Hi,

I already had similar problems. The system of equation is of order 3
which might be too hard. But there is a linear part and if this is
solved first and then plugged into the remaining 2 equations, sympy can
manage it.

I documented my attempt here:

https://gist.github.com/cknoll/c03dcf8443c0409d37da


(Generally, I think sympy.solve could sometimes be a little bit smarter,
to use such structural properties.)



On 02/23/2016 06:43 AM, Carl Sandrock wrote:
> I am attempting to work a problem from a textbook in sympy, but sympy
> fails to find a solution which appears valid. For interest, it is the
> design of a PID controller using direct synthesis with a second order
> plus dead time model.
> 
> The whole problem can be reduced to finding K_C, tau_I and tau_D which
> will make
> 
> K_C*(s**2*tau_D*tau_I + s*tau_I + 1)/(s*tau_I) = (s**2*tau_1*tau_2 +
> s*tau_1 + s*tau_2 + 1)/(K*s*(-phi + tau_c))
> 
> 
> for given tau_1, tau_2, K and phi.
> 
> 
> I have tried to solve this by matching coefficients:
> 
> 
> import sympy
> 
> 
> s, tau_c, tau_1, tau_2, phi, K = sympy.symbols('s, tau_c, tau_1, tau_2,
> phi, K')
> 
> target = (s**2*tau_1*tau_2 + s*tau_1 + s*tau_2 + 1)/(K*s*(-phi + tau_c))
> 
> K_C, tau_I, tau_D = sympy.symbols('K_C, tau_I, tau_D', real=True)
> PID = K_C*(1 + 1/(tau_I*s) + tau_D*s)
> 
> eq = (target - PID).together()
> eq *= sympy.denom(eq).simplify()
> eq = sympy.poly(eq, s)
> 
> sympy.solve(eq.coeffs(), [K_C, tau_I, tau_D])
> 
> This returns an empty matrix. However, the textbook provides the
> following solution:
> 
> booksolution = {K_C: 1/K*(tau_1 + tau_2)/(tau_c - phi),
>                 tau_I: tau_1 + tau_2,a
>                 tau_D: tau_1*tau_2/(tau_1 + tau_2)}
> 
> Which appears to satisfy the equations I'm trying to solve: 
> 
> [c.subs(booksolution).simplify() for c in eq.coeffs()]
> 
> returns
> 
> [0, 0, 0]
> 
> Can I massage this into a form which sympy can solve? What am I doing wong?
> 
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