QFT based gain-scheduling control design for linear time-varying systems

Most of linear time-varying (LTV) systems except special cases have no general solution for the dynamic equations. Thus, it is difficult to design time-varying controllers in analytic ways, and other control design approaches such as robust control and gain-scheduling have been applied to control design for the LTV systems. A robust control method such as quantitative feedback theory (QFT) has an advantage of guaranteeing the stability and the performance specification in frozen time sense. However, if these methods are applied to the approximated linear time-invariant (LTI) plants with large uncertainty, the designed control will be constructed in complicated forms and usually not suitable for fast dynamic performance. In this paper, as a method to enhance the fast dynamic performance, the approximated uncertainty of time-varying parameters are reduced by the proposed gain-scheduling control design based on QFT for LTV systems with bounded time-varying parameters. To generate a continuous and smooth gain-scheduling function, multilayer neural network is used