, IV e V
A figura abaixo apresenta o diagrama de um sistema de controle manual de um processo de aquecimento de água em um trocador de calor, usando vapor superaquecido como fonte de energia térmica.
![png sistema de controle manual](http://sga.uniube.br/images/uploads/5061/FIG_TROCADOR_CALOR.png)
Nos quadros numerados cabem os termos técnicos: processo, sistema de controle, sensor, controlador e atuador.
Nessa ordem, a numeração dos blocos será:
1 - processo; 2 - atuador ; 3 - sensor ; 4 - controlador ; 5 - sistema de controle
1 - processo; 2 - atuador ; 3 - sensor ; 4 - sistema de controle; 5 - controlador
1 - sistema de controle; 2 - processo; 3 - atuador ; 4 - sensor ; 5 - controlador
1 - processo; 2 - atuador ; 3 - sistema de controle; 4 - sensor; 5 - controlador
1 - processo; 2 - sistema de controle; 3 - atuador; 4 - sensor; 5 - controlador
Apresenta-se a seguir uma tabela com pares de transformadas de Laplace de algumas funções.
Porém, em uma mesma linha, pode ser que F(s) não seja a transformada de Laplace de f(t).
Na coluna A foi atribuída uma numeração das funções f(t).
Enumere a coluna B com o número da função f(t), de forma a haver a correta correspondência entre f(t) e F(s),
ou seja, que f(t) e F(s) correspondentes tenham a mesma numeração.
![TAB1 TAB1](http://sga.uniube.br/images/uploads/5061/TAB1.png)
A coluna B, na forma transposta, terá a seguinte sequência numérica:
1 4 3 2 5 6 8 7 9
1 3 4 2 6 5 8 7 9
1 3 4 2 6 5 7 8 9
1 3 4 2 5 6 7 8 9
1 3 4 5 2 6 8 7 9
Na figura abaixo apresenta-se os diagramas elétrico e de blocos de um sistema físico.
![blocrlc bloc_rlc](http://sga.uniube.br/images/uploads/5061/circuito_rlc_blocos.png)
As variáveis V1, V2, V3 e V4 correspondem, respectivamente às variáveis elétricas:
VC ; VR - VL ; I ; Vi
Vi ; VR ; VC ; VL
VL ; Vi - Vc ; I ; VC
Vi ; VR+VL ; I ; VC
Vi ; VC ; VL ; VR
Um sistema de controle de posição de braço de robô tem como variável de entrada tensão u(t), dada em volts, e pode ter duas variáveis de saída: velocidade v(t) dada em metros por segundo, e o deslocamento y(t) dado em metros.
O comportamento dinâmico do sistema é dado pela equação diferencial:
![edo2 edo2](http://sga.uniube.br/images/uploads/5061/edo_ord_2.png)
Sendo k=1, a=1, b=3 e c=2.
Considerando o sinal de excitação u(t) do tipo degrau unitário, a resposta y(t) será:
![1/2-e^(-3t)+1/2.e^(-2t)](http://sga.uniube.br/tinyMCE3.4.4/cgi-bin/mimetex.cgi?%5Cdisplaystyle%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D-%7B%7Be%7D%7D%5E%7B%7B-%7B3%7D%7Bt%7D%7D%7D%2B%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D.%7B%7Be%7D%7D%5E%7B%7B-%7B2%7D%7Bt%7D%7D%7D)
![1/2-e^(t)+1/2.e^(2t](http://sga.uniube.br/tinyMCE3.4.4/cgi-bin/mimetex.cgi?%5Cdisplaystyle%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D-%7B%7Be%7D%7D%5E%7B%7B%7Bt%7D%7D%7D%2B%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D.%7B%7Be%7D%7D%5E%7B%7B%7B2%7D%7Bt%7D%7D%7D)
![1/2-e^(-t)+1/2.e^(-2t](http://sga.uniube.br/tinyMCE3.4.4/cgi-bin/mimetex.cgi?%5Cdisplaystyle%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D-%7B%7Be%7D%7D%5E%7B%7B-%7Bt%7D%7D%7D%2B%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D.%7B%7Be%7D%7D%5E%7B%7B-%7B2%7D%7Bt%7D%7D%7D)
Nas figuras a seguir são apresentados respectivamente o gráfico do lugar das raízes de um sistema de controle, e respostas desse mesmo sistema para entrada degrau unitário, para 4 valores distintos do parâmetro ajustável K do controlador.
![lr5](http://sga.uniube.br/images/uploads/5061/lugar_raizes_2.png)
Considerando que o sistema tem realimentação unitária, e que o controlador é do tipo proporcional, avalie a veracidade das afirmativas a seguir:
I - Nas respostas oscilatórias foram utilizados valores de K maiores que nas repostas exponenciais
II - O menor valor de K foi utilizado na reposta de maior overshoot
III - O valor de K aumenta da curva de maior para a de menor overshoot
IV - O maior valor de K foi utilizado na curva de maior tempo de acomodação;
V - As respostas exponenciais (não-oscilatórias) foram utilizados valores de K que tornam os pólos do sistema reais.
São corretas as afirmativas:
Deseja-se controlar o sistema abaixo por malha fechada, dessa forma, qual será a variável manipulada (MV) e a variável controlada (PV).
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)
PV : qi(t) e MV : h(t).
PV : qo(t) e MV : qi(t).
PV : h(t) e MV : qo(t).
PV : qi(t) e MV : qo(t).
PV : h(t) e MV : qi(t).
