Classical MPC Systems in State-space Formulation
Dynamic matrix control (DMC) and generalized predictive control (GPC) are two classes of predictive control systems that have found application in many areas. This chapter will link the predictive control systems designed using the framework of state space to the classical predictive control systems. One of the common features of the classical predictive control systems is the direct utilization of plant input and output signals in the closed-loop feedback control, hence avoiding observers in the implementation. The key to the link is to revise the classical predictive control schemes using a special class of state-space formulations, where the state variables are chosen to be identical to the feedback variables that have been used in the classical predictive control systems. An example of a state-space formulation of GPC is the work by Ordys and Clarke (1993). Once the state-space model is formulated, the framework from the previous chapters is naturally extended to the classical predictive control systems, preserving all the advantages of a state-space design, including stability analysis, exponential data weighting and LQR equivalence. In addition, because of the direct use of plant input and output signals in the implementation, the predictive controller can be represented in a transfer function form, allowing direct frequency response analysis of the system to obtain critical information, such as gain and phase margins.
KeywordsModel Predictive Control Transfer Function Model Predictive Controller Laguerre Function Generalize Predictive Control
Unable to display preview. Download preview PDF.