A design method of a generalized predictive control system based on parametrization of two-degree-of-freedom integral controllers

Abstract

A new design method for a generalized predictive control (GPC) system based on parametrization of two‐degree‐of‐freedom integral controllers has been proposed. The objective is to guarantee stability of the control system without depending on the design parameters and to achieve low sensitivity against the plant perturbation and the disturbance. The design procedure consists of two steps. First, we design a basic integral controller for a nominal plant using the linear quadratic Gaussian (LQG) method and parametrize a class of two‐degree‐of‐freedom stabilizing controllers. Next, we tune the feedforward controller to incorporate the GPC method into our control structure. A numerical example is presented to show the effectiveness of the proposed method by comparing it with the conventional GPC method. © 1999 Scripta Technica, Electr Eng Jpn, 129(2): 62–70, 1999

Design of an optimal generalized predictive control system

“…By using Eqs. (1) and (2), the output prediction vector Ŷ td can be represented as where Furthermore, the prediction vector U t of the control input can be represented as [4] where and where Q t [q t,0 q t,Nd ] g is the coefficient vector of the time-varying smoother where r B t is the steady-state value of the setpoint. We then have the following result.…”

“…Recently, the GPC method is gaining a position of importance in the field of process control, and design methods based on the polynomial approaches [13] and the state space approaches [4,5] have been proposed. On the other hand, if there is no disturbance or plant perturbation, the servo system which can track to the step signal without steady-state error is constructed without the integrator.…”