Auto-Ignition Control in Spark-Ignition Engines Using Internal Model Control Structure

2016 ◽  
Vol 139 (2) ◽  
Author(s):  
Jorge Duarte ◽  
Jesús Garcia ◽  
Javier Jiménez ◽  
Marco E. Sanjuan ◽  
Antonio Bula ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Ignition Temperature ◽  
Control Strategies ◽  
Spark Ignition ◽  
Internal Model Control ◽  
Operating Conditions ◽  
Ignition Point ◽  
Single Output ◽  
Auto Ignition

This paper analyzes the feasibility of applying model predictive control strategies for mitigation of the auto-ignition phenomenon, which affects the performance of spark-ignition internal combustion engines. The first part of this paper shows the implementation and experimental validation of a two-dimensional model, based on thermodynamic equations, to simulate operating conditions in engines fueled with natural gas. Over this validated model, several control strategies are studied in order to evaluate, through simulation analysis, which of these offer the best handling capacity of the auto-ignition phenomenon. In order to achieve this goal, multivariate control strategies are implemented for a simultaneous manipulation of the fuel/air ratio, the crank angle at ignition, and the inlet pressure. The controlled variable in this research is the temperature at the ignition point. This temperature is obtained through an estimation based on pressure in the combustion chamber at that point, which is located toward the end zone of the compression stroke. If the ignition temperature of the fuel–air mixture is reached during the compression process, then auto-ignition takes place. Proposed control strategies consist of maintaining the temperature in the ignition point below the fuel–air mixture auto-ignition temperature. The results show that auto-ignition is difficult to avoid using a single input–single output (SISO) strategy. However, a multiple input–single output (MISO) approach avoids the influence of the phenomenon without a significant impact over the engine's performance. Among the developed strategies, an approach based on model predictive control and feedforward control strategy shows the best performance, measured through the integral absolute error (IAE) index. These results open the possibility of new ways for improving the control capacity of auto-ignition phenomenon in engines compared to currently available feedback control systems.

scholarly journals Predictive control of a cascade of biochemical reactors

2021 ◽  
Vol 14 (1) ◽  
pp. 51-59
Author(s):  
Martin Mojto ◽  
Michaela Horváthová ◽  
Karol Kiš ◽  
Matúš Furka ◽  
Monika Bakošová
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Wastewater Treatment Plants ◽  
Control Strategies ◽  
Good Control ◽  
Control Performance ◽  
Robust Model Predictive Control ◽  
Single Output ◽  
Biochemical Reactors ◽  
Final Approach

Abstract Rapid growth of the human population has led to various problems, such as massive overload of wastewater treatment plants. Therefore, optimal control of these plants is a relevant subject. This contribution analyses control of a cascade of ten biochemical reactors using simulation results with the aim to design optimal and predictive control strategies and to compare the achieved control performance. The plant represents a complicated process with many variables involved in the model structure, reduced to the single-input and single-output system. The first implemented approach is linear offset-free model predictive control which provides the optimal input trajectory minimising a quadratic cost function. The second control strategy is robust model predictive control with similar features as model predictive control but including the uncertainty of the process. The final approach is generalised predictive control, mostly used in the industry because of its simple structure and sufficiently good control performance. All considered predictive controllers provide satisfactory control performance and remove the steady-state control error despite the constrained control inputs.

Finite-Set Model-Predictive Control Strategies for a 3L-NPC Inverter Operating With Fixed Switching Frequency

2018 ◽  
Vol 65 (5) ◽  
pp. 3954-3965 ◽  
Author(s):  
Felipe Donoso ◽  
Andres Mora ◽  
Roberto Cardenas ◽  
Alejandro Angulo ◽  
Doris Saez ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategies ◽  
Switching Frequency ◽  
Finite Set

scholarly journals A Novel Fuzzy Model Predictive Control of a Gas Turbine in the Combined Cycle Unit

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Guolian Hou ◽  
Linjuan Gong ◽  
Xiaoyan Dai ◽  
Mengyi Wang ◽  
Congzhi Huang
Keyword(s):  
Model Predictive Control ◽  
Gas Turbine ◽  
Predictive Control ◽  
Control Strategies ◽  
Control Process ◽  
Fuzzy Model ◽  
Weight Coefficient ◽  
Combined Cycle ◽  
Generation Process ◽  
Selection Mechanism

