Model predictive control (MPC) is a very popular controller design method in the process industry. One of the main advantages of MPC is that it can handle constraints on the inputs and outputs. Usually MPC uses linear discrete-time models. Recently we have extended this framework to max-plus-linear discrete event systems. In this paper we further explore this topic. More specifically, we focus ...

Model predictive control (MPC) is a popular controller design technique in the process industry. Conventional MPC uses linear or nonlinear discrete-time models. Recently, we have extended MPC to a class of discrete event systems that can be described by a model that is “linear” in the max-plus algebra. In our previous work we have considered MPC for the time-invariant case. In this paper we con...

A number of results are derived for analysing the robust feasibility of a given Model Predictive Control (MPC) scheme which ignores model mismatch and/or disturbances during control input computation. The main contribution of this paper is the development of computationally tractable tests for determining the robust feasibility of an MPC controller for linear or piecewise affine systems, where ...

Model predictive control (MPC) is presented as a robust, flexible decision framework for dynamically managing inventories and satisfying customer demand in demand networks. In this paper, a formulation and the benefits of an MPC-based, control-oriented tactical inventory management system meaningful to the semiconductor industry are presented via two significant examples. The translation of ava...

A superheater is an essential part of the steam generation process in the boiler-turbine system. Reliable control of temperature in the superheated steam temperature system is necessary to guarantee efficiency and high load-following capability in the operation of coal-fired Thermal power plant. The PI and PID controllers are extensively used in cascade control of secondary superheated steam te...

A neural model-based predictive control scheme is proposed for dealing with steady-state offsets found in standard MPC schemes. This structure is based on a constrained local instantaneous linear model-based predictive control methodologies together with a static offset pre-filter for assuring free tracking errors and disturbance rejection features. A non-linear state-space neural network archi...

Multivariate controller performance assessment (MVPA) has been developed over the last several years, but its application in advanced model predictive control (MPC) has been very limited mainly due to issues associated with comparability of variance control objective and that of MPC. MPC has been proven as one of the most effective advanced process control (APC) strategies to deal with multivar...

Practical difficulties involved in implementing stabilizing model predictive control laws for nonlinear systems are well known. Stabilizing formulations of the method normally rely on the assumption that global and exact solutions of nonconvex, nonlinear optimization problems are possible in limited computational time. In this paper, we first establish conditions under which suboptimal model pr...

In this paper we developed a control scheme starting from Bond Graph representation of the Quanser Flexible link experiment. The objective of the experiment is to design a control system that positions the flexible arm as well as regulating vibration. The structure of the control scheme uses two types of control methods. In order to achieve a faster or robust response together with optimal cont...

Model Predictive Control (MPC) is of interest because it is one of the few control design methods which preserves standard design variables and yet handles constraints. This paper focuses on a stochastic MPC problem with constraints specified in a probabilistic sense. Our aim is to study the incorporation of state estimates into the MPC problem. The original problem can be approximated by a det...