Research On Unknowed Parameter Direct Generalized Predictive Control

Generalized predictive control (GPC) is a kind of advance control method and it is used to industrial broadly. But it has the shortcoming of large computation load because of online solving the Diophantine equation and inverse matrix. So some kinds of improved algorithms of GPC are proposed to improve the real-time in this paper. The main achievements are as follow.(1)A direct GPC (DGPC) method for a class of single-input-single-output (SISO) linear system with unknown parameters is presented. This method directly adjusts the controller parameters and the unknown parameters of the estimated generalized error based on the former estimated generalized error. It is proved that the method can make the estimated generalized error converge to a small neighborhood of the origin.(2)Then, based on the mean value theorem a class of nonlinear system is replaced by a time varying linear system, and cubic spline functions polynomials are used to approximate the time varying coefficients and the unknown vectors in estimated generalized error, namely the DGPC of linear system is generalized to nonlinear system. Simulation results demonstrate the effectiveness of the method.(3)A direct Grey GPC (DGGPC) method based on Grey model is presented. This algorithm directly adjusts the controller parameters based on generalized error. It identifies small parameters, and avoids online solving the Diophantine equation and inverse matrix. So the computation load of algorithm can be reduced greatly, and real-time property is advanced. Two methods are used to design the controllers of MIMO systems. First recurs to feed-forward decoupling, and the second doesn't to decouple. Simulation results demonstrate the effectiveness of the method.