The complex characteristics of the gas turbine in a combined cycle unit have brought great difficulties in its control process. Meanwhile, the increasing emphasis on the efficiency, safety, and cleanliness of the power generation process also makes it significantly important to put forward advanced control strategies to satisfy the desired control demands of the gas turbine system. Therefore, aiming at higher control performance of the gas turbine in the gas-steam combined cycle process, a novel fuzzy model predictive control (FMPC) strategy based on the fuzzy selection mechanism and simultaneous heat transfer search (SHTS) algorithm is presented in this paper. The objective function of rolling optimization in this novel FMPC consists of two parts which represent the state optimization and output optimization. In the weight coefficient selection of those two parts, the fuzzy selection mechanism is introduced to overcome the uncertainties existing in the system. Furthermore, on account of the rapidity of the control process, the SHTS algorithm is used to solve the optimization problem rather than the traditional quadratic programming method. The validity of the proposed method is confirmed through simulation experiments of the gas turbine in a combined power plant. The simulation results demonstrate the remarkable superiorities of the adopted algorithm with higher control precision and stronger disturbance rejection ability as well as less optimization time.

ON MODEL PREDICTIVE CONTROL OF QUASI-RESONANT CONVERTERS

2009 ◽  
Vol 18 (07) ◽  
pp. 1167-1183 ◽  
Author(s):  
FARZAD TAHAMI ◽  
MEHDI EBAD
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Linear Models ◽  
Control Strategies ◽  
Sampling Interval ◽  
Control Synthesis ◽  
Resonant Converters ◽  
Control Input ◽  
Model Approximation ◽  
Dynamic Matrix

In this paper, different model predictive control synthesis frameworks are examined for DC–DC quasi-resonant converters in order to achieve stability and desired performance. The performances of model predictive control strategies which make use of different forms of linearized models are compared. These linear models are ranging from a simple fixed model, linearized about a reference steady state to a weighted sum of different local models called multi model predictive control. A more complicated choice is represented by the extended dynamic matrix control in which the control input is determined based on the local linear model approximation of the system that is updated during each sampling interval, by making use of a nonlinear model. In this paper, by using and comparing these methods, a new control scheme for quasi-resonant converters is described. The proposed control strategy is applied to a typical half-wave zero-current switching QRC. Simulation results show an excellent transient response and a good tracking for a wide operating range and uncertainties in modeling.

scholarly journals Plant and Controller Optimization for Power and Energy Systems With Model Predictive Control

2021 ◽  
Vol 143 (8) ◽  
Author(s):  
Donald J. Docimo ◽  
Ziliang Kang ◽  
Kai A. James ◽  
Andrew G. Alleyne
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Hybrid Electric Vehicle ◽  
Performance Metrics ◽  
Control Strategies ◽  
Transportation Systems ◽  
Thermal Dynamics ◽  
Plant Optimization ◽  
Power And Energy Systems ◽  
And Control

Abstract This article explores the optimization of plant characteristics and controller parameters for electrified mobility. Electrification of mobile transportation systems, such as automobiles and aircraft, presents the ability to improve key performance metrics such as efficiency and cost. However, the strong bidirectional coupling between electrical and thermal dynamics within new components creates integration challenges, increasing component degradation, and reducing performance. Diminishing these issues requires novel plant designs and control strategies. The electrified mobility literature provides prior studies on plant and controller optimization, known as control co-design (CCD). A void within these studies is the lack of model predictive control (MPC), recognized to manage multi-domain dynamics for electrified systems, within CCD frameworks. This article addresses this through three contributions. First, a thermo-electromechanical hybrid electric vehicle (HEV) powertrain model is developed that is suitable for both plant optimization and MPC. Second, simultaneous plant and controller optimization is performed for this multi-domain system. Third, MPC is integrated within a CCD framework using the candidate HEV powertrain model. Results indicate that optimizing both the plant and MPC parameters simultaneously can reduce physical component sizes by over 60% and key performance metric errors by over 50%.

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