One-step active control strategy as approximation of dual control

Stochastic adaptive control gives a possibility to deal with uncertainties in system descriptions. In contrast to other robust methods, it uses probabilistic description of uncertain system parameters and consequently, stochastic optimization methods are used to design the controller. It also uses identification methods to improve the system model and thus further improve overall performance. In stochastic adaptive control, the controller that achieves required control performance and keeps gathering information about the system at the same time, is referred to as a controller with dual properties. As the optimal dual controller is computationally intractable, approximations of the optimal problem are searched. In this paper we propose a control strategy for ARX systems with dual properties. This active control strategy is based on the well known cautious strategy, but takes the quality of identification in one step ahead into consideration. This strategy shows how to improve control performance mainly in cases when the initial uncertainty in system parameters is large.