Integrated modeling and controller design with application to flexible structure control

Abstract

It is well known that modeling and control problems are not independent. Hence, the final step in any practical control design is a ‘try and see’ procedure. There will always be a need for on-line tuning by the test engineer. The purpose of mathematical theories of modeling and control is therefore not to replace the ‘try and see’ requirement of the engineer, but to help reduce the number of iterations the engineer must use to find a suitable solution to the real physical problem (which defies exact mathematical description). This paper presents an iterative method to integrate the two problems of modeling and control and demonstrates the procedure in a physical application. Specifically, we present a controller design scheme integrating a Q-Markov Covariance equivalent realization (QMC) identification algorithm, a Modal Cost Analysis (MCA) model reduction algorithm, and an Output Variance Constraint (OVC) controller design algorithm. The identified model is used as a truth model for subsequent (off-line) controller evaluation. In the first step of the integrated procedure, this model is reduced for controller design. Closed-loop evaluation provides information to improve the reduced model (this is the integration of the two model reduction and control disciplines that is contributed by this paper). The process repeats iteratively for low order and high performance controller design. This procedure is applied to NASA's ACES flexible structure at the Marshall Space Flight Center. The experimental results demonstrate the effectiveness of the procedure for large flexible structure control.