Nas equações apresentas abaixo, c(t) representa possíveis respostas de sistemas de segunda ordem, sujeitos a entrada do tipo degrau unitário.
![eq_ord_2 eq_ord_2](http://sga.uniube.br/images/uploads/5061/equacao_resposta_2a_ordem.png)
As equações (1), (2) e (3) correspondem, respectivamente, a respostas de sistemas:
Criticamente amortecido, sobreamortecido e subamortecido.
Criticamente amortecido, subamortecido e sobreamortecido.
Sobreamortecido, criticamente amortecido e subamortecido.
Subamortecido, sobreamortecido e criticamente amortecido.
Sobreamortecido, subamortecido e criticamente amortecido.
Considere o lugar das raízes de um sistema de controle com realimentação unitária, e controlador com ganho variável G dado na figura abaixo.
![lr1 lr1](http://sga.uniube.br/images/uploads/5061/lugar_raizes_3.png)
A partir da análise da informações contidas no gráfico, avalie a veracidade das afirmativas a seguir:
I - A equação caraterística do sistema é 1 + G. (s+1)/(s2 -3.s)=0
II - A Função de transferência de malha fechada do sistema de controle é: G(s+1)/(s2 + (G-3).s + G)
III - O lugar das raízes começa nos pólos s=0 e s=3, e terminos zeros s=-1 e s=-∞
IV - Dependendo do valor do parâmetro G, o sistema poderá ser estável ou instável
V - O sistema poderá responder de forma oscilatória ou monótona, dependendo do valor de G
São verdadeiras as afirmativas:
I, II, III, IV e V
II, III e V
II, III e IV
I, III, IV e V
II, III, IV e V
A figura abaixo apresenta o diagrama de um sistema de controle manual de um processo de aquecimento de água em um trocador de calor, usando vapor superaquecido como fonte de energia térmica.
![png sistema de controle manual](http://sga.uniube.br/images/uploads/5061/FIG_TROCADOR_CALOR.png)
Nos quadros numerados cabem os termos técnicos: processo, sistema de controle, sensor, controlador e atuador.
Nessa ordem, a numeração dos blocos será:
1 - processo; 2 - atuador ; 3 - sensor ; 4 - controlador ; 5 - sistema de controle
1 - processo; 2 - atuador ; 3 - sensor ; 4 - sistema de controle; 5 - controlador
1 - sistema de controle; 2 - processo; 3 - atuador ; 4 - sensor ; 5 - controlador
1 - processo; 2 - atuador ; 3 - sistema de controle; 4 - sensor; 5 - controlador
1 - processo; 2 - sistema de controle; 3 - atuador; 4 - sensor; 5 - controlador
Apresenta-se a seguir uma tabela com pares de transformadas de Laplace de algumas funções.
Porém, em uma mesma linha, pode ser que F(s) não seja a transformada de Laplace de f(t).
Na coluna A foi atribuída uma numeração das funções f(t).
Enumere a coluna B com o número da função f(t), de forma a haver a correta correspondência entre f(t) e F(s),
ou seja, que f(t) e F(s) correspondentes tenham a mesma numeração.
![TAB1 TAB1](http://sga.uniube.br/images/uploads/5061/TAB1.png)
A coluna B, na forma transposta, terá a seguinte sequência numérica:
1 4 3 2 5 6 8 7 9
1 3 4 2 6 5 8 7 9
1 3 4 2 6 5 7 8 9
1 3 4 2 5 6 7 8 9
1 3 4 5 2 6 8 7 9
Na figura abaixo apresenta-se os diagramas elétrico e de blocos de um sistema físico.
![blocrlc bloc_rlc](http://sga.uniube.br/images/uploads/5061/circuito_rlc_blocos.png)
As variáveis V1, V2, V3 e V4 correspondem, respectivamente às variáveis elétricas:
VC ; VR - VL ; I ; Vi
Vi ; VR ; VC ; VL
VL ; Vi - Vc ; I ; VC
Vi ; VR+VL ; I ; VC
Vi ; VC ; VL ; VR
Um sistema de controle de posição de braço de robô tem como variável de entrada tensão u(t), dada em volts, e pode ter duas variáveis de saída: velocidade v(t) dada em metros por segundo, e o deslocamento y(t) dado em metros.
O comportamento dinâmico do sistema é dado pela equação diferencial:
![edo2 edo2](http://sga.uniube.br/images/uploads/5061/edo_ord_2.png)
Sendo k=1, a=1, b=3 e c=2.
Considerando o sinal de excitação u(t) do tipo degrau unitário, a resposta y(t) será:
![1/2-e^(-3t)+1/2.e^(-2t)](http://sga.uniube.br/tinyMCE3.4.4/cgi-bin/mimetex.cgi?%5Cdisplaystyle%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D-%7B%7Be%7D%7D%5E%7B%7B-%7B3%7D%7Bt%7D%7D%7D%2B%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D.%7B%7Be%7D%7D%5E%7B%7B-%7B2%7D%7Bt%7D%7D%7D)
![1/2-e^(t)+1/2.e^(2t](http://sga.uniube.br/tinyMCE3.4.4/cgi-bin/mimetex.cgi?%5Cdisplaystyle%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D-%7B%7Be%7D%7D%5E%7B%7B%7Bt%7D%7D%7D%2B%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D.%7B%7Be%7D%7D%5E%7B%7B%7B2%7D%7Bt%7D%7D%7D)
![1/2-e^(-t)+1/2.e^(-2t](http://sga.uniube.br/tinyMCE3.4.4/cgi-bin/mimetex.cgi?%5Cdisplaystyle%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D-%7B%7Be%7D%7D%5E%7B%7B-%7Bt%7D%7D%7D%2B%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D.%7B%7Be%7D%7D%5E%7B%7B-%7B2%7D%7Bt%7D%7D%7D)
Nas figuras a seguir são apresentados respectivamente o gráfico do lugar das raízes de um sistema de controle, e respostas desse mesmo sistema para entrada degrau unitário, para 4 valores distintos do parâmetro ajustável K do controlador.
