Reasoning about input-output modeling of dynamical systems

Abstract. The goal of input-output modeling is to apply a test input to a system, analyze the results, and learn something useful from the causeeffect pair. Any automated modeling tool that takes this approach must be able to reason effectively about sensors and actuators and their interactions with the target system. Distilling qualitative information from sensor data is fairly easy, but a variety of difficult control-theoretic issues — controllability, reachability, and utility — arise during the planning and execution of experiments. This paper describes some representations and reasoning tactics, collectively termed qualitative bifurcation analysis, that make it possible to automate this task. 1 Input-Output Modeling System identification (SID) is the process of inferring an internal ordinary differential equation (ODE) model from external observations of a system. The computer program pret[5] automates the SID process, using a combination of artificial intelligence and system identification techniques to construct ODE models of lumped-parameter continuous-time nonlinear dynamic systems. As di-modeling specification ODE model domain math PRET data excitation sensors actuators target system Fig. 1. pret uses sensors and actuators to interact with target systems in an inputoutput approach to dynamical system modeling. agrammed in Fig. 1, pret uses domain knowledge to combine model fragments into ODEs, then employs actuators and sensors to learn more about the target system, and finally tests the ODEs against the actuator/sensor data using a body of mathematical knowledge encoded in first-order logic[20]