In this paper we present a framework for robust explicit/multi–parametric model predictive control (MPC). Based on four key steps, the proposed framework offers a systematic method for the off–line design, validation/testing and implementation of robust explicit MPC controllers for embedded systems. An important feature of the framework, is the use of a robust explicit/multi–parametric MPC tech...

This paper provides an overview of commercially available model predictive control (MPC) technology, both linear and nonlinear, based primarily on data provided by MPC vendors. A brief history of industrial MPC technology is presented first, followed by results of our vendor survey of MPC control and identification technology. A general MPC control algorithm is presented, and approaches taken b...

In this paper, several relations between pole placement (PP) controller and unconstrained predictive controller are investigated. The placement of poles at certain positions leads to various types of time optimal control and has a direct relation to the choice of minimum control and output horizons for stabilising MPC. On the other hand, a predictive control method for linear systems is derived...

In this paper a robust one-step model predictive control (MPC) scheme is developed for discrete time-delay systems with polytopic-type uncertainty. The proposed MPC is obtained by minimizing a new cost function that includes multi-terminal weighting terms, subject to constraints on input. This MPC scheme allows the first move u(k|k) to be separated from the control moves governed by a state fee...

For the purpose of traffic control a piecewiseaffine (PWA) approximation of the METANET model is proposed and tested in a model-based predictive control (MPC) framework. This approximation is provided as an alternative to the rather intensive computations when using the original nonlinear nonconvex METANET traffic flow model extended with a model for vehicular emissions and fuel consumption in ...

The high cost of model predictive control (MPC) technology has hampered its wide application in process industries beyond the refining/petrochemical industry. This work aims to increase the efficiency of MPC deployment. First a semi-automatic MPC system is introduced. It consists of three modules: an MPC control module, an online identification module and a control monitor module. The goal of t...

Subspace predictive control (SPC) is recently seen in the literature for joint system identification and control design. This combination enables automatically tuning the parameters in conventional model predictive control (MPC); and therefore provides a solution to the problem of fault tolerant MPC design. The existing SPCs either deal with open-loop data or depend on the information of the co...

We propose a decentralized model predictive control (MPC) design approach for possibly large-scale processes whose structure may not be dynamically decoupled. The decoupling assumption only appears in the prediction models used by the different MPC control agents. In [1] we presented a sufficient criterion for analyzing a posteriori the asymptotic stability of the process model in closed-loop w...

This paper investigates model predictive control (MPC) of a sea wave energy converter(WEC). A novel objective function is adopted in the MPC design, which brings obvious benefits: First, the quadratic program (QP) derived from this objective function can be easily convexified, which facilitates the employment of existing efficient optimization algorithms. Second, this novel design can trade off...

In this paper, model predictive control (MPC) strategy is implemented to a GE9001E gas turbine power plant. A linear model is developed for the gas turbine using conventional mathematical models and ARX identification procedure. Also a process control model is identified for system outputs prediction. The controller is designed in order to adjust the exhaust gas temperature and the rotor speed ...