![lr5](http://sga.uniube.br/images/uploads/5061/lugar_raizes_2.png)
Considerando que o sistema tem realimentação unitária, e que o controlador é do tipo proporcional, avalie a veracidade das afirmativas a seguir:
I - Nas respostas oscilatórias foram utilizados valores de K maiores que nas repostas exponenciais
II - O menor valor de K foi utilizado na reposta de maior overshoot
III - O valor de K aumenta da curva de maior para a de menor overshoot
IV - O maior valor de K foi utilizado na curva de maior tempo de acomodação;
V - As respostas exponenciais (não-oscilatórias) foram utilizados valores de K que tornam os pólos do sistema reais.
São corretas as afirmativas:
PV : qi(t) e MV : h(t).
PV : qo(t) e MV : qi(t).
PV : h(t) e MV : qo(t).
PV : qi(t) e MV : qo(t).
PV : h(t) e MV : qi(t).
Nas equações apresentas abaixo, c(t) representa possíveis respostas de sistemas de segunda ordem, sujeitos a entrada do tipo degrau unitário.
![eq_ord_2 eq_ord_2](http://sga.uniube.br/images/uploads/5061/equacao_resposta_2a_ordem.png)
As equações (1), (2) e (3) correspondem, respectivamente, a respostas de sistemas:
Criticamente amortecido, sobreamortecido e subamortecido.
Criticamente amortecido, subamortecido e sobreamortecido.
Sobreamortecido, criticamente amortecido e subamortecido.
Subamortecido, sobreamortecido e criticamente amortecido.
Sobreamortecido, subamortecido e criticamente amortecido.
Considere o lugar das raízes de um sistema de controle com realimentação unitária, e controlador com ganho variável G dado na figura abaixo.
![lr1 lr1](http://sga.uniube.br/images/uploads/5061/lugar_raizes_3.png)
A partir da análise da informações contidas no gráfico, avalie a veracidade das afirmativas a seguir:
I - A equação caraterística do sistema é 1 + G. (s+1)/(s2 -3.s)=0
II - A Função de transferência de malha fechada do sistema de controle é: G(s+1)/(s2 + (G-3).s + G)
III - O lugar das raízes começa nos pólos s=0 e s=3, e terminos zeros s=-1 e s=-∞
IV - Dependendo do valor do parâmetro G, o sistema poderá ser estável ou instável
V - O sistema poderá responder de forma oscilatória ou monótona, dependendo do valor de G
São verdadeiras as afirmativas:
I, II, III, IV e V
II, III e V
II, III e IV
I, III, IV e V
II, III, IV e V
A figura abaixo apresenta o diagrama de um sistema de controle manual de um processo de aquecimento de água em um trocador de calor, usando vapor superaquecido como fonte de energia térmica.
![png sistema de controle manual](http://sga.uniube.br/images/uploads/5061/FIG_TROCADOR_CALOR.png)
Nos quadros numerados cabem os termos técnicos: processo, sistema de controle, sensor, controlador e atuador.
Nessa ordem, a numeração dos blocos será:
1 - processo; 2 - atuador ; 3 - sensor ; 4 - controlador ; 5 - sistema de controle
1 - processo; 2 - atuador ; 3 - sensor ; 4 - sistema de controle; 5 - controlador
1 - sistema de controle; 2 - processo; 3 - atuador ; 4 - sensor ; 5 - controlador
1 - processo; 2 - atuador ; 3 - sistema de controle; 4 - sensor; 5 - controlador
1 - processo; 2 - sistema de controle; 3 - atuador; 4 - sensor; 5 - controlador
Apresenta-se a seguir uma tabela com pares de transformadas de Laplace de algumas funções.
Porém, em uma mesma linha, pode ser que F(s) não seja a transformada de Laplace de f(t).
Na coluna A foi atribuída uma numeração das funções f(t).
Enumere a coluna B com o número da função f(t), de forma a haver a correta correspondência entre f(t) e F(s),
ou seja, que f(t) e F(s) correspondentes tenham a mesma numeração.
![TAB1 TAB1](http://sga.uniube.br/images/uploads/5061/TAB1.png)
A coluna B, na forma transposta, terá a seguinte sequência numérica:
1 4 3 2 5 6 8 7 9
1 3 4 2 6 5 8 7 9
1 3 4 2 6 5 7 8 9
1 3 4 2 5 6 7 8 9
1 3 4 5 2 6 8 7 9
Na figura abaixo apresenta-se os diagramas elétrico e de blocos de um sistema físico.
![blocrlc bloc_rlc](http://sga.uniube.br/images/uploads/5061/circuito_rlc_blocos.png)
As variáveis V1, V2, V3 e V4 correspondem, respectivamente às variáveis elétricas:
VC ; VR - VL ; I ; Vi
Vi ; VR ; VC ; VL
VL ; Vi - Vc ; I ; VC
Vi ; VR+VL ; I ; VC
Vi ; VC ; VL ; VR
Um sistema de controle de posição de braço de robô tem como variável de entrada tensão u(t), dada em volts, e pode ter duas variáveis de saída: velocidade v(t) dada em metros por segundo, e o deslocamento y(t) dado em metros.
