Study On Simplified Implementation Of Generalized Predictive Control

It is the development of industrial process control that has speeded up the emerging of model predictive control (MPC) methodology and the basic motivation of using MPC technology is to ensure significant economic benefits. Thus, one of the most essential problems of MPC technology development is how to implement these advanced algorithms in real world effectively. Just from this view, a set of simplified implementation for generalized predictive control (GPC) are presented based on the current framework of MPC. The dissertation is organized as follows:First, a brief history of MPC technology development is presented, followed by its current features and limitations of existing technology and its future trends. The distinguishing features of GPC approach and its application difficulty are discussed. After summarizing the general ways to simplify the implementation of MPC and the achievements in simplifying implementation for GPC, the main research works are given.Second, for most of the physical realizable processes, the matrices C(z-1) andA(z-1) of their Controlled Autoregressive Integrated Moving Average (CARIMA)model can always be diagonally constructed, so that the formulation of GPC can be developed in more detail while explicitly considering the dead time in order to improve the computational efficiency. This model structure greatly simplifies not only the development of the GPC but also its parameter identification which can be transformed into a set of multiple input single output model parameter identification problems.Third, a state-feedback like controller of GPC is obtained by further manipulating the free response of the output predictor, whose control increment equals to the controller's coefficients multiplied by set-points and historical plant input and output data. The controller's coefficients are only determined by the model parameters and design parameters and its dimension is determined by the model structure parameters and predictive horizon, which eliminates the need to compute the free response on-line and makes the implementation of GPC controller as easy as thatof PID under the non-adaptive mode.Fourth, a more concise GPC controller is obtained by directly manipulating the output predictor using the multivariable CARIMA model recursively, whose control moves are the product of the controller's coefficients and set-points, historical input/output data of the plant and predictive errors of the predictor. The controller's coefficients are determined only by the model parameters and design parameters and its dimension only depends on the orders of the model, which avoids solving Diophantine equations on-line under adaptive mode and reduces difficulties of implementing the GPC controller and the computational overhead to the lowest limit under the non-adaptive mode.Fifth, it is pointed out that the multivariable GPC algorithm is essentially a kind of functional mapping from the multivariable process model parameters' space to the multivariable GPC controller's coefficients' space by analyzing its intrinsic mechanism. This mapping can be realized by BP neural network to obtain the GPC controller's coefficients from the model parameters directly, which can extremely reduce the computational overhead on-line and simplify the implementation of the GPC controller.Sixth, the above schemes developed in this dissertation are compared by a set of contrast experiments on a nonlinear liquid level equipment. Their feasibility, validity and equivalency are demonstrated by experiment results.Last, a summary is given to show what has been done in this paper. The applicable scope of these methods developed in this dissertation is discussed, followed by some items that must pay attention to when implement these methods and future potential research opportunities.