Analysis of Tuning Parameters of Model Predictive Controller (MPC)

Process optimization is very important in the engineering industries. As optimisation is achieved, less consumption of energy and utilities can be obtained for the process. In achieving optimisation, the response should be responded close to the reference values. The refineries nowadays consist mainly of multi variable unit process. Thus, to achieve optimisation using classical approach will be less reliable and time consuming. Hence, the introduction of Model Predictive Controller (MPC) to the process unit is more suitable compared to the classical approach. MPC is capable to solve high order problem and multivariate processes. The successful of MPC depends on the selection of tuning parameters. Therefore, by analysing the effect of each tuning parameters on the controller performance, promising performance of MPC can be produced. Firstly, the processes are selected from books as a case study to resemble the high order and multi variable problem processes. Then, the analysis will be done to study the effect of input weightage (UwO, output weightage (ywt), control horizon (M) and prediction horizon (P) on the controller performance. By changing one of the tuning parameter, the other tuning parameters have to be kept constant

Implementación de model predictive control en sistemas multivariables

Model Predictive Control está diseñado para resolver problemas de automatización de procesos industriales y control los cuales se caracterizan por presentar un comportamiento dinámico difícil, inestable, de fase no-mínima y sistemas con retardos y perturbaciones. MPC se puede ver como una estrategia de control la cual utiliza un sistema matemático interno de lo que se quiere controlar siendo éste el modelo de predicción. Dicho modelo se utiliza en la estadística de las variables a controlar. MPC funciona de la siguiente manera: se muestrean las variables futuras a ser manipuladas para lograr que en el Horizonte de Predicción de dichas variables controladas apunten a un punto de referencia. MPC es ideal frente a otros métodos de control ya que es muy flexible lo cual permite incorporar varios modelos de predicción, al igual que la restricción en señales del sistema, lo cual es esencial en la solución de muchos problemas de ingeniería en plantas complejas cuyo modelado exacto no es posible

Analysis of Tuning Parameters of Model Predictive Controller (MPC)

Process optimization is very important in the engineering industries. As optimisation is achieved, less consumption of energy and utilities can be obtained for the process. In achieving optimisation, the response should be responded close to the reference values. The refineries nowadays consist mainly of multi variable unit process. Thus, to achieve optimisation using classical approach will be less reliable and time consuming. Hence, the introduction of Model Predictive Controller (MPC) to the process unit is more suitable compared to the classical approach. MPC is capable to solve high order problem and multivariate processes. The successful of MPC depends on the selection of tuning parameters. Therefore, by analysing the effect of each tuning parameters on the controller performance, promising performance of MPC can be produced. Firstly, the processes are selected from books as a case study to resemble the high order and multi variable problem processes. Then, the analysis will be done to study the effect of input weightage (UwO, output weightage (ywt), control horizon (M) and prediction horizon (P) on the controller performance. By changing one of the tuning parameter, the other tuning parameters have to be kept constant

Generalized predictive control based on particle swarm optimization for linear/nonlinear process with constraints

Abstract: This paper presents an intelligent generalized predictive controller (GPC) based on particle swarm optimization (PSO) for linear or nonlinear process with constraints. We propose several constraints for the plants from the engineering point of view and the cost function is also simplified. No complicated mathematics is used which originated from the characteristics ofPSO. This method is easy to be used to control the plants with linear or/and nonlinear constraints. Numerical simulations are used to show the performance of this control technique for linear and nonlinear processes, respectively. In the first simulation, the control signal is computed based on an adaptive linear model. In the second simulation, the proposed method is based on a fixed neural network model for a nonlinear plant. Both of them show that the proposed control scheme can guarantee a good control performance

Development of a new comprehensive predictive modeling and control framework for multiple-input, multiple-output processes

The increase in the competitiveness of chemical process industries has necessitated the need for lowered energy and raw material consumption and improved quality control with tighter limits. This stronger control of the process conditions has generated interest in the use of advanced process control, and Model Predictive Control (MPC) is one such approach. The idea behind MPC is the use of a model to predict future behavior and use that knowledge to manipulate the input variables so that a cost function is minimized, resulting in optimal control. The predictive model is the core of the MPC method, and the success of a particular strategy hence depends on the accuracy of the model. The task of model building is also very challenging and time-consuming, so an ideal modeling approach would be one that does not require too much data but maintains its accuracy. The semi-empirical modeling approach has the strength that by using an intelligent model form, it has minimal data requirements. The use of semi-empirical models was first demonstrated by Rollins et al., and they coined the term SET (semi-empirical approach) for their method. The accuracy of SET over conventional empirical models was one of the biggest advantages of that approach. The ease of model identification, robustness in parameters, and a novel algorithm were some of the other strengths. Thus SET showed great potential for use in a multivariate situation, and the results of this work have shown that SET has been able to handle this challenge successfully. This extension led to the creation of a comprehensive modeling framework, which is far superior to the current modeling approach using the semi-empirical models. The various components of this approach are: use of statistical design of experiments, model identification, a novel model structure, and finally an algorithm that seeks to maximize accuracy

Control of nonlinear flexible space structures

With the advances made in computer technology and efficiency of numerical algorithms over last decade, the MPC strategies have become quite popular among control community. However, application of MPC or GPC to flexible space structure control has not been explored adequately in the literature. The work presented in this thesis primarily focuses on application of GPC to control of nonlinear flexible space structures;This thesis is particularly devoted to the development of various approximate dynamic models, design and assessment of candidate controllers, and extensive numerical simulations for a realistic multibody flexible spacecraft, namely, Jupiter Icy Moons Orbiter (JIMO)---a Prometheus class of spacecraft proposed by NASA for deep space exploratory missions;A stable GPC algorithm is developed for Multi-Input-Multi-Output (MIMO) systems. An end-point weighting (penalty) is used in the GPC cost function to guarantee the nominal stability of the closed-loop system. A method is given to compute the desired end-point state from the desired output trajectory. The methodologies based on Fake Algebraic Riccati Equation (FARE) and constrained nonlinear optimization, are developed for synthesis of state weighting matrix. This makes this formulation more practical. A stable reconfigurable GPC architecture is presented and its effectiveness is demonstrated on both aircraft as well as spacecraft model;A representative in-orbit maneuver is used for assessing the performance of various control strategies using various design models. Different approximate dynamic models used for analysis include linear single body flexible structure, nonlinear single body flexible structure, and nonlinear multibody flexible structure. The control laws evaluated include traditional GPC, feedback linearization-based GPC (FLGPC), reconfigurable GPC, and nonlinear dissipative control. These various control schemes are evaluated for robust stability and robust performance in the presence of parametric uncertainties and input disturbances. Finally, the conclusions are made with regard to the efficacy of these controllers and potential directions for future research

Adaptive Optimal Control The Thinking Man's GPC

Exploring connections between adaptive control theory and practice, this book treats the techniques of linear quadratic optimal control and estimation (Kalman filtering), recursive identification, linear systems theory and robust arguments