Model predictive control techniques for hybrid systems

This paper describes the main issues encountered when applying model predictive control to hybrid processes. Hybrid model predictive control (HMPC) is a research field non-fully developed with many open challenges. The paper describes some of the techniques proposed by the research community to overcome the main problems encountered. Issues related to the stability and the solution of the optimization problem are also discussed. The paper ends by describing the results of a benchmark exercise in which several HMPC schemes were applied to a solar air conditioning plant.Ministerio de Eduación y Ciencia DPI2007-66718-C04-01Ministerio de Eduación y Ciencia DPI2008-0581

Streakline-based closed-loop control of a bluff body flow

A novel closed-loop control methodology is introduced to stabilize a cylinder wake flow based on images of streaklines. Passive scalar tracers are injected upstream the cylinder and their concentration is monitored downstream at certain image sectors of the wake. An AutoRegressive with eXogenous inputs mathematical model is built from these images and a Generalized Predictive Controller algorithm is used to compute the actuation required to stabilize the wake by adding momentum tangentially to the cylinder wall through plasma actuators. The methodology is new and has real-world applications. It is demonstrated on a numerical simulation and the provided results show that good performances are achieved.Fil: Roca, Pablo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; ArgentinaFil: Cammilleri, Ada. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; ArgentinaFil: Duriez, Thomas Pierre Cornil. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; ArgentinaFil: Mathelin, Lionel. Centre National de la Recherche Scientifique. Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur; FranciaFil: Artana, Guillermo Osvaldo. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

On the almost sure central limit theorem for ARX processes in adaptive tracking

The goal of this paper is to highlight the almost sure central limit theorem for martingales to the control community and to show the usefulness of this result for the system identification of controllable ARX(p,q) process in adaptive tracking. We also provide strongly consistent estimators of the even moments of the driven noise of a controllable ARX(p,q) process as well as quadratic strong laws for the average costs and estimation errors sequences. Our theoretical results are illustrated by numerical experiments

Advanced Generalized Predictive Control and Its Application to Tiltrotor Aircraft for Stability Augmentation and Vibration Reduction

The goals of this research were to restore generalized predictive control (GPC) capability at NASA and within the community, to better understand GPC and its performance relative to other options, and to improve upon the capability of GPC. Unique to this research is the comparison of GPC with other control options including PID controllers, optimal control theory, and other versions of the similar AutoRegressive moving average model with eXogenous inputs (ARX) models. Similar to GPC, ARX models use an experimentally acquired system identification to characterize the input/output relationship between controls and response measurements. Because this relationship is determined from acquired data, minimal knowledge of the system behavior is required to employ ARX or GPC controllers. As a result of these comparisons, it was observed that GPC is typically the best performing control option and typically has better gain and phase margins when properly employed. Also unique to this dissertation is the use of orthogonal multisine excitation as the command inputs for GPC application rather than the typical distinguishable random noise. Finally, the concept of Advanced GPC (AGPC) is introduced as a part of this dissertation work. AGPC is a self-adapting algorithm that improves traditional GPC when conditions change from those used to derive the system identification. AGPC is also better performing than traditional GPC in some cases even when the conditions do not change from those used to acquire the system identification. Application of AGPC requires the monitoring of performance figures of merit, and the application of control dither when the metrics indicate that the controls are not distinguishable enough or the response of the system is inadequate to properly characterize the input/output relationship. Finally, for experimental application of GPC and AGPC, techniques were introduced to increase model safety and include features such as a magnitude ramp rate when closing the control loop, master gain values to reduce control or dither authority, continual computation of figures of merit, the ability to gradually change from one control algorithm to another, and visualization of control commands prior to closing the control loop and/or switching from one control algorithm to another

Study on adaptive control of nonlinear dynamical systems based on quansi-ARX models

制度:新 ; 報告番号:甲3441号 ; 学位の種類:博士(工学) ; 授与年月日:15-Sep-11 ; 早大学位記番号:新576

Benchmarking of Advanced Control Strategies for a Simulated Hydroelectric System

This paper analyses and develops the design of advanced control strategies for a typical hydroelectric plant during unsteady conditions, performed in the Matlab and Simulink environments. The hydraulic system consists of a high water head and a long penstock with upstream and downstream surge tanks, and is equipped with a Francis turbine. The nonlinear characteristics of hydraulic turbine and the inelastic water hammer effects were considered to calculate and simulate the hydraulic transients. With reference to the control solutions addressed in this work, the proposed methodologies rely on data-driven and model-based approaches applied to the system under monitoring. Extensive simulations and comparisons serve to determine the best solution for the development of the most effective, robust and reliable control tool when applied to the considered hydraulic system

Approximate Model Predictive Control for Nonlinear Multivariable Systems

The control of multi-input multi-output (MIMO) systems is a common problem in practical control scenarios. However in the last two decades, of the advanced control schemes, only linear model predictive control (MPC) was widely used in industrial process control (Ma-ciejowski, 2002). The fundamental common idea behind all MPC techniques is to rely o

MULTIVARIABLE PREDICTIVE CONTROL OF A THERMAL POWER PLANT

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Model Predictive Control Using Orthonormal Basis Filter

Proportional Integral Derivative (PID) controller is the most common controller that acts as standard tool in a process control industry. However, when interacting with Multiple Input and Multiple Output (MIMO) process, the interaction is difficult to be controlled by PID controller. Therefore, this project will focus on Model Predictive Control (MPC) that is one of optimization strategy that can control MIMO interaction by predicting the effect of potential control action. In this project, a mathematical model of Orthonormal Basis Filter (OBF) will be developed on the distillation column based on Wood-Berry model with a feedback control (a closed loop system). A simulation of MPC is done by using MATLAB coding while PID is simulated using SIMULINK. Based on the simulation, the performance of MPC and PID controller are evaluated by using the Integral Error Criteria: Integral Absolute Error (IAE), Integral of the Squared Error (ISE) and Integral of the time-weighted absolute error (ITAE) and also with total input variation. Lower integral error criteria and total input variation value indicate a better model accuracy and efficiency of controller for MIMO system