Adaptive control of shaking tables using the minimal control synthesis algorithm

Traditional shaking–table testing has been limited by the effectiveness of conventional fixed–gain algorithms used in their control. These algorithms are normally based on linear models of the shaking table and specimen, whose parameters are assumed to be fixed for the duration of the test. Although the influence of the specimen in the overall system dynamics can be partly removed by fine–tuning the linear controller, this process cannot deal with nonlinear effects and is limited in scope by the expertise of the operator. The minimal control synthesis (MCS) algorithm is a form of adaptive control, which was originally and successfully employed to cope with the nonlinear problems in the field of robotics. The MCS algorithm can tune the controller in real–time without any parametric knowledge of the system to be controlled. This paper describes how MCS has been incorporated within both analog and digital controllers for shaking tables and shows some of the results achieved on tables at the University of Bristol and at Athens Technical University. In both cases, the introduction of adaptive control has noticeably improved the performance of the shaking table, correcting errors by more than 5 dB in some experiments.