Subspace Data-Driven Control for Linear Parameter Varying Systems

In this research, a unique subspace data driven control for linear parameter changing system with scheduling parameters is presented. This paves the way investigating nonlinear based on results regarding that are already known. Only matrix utilized to represent output prediction value in future various time instants, while input-output observation used identify Markov form of state space forms. The cost function data-driven then adjusted using value. optimal input quadratic solved parallel distribution technique, and algorithm's iterative convergence thoroughly examined. Finally, DC motor, whose mass factor considered be one varying system, controlled suggested approach.