O comportamento dinâmico do sistema é dado pela equação diferencial:
![edo2 edo2](http://sga.uniube.br/images/uploads/5061/edo_ord_2.png)
Sendo k=1, a=1, b=3 e c=2.
Considerando o sinal de excitação u(t) do tipo degrau unitário, a resposta y(t) será:
![1/2-e^(-3t)+1/2.e^(-2t)](http://sga.uniube.br/tinyMCE3.4.4/cgi-bin/mimetex.cgi?%5Cdisplaystyle%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D-%7B%7Be%7D%7D%5E%7B%7B-%7B3%7D%7Bt%7D%7D%7D%2B%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D.%7B%7Be%7D%7D%5E%7B%7B-%7B2%7D%7Bt%7D%7D%7D)
![1/2-e^(t)+1/2.e^(2t](http://sga.uniube.br/tinyMCE3.4.4/cgi-bin/mimetex.cgi?%5Cdisplaystyle%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D-%7B%7Be%7D%7D%5E%7B%7B%7Bt%7D%7D%7D%2B%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D.%7B%7Be%7D%7D%5E%7B%7B%7B2%7D%7Bt%7D%7D%7D)
![1/2-e^(-t)+1/2.e^(-2t](http://sga.uniube.br/tinyMCE3.4.4/cgi-bin/mimetex.cgi?%5Cdisplaystyle%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D-%7B%7Be%7D%7D%5E%7B%7B-%7Bt%7D%7D%7D%2B%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D.%7B%7Be%7D%7D%5E%7B%7B-%7B2%7D%7Bt%7D%7D%7D)
Nas figuras a seguir são apresentados respectivamente o gráfico do lugar das raízes de um sistema de controle, e respostas desse mesmo sistema para entrada degrau unitário, para 4 valores distintos do parâmetro ajustável K do controlador.
![lr5](http://sga.uniube.br/images/uploads/5061/lugar_raizes_2.png)
Considerando que o sistema tem realimentação unitária, e que o controlador é do tipo proporcional, avalie a veracidade das afirmativas a seguir:
I - Nas respostas oscilatórias foram utilizados valores de K maiores que nas repostas exponenciais
II - O menor valor de K foi utilizado na reposta de maior overshoot
III - O valor de K aumenta da curva de maior para a de menor overshoot
IV - O maior valor de K foi utilizado na curva de maior tempo de acomodação;
V - As respostas exponenciais (não-oscilatórias) foram utilizados valores de K que tornam os pólos do sistema reais.
São corretas as afirmativas:
![eq_ord_2 eq_ord_2](http://sga.uniube.br/images/uploads/5061/equacao_resposta_2a_ordem.png)
Criticamente amortecido, sobreamortecido e subamortecido.
Criticamente amortecido, subamortecido e sobreamortecido.
Sobreamortecido, criticamente amortecido e subamortecido.
Subamortecido, sobreamortecido e criticamente amortecido.
Sobreamortecido, subamortecido e criticamente amortecido.
Considere o lugar das raízes de um sistema de controle com realimentação unitária, e controlador com ganho variável G dado na figura abaixo.
![lr1 lr1](http://sga.uniube.br/images/uploads/5061/lugar_raizes_3.png)
A partir da análise da informações contidas no gráfico, avalie a veracidade das afirmativas a seguir:
I - A equação caraterística do sistema é 1 + G. (s+1)/(s2 -3.s)=0
II - A Função de transferência de malha fechada do sistema de controle é: G(s+1)/(s2 + (G-3).s + G)
III - O lugar das raízes começa nos pólos s=0 e s=3, e terminos zeros s=-1 e s=-∞
IV - Dependendo do valor do parâmetro G, o sistema poderá ser estável ou instável
V - O sistema poderá responder de forma oscilatória ou monótona, dependendo do valor de G
São verdadeiras as afirmativas:
I, II, III, IV e V
II, III e V
II, III e IV
I, III, IV e V
II, III, IV e V
A figura abaixo apresenta o diagrama de um sistema de controle manual de um processo de aquecimento de água em um trocador de calor, usando vapor superaquecido como fonte de energia térmica.
![png sistema de controle manual](http://sga.uniube.br/images/uploads/5061/FIG_TROCADOR_CALOR.png)
Nos quadros numerados cabem os termos técnicos: processo, sistema de controle, sensor, controlador e atuador.
Nessa ordem, a numeração dos blocos será:
1 - processo; 2 - atuador ; 3 - sensor ; 4 - controlador ; 5 - sistema de controle
1 - processo; 2 - atuador ; 3 - sensor ; 4 - sistema de controle; 5 - controlador
1 - sistema de controle; 2 - processo; 3 - atuador ; 4 - sensor ; 5 - controlador
1 - processo; 2 - atuador ; 3 - sistema de controle; 4 - sensor; 5 - controlador
1 - processo; 2 - sistema de controle; 3 - atuador; 4 - sensor; 5 - controlador
Apresenta-se a seguir uma tabela com pares de transformadas de Laplace de algumas funções.
Porém, em uma mesma linha, pode ser que F(s) não seja a transformada de Laplace de f(t).
