System identification: Design variables and the design objective. (English) Zbl 0639.93059
Modelling, robustness and sensitivity reduction in control systems, Proc. NATO Adv. Res. Workshop, Groningen/Neth. 1986, NATO ASI Ser., Ser. F 34, 251-270 (1987).
Summary: [For the entire collection see Zbl 0624.00022.]
Models and model quality are prime concerns for most design issues in control and system analysis. In this contribution we discuss how to build mathematical models that, given certain constraints, are of optimum quality for a prespecified application. We then take into account the influence of both bias errors and random errors on the model. It turns out that for a fairly broad class of identification methods in the prediction error family, the optimal choices of design variables can be given in an explicit form.
Models and model quality are prime concerns for most design issues in control and system analysis. In this contribution we discuss how to build mathematical models that, given certain constraints, are of optimum quality for a prespecified application. We then take into account the influence of both bias errors and random errors on the model. It turns out that for a fairly broad class of identification methods in the prediction error family, the optimal choices of design variables can be given in an explicit form.
MSC:
| 93E12 | Identification in stochastic control theory |
| 68U20 | Simulation (MSC2010) |
| 93C05 | Linear systems in control theory |
| 62M20 | Inference from stochastic processes and prediction |
| 93B50 | Synthesis problems |
| 93B55 | Pole and zero placement problems |
| 93E10 | Estimation and detection in stochastic control theory |
