Step info improve jpp #567
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1) System Type 1 - Step response not stationary: G(s)=1/s(s+1) 2) SISO system with under shoot response and positive final value G(s)=(-s+1)/(s²+s+1) 3) Same system that 2) with k=-1 4) example from matlab online help https://www.mathworks.com/help/control/ref/stepinfo.html G(s)=(s²+5s+5)/(s^4+1.65s^3+6.5s+2) with stepinfo output: RiseTime: 3.8456 SettlingTime: 27.9762 SettlingMin: 2.0689 SettlingMax: 2.6873 Overshoot: 7.4915 Undershoot: 0 Peak: 2.6873 PeakTime: 8.0530 5) example from matlab online help https://www.mathworks.com/help/control/ref/stepinfo.html A = [0.68 -0.34; 0.34 0.68]; B = [0.18 -0.05; 0.04 0.11]; C = [0 -1.53; -1.12 -1.10]; D = [0 0; 0.06 -0.37]; sys = StateSpace(A,B,C,D,0.2); examine the response characteristics for the response from the first input to the second output of sys. with stepinfo output: RiseTime: 0.4000 SettlingTime: 2.8000 SettlingMin: -0.6724 SettlingMax: -0.5188 Overshoot: 24.6476 Undershoot: 11.1224 Peak: 0.6724 PeakTime: 1
bnavigator
reviewed
Mar 12, 2021
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| rise_time: float = np.NaN | ||
| settling_time: float = np.NaN | ||
| settling_min: float = np.NaN | ||
| settling_max: float = np.NaN | ||
| peak_value: float = np.Inf | ||
| peak_time: float = np.Inf | ||
| undershoot: float = np.NaN | ||
| overshoot: float = np.NaN |
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The first use of typing in python-control?
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Not at all! Good thing we already ditched Python 2.
control/timeresp.py
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| if InfValue < 0.0: | ||
| # RiseTime | ||
| tr_lower_index = (np.where(yout <= RiseTimeLimits[0] * InfValue)[0])[0] | ||
| tr_upper_index = (np.where(yout <= RiseTimeLimits[1] * InfValue)[0])[0] | ||
| # SettlingTime | ||
| for i in reversed(range(T.size - 1)): | ||
| if (-yout[i] <= np.abs(inf_margin)) | (-yout[i] >= np.abs(sup_margin)): | ||
| settling_time = T[i + 1] | ||
| break | ||
| # Overshoot and Undershoot | ||
| overshoot = np.abs(100. * ((-yout).max() - np.abs(InfValue)) / np.abs(InfValue)) | ||
| undershoot = np.abs(100. * (-yout).min() / np.abs(InfValue)) | ||
| else: | ||
| tr_lower_index = (np.where(yout >= RiseTimeLimits[0] * InfValue)[0])[0] | ||
| tr_upper_index = (np.where(yout >= RiseTimeLimits[1] * InfValue)[0])[0] | ||
| # SettlingTime | ||
| for i in reversed(range(T.size - 1)): | ||
| if (yout[i] <= inf_margin) | (yout[i] >= sup_margin): | ||
| settling_time = T[i + 1] | ||
| break | ||
| # Overshoot and Undershoot | ||
| overshoot = np.abs(100. * (yout.max() - InfValue) / InfValue) | ||
| undershoot = np.abs(100. * yout.min() / InfValue) |
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Hmm, this has a lot of duplicate code. Have you considered using a sign variable and the abs() function instead of two completely separate blocks? I think It won't turn out much more convoluted than the current InfValue < 0 part.
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Mar 13, 2021
systems converting to SISO systems from input=0 to output =0 solve problems with non stationary systems doing SteadyStateValue= nan when y_final is inf
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Other than the MIMO handling (#574), this looks good to me. I'll merge this. Future PRs can improve it further. |
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This modification improve step_info results:
Solve the issue Any plan for step_info to support negative step responses? #337
solve the issue step_info problem with non stationary systems response #564
solve the issue step_info undershoot must be "Undershoot: Percentage of undershoot" #565
I try to fullfill pep 8
Add some aditional tests to check the results