Na coluna A foi atribuída uma numeração das funções f(t).
Enumere a coluna B com o número da função f(t), de forma a haver a correta correspondência entre f(t) e F(s),
ou seja, que f(t) e F(s) correspondentes tenham a mesma numeração.
![TAB1 TAB1](http://sga.uniube.br/images/uploads/5061/TAB1.png)
A coluna B, na forma transposta, terá a seguinte sequência numérica:
1 4 3 2 5 6 8 7 9
1 3 4 2 6 5 8 7 9
1 3 4 2 6 5 7 8 9
1 3 4 2 5 6 7 8 9
1 3 4 5 2 6 8 7 9
Na figura abaixo apresenta-se os diagramas elétrico e de blocos de um sistema físico.
![blocrlc bloc_rlc](http://sga.uniube.br/images/uploads/5061/circuito_rlc_blocos.png)
As variáveis V1, V2, V3 e V4 correspondem, respectivamente às variáveis elétricas:
VC ; VR - VL ; I ; Vi
Vi ; VR ; VC ; VL
VL ; Vi - Vc ; I ; VC
Vi ; VR+VL ; I ; VC
Vi ; VC ; VL ; VR
Um sistema de controle de posição de braço de robô tem como variável de entrada tensão u(t), dada em volts, e pode ter duas variáveis de saída: velocidade v(t) dada em metros por segundo, e o deslocamento y(t) dado em metros.
O comportamento dinâmico do sistema é dado pela equação diferencial:
![edo2 edo2](http://sga.uniube.br/images/uploads/5061/edo_ord_2.png)
Sendo k=1, a=1, b=3 e c=2.
Considerando o sinal de excitação u(t) do tipo degrau unitário, a resposta y(t) será:
![1/2-e^(-3t)+1/2.e^(-2t)](http://sga.uniube.br/tinyMCE3.4.4/cgi-bin/mimetex.cgi?%5Cdisplaystyle%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D-%7B%7Be%7D%7D%5E%7B%7B-%7B3%7D%7Bt%7D%7D%7D%2B%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D.%7B%7Be%7D%7D%5E%7B%7B-%7B2%7D%7Bt%7D%7D%7D)
![1/2-e^(t)+1/2.e^(2t](http://sga.uniube.br/tinyMCE3.4.4/cgi-bin/mimetex.cgi?%5Cdisplaystyle%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D-%7B%7Be%7D%7D%5E%7B%7B%7Bt%7D%7D%7D%2B%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D.%7B%7Be%7D%7D%5E%7B%7B%7B2%7D%7Bt%7D%7D%7D)
![1/2-e^(-t)+1/2.e^(-2t](http://sga.uniube.br/tinyMCE3.4.4/cgi-bin/mimetex.cgi?%5Cdisplaystyle%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D-%7B%7Be%7D%7D%5E%7B%7B-%7Bt%7D%7D%7D%2B%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D.%7B%7Be%7D%7D%5E%7B%7B-%7B2%7D%7Bt%7D%7D%7D)
Nas figuras a seguir são apresentados respectivamente o gráfico do lugar das raízes de um sistema de controle, e respostas desse mesmo sistema para entrada degrau unitário, para 4 valores distintos do parâmetro ajustável K do controlador.
![lr5](http://sga.uniube.br/images/uploads/5061/lugar_raizes_2.png)
Considerando que o sistema tem realimentação unitária, e que o controlador é do tipo proporcional, avalie a veracidade das afirmativas a seguir:
I - Nas respostas oscilatórias foram utilizados valores de K maiores que nas repostas exponenciais
II - O menor valor de K foi utilizado na reposta de maior overshoot
III - O valor de K aumenta da curva de maior para a de menor overshoot
IV - O maior valor de K foi utilizado na curva de maior tempo de acomodação;
V - As respostas exponenciais (não-oscilatórias) foram utilizados valores de K que tornam os pólos do sistema reais.
São corretas as afirmativas:
![lr1 lr1](http://sga.uniube.br/images/uploads/5061/lugar_raizes_3.png)
I, II, III, IV e V
II, III e V
II, III e IV
I, III, IV e V
II, III, IV e V
A figura abaixo apresenta o diagrama de um sistema de controle manual de um processo de aquecimento de água em um trocador de calor, usando vapor superaquecido como fonte de energia térmica.
![png sistema de controle manual](http://sga.uniube.br/images/uploads/5061/FIG_TROCADOR_CALOR.png)
Nos quadros numerados cabem os termos técnicos: processo, sistema de controle, sensor, controlador e atuador.
Nessa ordem, a numeração dos blocos será:
1 - processo; 2 - atuador ; 3 - sensor ; 4 - controlador ; 5 - sistema de controle
1 - processo; 2 - atuador ; 3 - sensor ; 4 - sistema de controle; 5 - controlador
1 - sistema de controle; 2 - processo; 3 - atuador ; 4 - sensor ; 5 - controlador
1 - processo; 2 - atuador ; 3 - sistema de controle; 4 - sensor; 5 - controlador
1 - processo; 2 - sistema de controle; 3 - atuador; 4 - sensor; 5 - controlador
Apresenta-se a seguir uma tabela com pares de transformadas de Laplace de algumas funções.
Porém, em uma mesma linha, pode ser que F(s) não seja a transformada de Laplace de f(t).
Na coluna A foi atribuída uma numeração das funções f(t).
Enumere a coluna B com o número da função f(t), de forma a haver a correta correspondência entre f(t) e F(s),
ou seja, que f(t) e F(s) correspondentes tenham a mesma numeração.
