-A novel approach for the implementation of Nonlinear Model Predictive Control (NMPC) using Particle Swarm Optimization (PSO) technique is proposed. Two different approaches are made in the PSO algorithms, Random PSO (RPSO) and knowledge based PSO (KPSO) for the determination of optimum controller gain in MPC structure In order to test the performance of the proposed PSO based MPC system a nonl...

Therefore, the presence of the plant model is a necessary condition for the development of the predictive control. The success of MPC depends on the degree of precision of the plant model. In practice, modelling real plants inherently includes uncertainties that have to be considered in control design, that is control design procedure has to guarantee robustness properties such as stability and...

The stability of nominal model predictive control (MPC) is well characterized in the literature. Lyapunov theory forms a framework that unites both linear and nonlinear control problems, and it is amenable to a wide variety of new MPC formulations (see (Rawlings and Mayne, 2009, Ch. 2) and the references it contains). In all practical implementations, however, robustness to disturbances is a re...

This paper addresses the development of new formulations for estimating modeling errors or unmeasured disturbances to be used in Model predictive control (MPC) algorithms during open-loop prediction. Two different formulations were developed in this paper. One is used in MPC that directly utilizes linear models and the other in MPC that utilizes non-linear models. These estimation techniques we...

A determination method for 3-methylpyrazole-5-carboxylic acid (MPC), an inhibitor of D-amino acid oxidase (DAAO), in rat plasma was developed by using high-performance liquid chromatography-mass spectrometry (LC-MS). The structural isomer of MPC, 3-methylpyrazole-4-carboxylic acid, was used as an internal standard, and the intra- and inter-day accuracies and precisions were satisfactory for the...

This thesis is concerned with the theoretical foundations of Robust Model Predictive Control and its application to tracking control problems. Its first part provides an introduction to MPC for constrained linear systems as well as a survey of different Robust MPC methodologies. The second part consists of a discussion of the recently developed Tube-Based Robust MPC framework and its extension ...

This paper details nonlinear Model-based Predictive Control (MPC) algorithms for MIMO processes modelled by means of neural networks of a feedforward structure. Two general MPC techniques are considered: the one with Nonlinear Optimisation (MPC-NO) and the one with Nonlinear Prediction and Linearisation (MPC-NPL). In the first case a nonlinear optimisation problem is solved in real time on-line...

An increasing number of applications ranging from multi-vehicle systems, largescale process control systems, transportation systems to smart grids call for the development of cooperative control theory. Meanwhile, when designing the cooperative controller, the state and control constraints, ubiquitously existing in the physical system, have to be respected. Model predictive control (MPC) is one...

The focus of this chapter is on MPC of constrained dynamic systems, both linear and nonlin‐ ear, to illuminate the ability of MPC to handle constraints that makes it so attractive to in‐ dustry. We first give an overview of the origin of MPC and introduce the definitions, characteristics, mathematical formulation and properties underlying the MPC. Furthermore, MPC methods for linear or nonlinea...

This paper presents an analysis of the continuous stirred tank reactor (CSTR) temperature control with Proportional-Integral-Derivative (PID) Controller, Model Predictive Controller (MPC) and Hybrid-Model Controller-Proportional Integral Derivative (MPC-PID). It is main goal this project to find a suitable improvement strategy for system’s stability accuracy be more stable. By creating model, s...