scholarly journals Fractional-Order Generalized Predictive Control: Application for Low-Speed Control of Gasoline-Propelled Cars

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
M. Romero ◽  
A. P. de Madrid ◽  
C. Mañoso ◽  
V. Milanés ◽  
B. M. Vinagre
Keyword(s):  
Predictive Control ◽  
Pid Controller ◽  
Control Strategies ◽  
Generalized Predictive Control ◽  
Unmodeled Dynamics ◽  
Mathematical Tool ◽  
Test Bed ◽  
External Disturbances ◽  
Highly Nonlinear ◽  
Control Application

There is an increasing interest in using fractional calculus applied to control theory generalizingclassicalcontrol strategies as the PID controller and developing new ones with the intention of taking advantage of characteristics supplied by this mathematical tool for the controller definition. In this work, the fractional generalization of the successful and spread control strategy known as model predictive control is applied to drive autonomously a gasoline-propelled vehicle at low speeds. The vehicle is a Citroën C3 Pluriel that was modified to act over the throttle and brake pedals. Its highly nonlinear dynamics are an excellent test bed for applying beneficial characteristics of fractional predictive formulation to compensate unmodeled dynamics and external disturbances.

scholarly journals Nonlinear model predictive control of LDPE autoclave reactors

2021 ◽  
Author(s):  
Noel C Jacob
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Chemical Engineering ◽  
Closed Loop ◽  
Control Strategies ◽  
Linear Process ◽  
Mechanistic Modeling ◽  
Nonlinear Model Predictive Control ◽  
Highly Nonlinear

Polymerization reactors are characterized by highly nonlinear dynamics, multiple operating regions, and significant interaction among the process variables, and are therefore, usually difficult to control efficiently using conventional linear process control strategies. It is generally accepted that nonlinear control strategies are required to adequately handle such processes. In this work, we develop, implement, and evaluate via simulation a nonlinear model predictive control (NMPC) formulation for the control of two classes of commercially relevant low-density polyethylene (LDPE) autoclave reactors, namely, the single, and multi-zone multi-feed LDPE autoclave reactors. Mathematical models based on rigorous, first-principles mechanistic modeling of the underlying reaction kinetics, previously developed by our research group, were extended to describe the dynamic behaviour of the two LDPE autoclave reactors. Unscented Kalman filtering (UKF) based state estimation, not commonly used in chemical engineering applications, was implemented and found to perform quite well. The performance of the proposed NMPC formulation was investigated through a select number of simulation cases on the mathematical ‘plant’ models. The resulting closed-loop NMPC performance was compared with performance obtained with conventional linear model predictive control (LMPC) and proportional-integral-derivative (PID) controllers. The results of the present study indicate that the closed-loop disturbance rejection and tracking performance delivered by the NMPC algorithm is a significant improvement over the aforementioned controllers.

scholarly journals A Design of Auto-Tuning PID Controller Based on Generalized Predictive Control for a PVC Reactor.

1999 ◽  
Vol 25 (2) ◽  
pp. 248-252
Author(s):  
NORIO MIURA ◽  
MASAO IMAEDA ◽  
KYOUJI HASHIMOTO ◽  
R. K. WOOD ◽  
HIROFUMI HATTORI ◽  
...  
Keyword(s):  
Predictive Control ◽  
Pid Controller ◽  
Generalized Predictive Control ◽  
Auto Tuning

Design and Experimental Evaluation of a Data-Oriented Generalized Predictive PID Controller

2016 ◽  
Vol 28 (5) ◽  
pp. 722-729 ◽  
Author(s):  
Zhe Guan ◽  
◽  
Shin Wakitani ◽  
Toru Yamamoto ◽  
Keyword(s):  
Predictive Control ◽  
Pid Controller ◽  
Control Design ◽  
Point Of View ◽  
Generalized Predictive Control ◽  
Control Law ◽  
Control Parameters ◽  
Proportional Integral Derivative ◽  
Molding Machine ◽  
Output Data

[abstFig src='/00280005/15.jpg' width='300' text='Schematic figure of data-oriented GPC-PID controller' ] This paper presents a data-oriented technique for designing a proportional-integral-derivative (PID) controller based on a generalized predictive control law for linear unknown systems. In several control design approaches, a model-based control theory, which requires accurate modeling and identification of the plant, is used to calculate the control parameters. However, in higher-order systems and/or systems with an unknown time delay such as chemical industries and thermal industries, it is difficult to model or identify the plant accurately. Over the last decade, data-oriented techniques in which the online or offline data are utilized have been attracting considerable attention. Designing the controllers for unknown plants based on only the input/output data is the main feature of this technique. In this study, controller parameters are first obtained by using a generalized predictive control law with the data-oriented technique, and are converted to PID parameters from the practical point of view. The proposed method is validated experimentally using a real injection-molding machine. The results demonstrate the efficiency of the proposed method.