![TAB1 TAB1](http://sga.uniube.br/images/uploads/5061/TAB1.png)
A coluna B, na forma transposta, terá a seguinte sequência numérica:
1 4 3 2 5 6 8 7 9
1 3 4 2 6 5 8 7 9
1 3 4 2 6 5 7 8 9
1 3 4 2 5 6 7 8 9
1 3 4 5 2 6 8 7 9
Na figura abaixo apresenta-se os diagramas elétrico e de blocos de um sistema físico.
![blocrlc bloc_rlc](http://sga.uniube.br/images/uploads/5061/circuito_rlc_blocos.png)
As variáveis V1, V2, V3 e V4 correspondem, respectivamente às variáveis elétricas:
VC ; VR - VL ; I ; Vi
Vi ; VR ; VC ; VL
VL ; Vi - Vc ; I ; VC
Vi ; VR+VL ; I ; VC
Vi ; VC ; VL ; VR
Um sistema de controle de posição de braço de robô tem como variável de entrada tensão u(t), dada em volts, e pode ter duas variáveis de saída: velocidade v(t) dada em metros por segundo, e o deslocamento y(t) dado em metros.
O comportamento dinâmico do sistema é dado pela equação diferencial:
![edo2 edo2](http://sga.uniube.br/images/uploads/5061/edo_ord_2.png)
Sendo k=1, a=1, b=3 e c=2.
Considerando o sinal de excitação u(t) do tipo degrau unitário, a resposta y(t) será:
![1/2-e^(-3t)+1/2.e^(-2t)](http://sga.uniube.br/tinyMCE3.4.4/cgi-bin/mimetex.cgi?%5Cdisplaystyle%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D-%7B%7Be%7D%7D%5E%7B%7B-%7B3%7D%7Bt%7D%7D%7D%2B%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D.%7B%7Be%7D%7D%5E%7B%7B-%7B2%7D%7Bt%7D%7D%7D)
![1/2-e^(t)+1/2.e^(2t](http://sga.uniube.br/tinyMCE3.4.4/cgi-bin/mimetex.cgi?%5Cdisplaystyle%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D-%7B%7Be%7D%7D%5E%7B%7B%7Bt%7D%7D%7D%2B%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D.%7B%7Be%7D%7D%5E%7B%7B%7B2%7D%7Bt%7D%7D%7D)
![1/2-e^(-t)+1/2.e^(-2t](http://sga.uniube.br/tinyMCE3.4.4/cgi-bin/mimetex.cgi?%5Cdisplaystyle%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D-%7B%7Be%7D%7D%5E%7B%7B-%7Bt%7D%7D%7D%2B%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D.%7B%7Be%7D%7D%5E%7B%7B-%7B2%7D%7Bt%7D%7D%7D)
Nas figuras a seguir são apresentados respectivamente o gráfico do lugar das raízes de um sistema de controle, e respostas desse mesmo sistema para entrada degrau unitário, para 4 valores distintos do parâmetro ajustável K do controlador.
![lr5](http://sga.uniube.br/images/uploads/5061/lugar_raizes_2.png)
Considerando que o sistema tem realimentação unitária, e que o controlador é do tipo proporcional, avalie a veracidade das afirmativas a seguir:
I - Nas respostas oscilatórias foram utilizados valores de K maiores que nas repostas exponenciais
II - O menor valor de K foi utilizado na reposta de maior overshoot
III - O valor de K aumenta da curva de maior para a de menor overshoot
IV - O maior valor de K foi utilizado na curva de maior tempo de acomodação;
V - As respostas exponenciais (não-oscilatórias) foram utilizados valores de K que tornam os pólos do sistema reais.
São corretas as afirmativas:
![png sistema de controle manual](http://sga.uniube.br/images/uploads/5061/FIG_TROCADOR_CALOR.png)
1 - processo; 2 - atuador ; 3 - sensor ; 4 - controlador ; 5 - sistema de controle
1 - processo; 2 - atuador ; 3 - sensor ; 4 - sistema de controle; 5 - controlador
1 - sistema de controle; 2 - processo; 3 - atuador ; 4 - sensor ; 5 - controlador
1 - processo; 2 - atuador ; 3 - sistema de controle; 4 - sensor; 5 - controlador
1 - processo; 2 - sistema de controle; 3 - atuador; 4 - sensor; 5 - controlador
Apresenta-se a seguir uma tabela com pares de transformadas de Laplace de algumas funções.
Porém, em uma mesma linha, pode ser que F(s) não seja a transformada de Laplace de f(t).
Na coluna A foi atribuída uma numeração das funções f(t).
Enumere a coluna B com o número da função f(t), de forma a haver a correta correspondência entre f(t) e F(s),
ou seja, que f(t) e F(s) correspondentes tenham a mesma numeração.
![TAB1 TAB1](http://sga.uniube.br/images/uploads/5061/TAB1.png)
A coluna B, na forma transposta, terá a seguinte sequência numérica:
1 4 3 2 5 6 8 7 9
1 3 4 2 6 5 8 7 9
1 3 4 2 6 5 7 8 9
1 3 4 2 5 6 7 8 9
1 3 4 5 2 6 8 7 9
Na figura abaixo apresenta-se os diagramas elétrico e de blocos de um sistema físico.
![blocrlc bloc_rlc](http://sga.uniube.br/images/uploads/5061/circuito_rlc_blocos.png)
As variáveis V1, V2, V3 e V4 correspondem, respectivamente às variáveis elétricas:
VC ; VR - VL ; I ; Vi
Vi ; VR ; VC ; VL
VL ; Vi - Vc ; I ; VC
Vi ; VR+VL ; I ; VC
Vi ; VC ; VL ; VR
Um sistema de controle de posição de braço de robô tem como variável de entrada tensão u(t), dada em volts, e pode ter duas variáveis de saída: velocidade v(t) dada em metros por segundo, e o deslocamento y(t) dado em metros.
O comportamento dinâmico do sistema é dado pela equação diferencial:
![edo2 edo2](http://sga.uniube.br/images/uploads/5061/edo_ord_2.png)
Sendo k=1, a=1, b=3 e c=2.
Considerando o sinal de excitação u(t) do tipo degrau unitário, a resposta y(t) será:
![1/2-e^(-3t)+1/2.e^(-2t)](http://sga.uniube.br/tinyMCE3.4.4/cgi-bin/mimetex.cgi?%5Cdisplaystyle%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D-%7B%7Be%7D%7D%5E%7B%7B-%7B3%7D%7Bt%7D%7D%7D%2B%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D.%7B%7Be%7D%7D%5E%7B%7B-%7B2%7D%7Bt%7D%7D%7D)
![1/2-e^(t)+1/2.e^(2t](http://sga.uniube.br/tinyMCE3.4.4/cgi-bin/mimetex.cgi?%5Cdisplaystyle%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D-%7B%7Be%7D%7D%5E%7B%7B%7Bt%7D%7D%7D%2B%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D.%7B%7Be%7D%7D%5E%7B%7B%7B2%7D%7Bt%7D%7D%7D)
![1/2-e^(-t)+1/2.e^(-2t](http://sga.uniube.br/tinyMCE3.4.4/cgi-bin/mimetex.cgi?%5Cdisplaystyle%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D-%7B%7Be%7D%7D%5E%7B%7B-%7Bt%7D%7D%7D%2B%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D.%7B%7Be%7D%7D%5E%7B%7B-%7B2%7D%7Bt%7D%7D%7D)
Nas figuras a seguir são apresentados respectivamente o gráfico do lugar das raízes de um sistema de controle, e respostas desse mesmo sistema para entrada degrau unitário, para 4 valores distintos do parâmetro ajustável K do controlador.
![lr5](http://sga.uniube.br/images/uploads/5061/lugar_raizes_2.png)
Considerando que o sistema tem realimentação unitária, e que o controlador é do tipo proporcional, avalie a veracidade das afirmativas a seguir:
I - Nas respostas oscilatórias foram utilizados valores de K maiores que nas repostas exponenciais
II - O menor valor de K foi utilizado na reposta de maior overshoot
III - O valor de K aumenta da curva de maior para a de menor overshoot
IV - O maior valor de K foi utilizado na curva de maior tempo de acomodação;
V - As respostas exponenciais (não-oscilatórias) foram utilizados valores de K que tornam os pólos do sistema reais.
São corretas as afirmativas:
![TAB1 TAB1](http://sga.uniube.br/images/uploads/5061/TAB1.png)
1 4 3 2 5 6 8 7 9
1 3 4 2 6 5 8 7 9
1 3 4 2 6 5 7 8 9
1 3 4 2 5 6 7 8 9
1 3 4 5 2 6 8 7 9
Na figura abaixo apresenta-se os diagramas elétrico e de blocos de um sistema físico.
![blocrlc bloc_rlc](http://sga.uniube.br/images/uploads/5061/circuito_rlc_blocos.png)
As variáveis V1, V2, V3 e V4 correspondem, respectivamente às variáveis elétricas:
VC ; VR - VL ; I ; Vi
Vi ; VR ; VC ; VL
VL ; Vi - Vc ; I ; VC
Vi ; VR+VL ; I ; VC
Vi ; VC ; VL ; VR
Um sistema de controle de posição de braço de robô tem como variável de entrada tensão u(t), dada em volts, e pode ter duas variáveis de saída: velocidade v(t) dada em metros por segundo, e o deslocamento y(t) dado em metros.
O comportamento dinâmico do sistema é dado pela equação diferencial:
![edo2 edo2](http://sga.uniube.br/images/uploads/5061/edo_ord_2.png)
Sendo k=1, a=1, b=3 e c=2.
Considerando o sinal de excitação u(t) do tipo degrau unitário, a resposta y(t) será:
![1/2-e^(-3t)+1/2.e^(-2t)](http://sga.uniube.br/tinyMCE3.4.4/cgi-bin/mimetex.cgi?%5Cdisplaystyle%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D-%7B%7Be%7D%7D%5E%7B%7B-%7B3%7D%7Bt%7D%7D%7D%2B%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D.%7B%7Be%7D%7D%5E%7B%7B-%7B2%7D%7Bt%7D%7D%7D)
![1/2-e^(t)+1/2.e^(2t](http://sga.uniube.br/tinyMCE3.4.4/cgi-bin/mimetex.cgi?%5Cdisplaystyle%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D-%7B%7Be%7D%7D%5E%7B%7B%7Bt%7D%7D%7D%2B%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D.%7B%7Be%7D%7D%5E%7B%7B%7B2%7D%7Bt%7D%7D%7D)
![1/2-e^(-t)+1/2.e^(-2t](http://sga.uniube.br/tinyMCE3.4.4/cgi-bin/mimetex.cgi?%5Cdisplaystyle%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D-%7B%7Be%7D%7D%5E%7B%7B-%7Bt%7D%7D%7D%2B%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D.%7B%7Be%7D%7D%5E%7B%7B-%7B2%7D%7Bt%7D%7D%7D)
Nas figuras a seguir são apresentados respectivamente o gráfico do lugar das raízes de um sistema de controle, e respostas desse mesmo sistema para entrada degrau unitário, para 4 valores distintos do parâmetro ajustável K do controlador.