Design of an Auto-Tuning PID Controller by a Generalized Predictive Control Method

2005 ◽  
Vol 38 (2) ◽  
pp. 147-153 ◽  
Author(s):  
Wenlong Zhang ◽  
Masao Imaeda ◽  
Reginald K. Wood ◽  
Kyoji Hashimoto
Keyword(s):  
Predictive Control ◽  
Pid Controller ◽  
Control Method ◽  
Generalized Predictive Control ◽  
Auto Tuning

Achieving Improved Personalization and Energy Efficiency in Cohabited Work-Spaces Through Data-Driven Predictive Control

2020 ◽  
Author(s):  
Syed Ahsan Raza Naqvi ◽  
Zachary Nawrocki ◽  
Zaid Bin Tariq ◽  
Koushik Kar ◽  
Sandipan Mishra
Keyword(s):  
Predictive Control ◽  
Energy Savings ◽  
Control Strategies ◽  
Control Policy ◽  
Data Driven ◽  
Test Bed ◽  
Model Learning ◽  
Conference Room ◽  
Work Spaces ◽  
Temperature Preferences

Abstract This paper studies the problem of indoor zone temperature control in shared work-spaces equipped with heterogeneous heating and cooling sources with the goal of increased energy savings and environment personalization. We consider two scenarios to assess the performance of our control strategies. The first scenario requires time-bound pre-cooling/pre-heating of a shared space in preparation for a scheduled activity (Scenario A). The second scenario considers a cohabited work-space where occupants have different temperature preferences (Scenario B). Utilizing an on-campus smart conference room (SCR) as a test-bed, we use data-driven model learning to establish a relationship between the room’s heating, ventilation and cooling (HVAC) operations and the zone temperatures. Next, we use a model predictive control (MPC)-based approach to achieve a desired average temperature while minimizing power consumption (for Scenario A) and minimize the thermal discomfort experienced by individuals based on their temperature preferences (for Scenario B). The experimental results show that for Scenario A, the proposed control policy can save a significant amount of energy and achieve the desired mean temperature in the space fairly accurately. We further note that for Scenario B, the control scheme can achieve a significant spatial differentiation in temperature towards satisfying the occupants’ thermal preferences.

Mimo Generalized Predictive Control for a Small Wind Turbine–Fuel Cell Hybrid Energy System

2009 ◽  
Author(s):  
Guillermo E. Valencia ◽  
Gabriel Cubas Glen ◽  
John C. Turizo ◽  
Ramiro J. Chamorro
Keyword(s):  
Fuel Cell ◽  
Wind Turbine ◽  
Predictive Control ◽  
Energy System ◽  
Pem Fuel Cells ◽  
Generalized Predictive Control ◽  
Hybrid Energy System ◽  
Hybrid Energy ◽  
Small Wind Turbine ◽  
Highly Nonlinear

This paper presents a comparative performance analysis between the generalized predictive control (GPC) and the traditional proportional integral derivative (PID) under different dynamics cases in hybrid energy systems, which is composed of by a 400 W small wind turbine, a polymer electrolyte membrane (PEM) fuel cells (PEMFC), an electrolyzer, and finally the ultracapacitors and a power converter unit in order to minimize voltage fluctuations in the system and generate AC voltage. In addition, the transient responses of the system to step changes in the load current and wind speed are presented as a result of the manipulation in the flow of reactants to the fuel cell. SIMULINK™ is used for the simulation of this highly nonlinear hybrid energy system.

scholarly journals Comparison of model predictive control strategies for a fluidized catalytic cracker

2017 ◽  
Vol 6 (4) ◽  
pp. 181
Author(s):  
Alexandre Boum ◽  
Jean Pierre Corriou ◽  
Abderrazak Latifi
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategies ◽  
Multivariable Control ◽  
Gas Oil ◽  
Dynamic Matrix Control ◽  
Catalyst Recycling ◽  
Dynamic Matrix ◽  
Highly Nonlinear ◽  
Matrix Control

A FCC model is used to compare five different Model Predictive Control (MPC) strategies. The FCC process is a complex petrochemical unit with catalyst recycling that makes its behaviour highly nonlinear. The FCC comprises a riser, a separator and a regenerator with important heat coupling due to the endothermic cracking reactions of gas oil in the riser and the exothermic combustion reactions in the regenerator. The riser and the regenerator exhibit fast and slow dynamics respectively. The temperatures at riser top and in the regenerator should be controlled by manipulation of catalyst and air flow rates. All these nonlinear and coupled characteristics render the multivariable control problem difficult and thus the FCC process constitutes a valuable benchmark for comparing control strategies. Here, the performances of Dynamic Matrix Control, Quadratic Dynamic Matrix Control, MPC control with penalty on the outputs, NonLinear MPC control, Observer Based MPC control are compared.

Study on an Improved Generalized Predictive Control Algorithm in 660MW Generating Unit

2013 ◽  
Vol 420 ◽  
pp. 381-386
Author(s):  
Tong Xiang He ◽  
Yao Du
Keyword(s):  
Predictive Control ◽  
Control Algorithm ◽  
Good Control ◽  
Generalized Predictive Control ◽  
Reference Trajectory ◽  
Optimal Prediction ◽  
External Disturbances ◽  
Controlled Object

In this paper, the generalized predictive control algorithm with measurable external disturbances (FF-GPC) is deduced. On this basis, in view of the shortcoming of GPC algorithm that the overshooting is large in some degree with large lag and delay process, an improved generalized predictive control algorithm (FF-IGPC) is proposed. Based on the concept of multi-step "quasi-optimal prediction", the new algorithm puts forward an improvement for the reference trajectory. Taking 660MW coal-fired generating unit as controlled object, the FF-GPC and FF-IGPC algorithm are simulated. The results verified the feasibility of the FF-GPC algorithm, and the good control performances of the FF-IGPC algorithm.

Sign in / Sign up

Export Citation Format

Share Document