![lr5](http://sga.uniube.br/images/uploads/5061/lugar_raizes_2.png)
Considerando que o sistema tem realimentação unitária, e que o controlador é do tipo proporcional, avalie a veracidade das afirmativas a seguir:
I - Nas respostas oscilatórias foram utilizados valores de K maiores que nas repostas exponenciais
II - O menor valor de K foi utilizado na reposta de maior overshoot
III - O valor de K aumenta da curva de maior para a de menor overshoot
IV - O maior valor de K foi utilizado na curva de maior tempo de acomodação;
V - As respostas exponenciais (não-oscilatórias) foram utilizados valores de K que tornam os pólos do sistema reais.
São corretas as afirmativas:
![blocrlc bloc_rlc](http://sga.uniube.br/images/uploads/5061/circuito_rlc_blocos.png)
VC ; VR - VL ; I ; Vi
Vi ; VR ; VC ; VL
VL ; Vi - Vc ; I ; VC
Vi ; VR+VL ; I ; VC
Vi ; VC ; VL ; VR
Um sistema de controle de posição de braço de robô tem como variável de entrada tensão u(t), dada em volts, e pode ter duas variáveis de saída: velocidade v(t) dada em metros por segundo, e o deslocamento y(t) dado em metros.
O comportamento dinâmico do sistema é dado pela equação diferencial:
![edo2 edo2](http://sga.uniube.br/images/uploads/5061/edo_ord_2.png)
Sendo k=1, a=1, b=3 e c=2.
Considerando o sinal de excitação u(t) do tipo degrau unitário, a resposta y(t) será:
![1/2-e^(-3t)+1/2.e^(-2t)](http://sga.uniube.br/tinyMCE3.4.4/cgi-bin/mimetex.cgi?%5Cdisplaystyle%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D-%7B%7Be%7D%7D%5E%7B%7B-%7B3%7D%7Bt%7D%7D%7D%2B%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D.%7B%7Be%7D%7D%5E%7B%7B-%7B2%7D%7Bt%7D%7D%7D)
![1/2-e^(t)+1/2.e^(2t](http://sga.uniube.br/tinyMCE3.4.4/cgi-bin/mimetex.cgi?%5Cdisplaystyle%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D-%7B%7Be%7D%7D%5E%7B%7B%7Bt%7D%7D%7D%2B%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D.%7B%7Be%7D%7D%5E%7B%7B%7B2%7D%7Bt%7D%7D%7D)
![1/2-e^(-t)+1/2.e^(-2t](http://sga.uniube.br/tinyMCE3.4.4/cgi-bin/mimetex.cgi?%5Cdisplaystyle%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D-%7B%7Be%7D%7D%5E%7B%7B-%7Bt%7D%7D%7D%2B%5Cfrac%7B%7B1%7D%7D%7B%7B2%7D%7D.%7B%7Be%7D%7D%5E%7B%7B-%7B2%7D%7Bt%7D%7D%7D)
Nas figuras a seguir são apresentados respectivamente o gráfico do lugar das raízes de um sistema de controle, e respostas desse mesmo sistema para entrada degrau unitário, para 4 valores distintos do parâmetro ajustável K do controlador.
![lr5](http://sga.uniube.br/images/uploads/5061/lugar_raizes_2.png)
Considerando que o sistema tem realimentação unitária, e que o controlador é do tipo proporcional, avalie a veracidade das afirmativas a seguir:
I - Nas respostas oscilatórias foram utilizados valores de K maiores que nas repostas exponenciais
II - O menor valor de K foi utilizado na reposta de maior overshoot
III - O valor de K aumenta da curva de maior para a de menor overshoot
IV - O maior valor de K foi utilizado na curva de maior tempo de acomodação;
V - As respostas exponenciais (não-oscilatórias) foram utilizados valores de K que tornam os pólos do sistema reais.
São corretas as afirmativas:
![edo2 edo2](http://sga.uniube.br/images/uploads/5061/edo_ord_2.png)
Nas figuras a seguir são apresentados respectivamente o gráfico do lugar das raízes de um sistema de controle, e respostas desse mesmo sistema para entrada degrau unitário, para 4 valores distintos do parâmetro ajustável K do controlador.
![lr5](http://sga.uniube.br/images/uploads/5061/lugar_raizes_2.png)
Considerando que o sistema tem realimentação unitária, e que o controlador é do tipo proporcional, avalie a veracidade das afirmativas a seguir:
I - Nas respostas oscilatórias foram utilizados valores de K maiores que nas repostas exponenciais
II - O menor valor de K foi utilizado na reposta de maior overshoot
III - O valor de K aumenta da curva de maior para a de menor overshoot
IV - O maior valor de K foi utilizado na curva de maior tempo de acomodação;
V - As respostas exponenciais (não-oscilatórias) foram utilizados valores de K que tornam os pólos do sistema reais.
São corretas as afirmativas:
![lr5](http://sga.uniube.br/images/uploads/5061/lugar_raizes_2.png)