Multicylinder HCCI Control With Coupled Valve Actuation Using Model Predictive Control

2013 ◽  
Vol 135 (5) ◽  
Author(s):  
Stephen M. Erlien ◽  
Adam F. Jungkunz ◽  
J. Christian Gerdes
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Valve Timing ◽  
Test Bed ◽  
Time Step ◽  
Hcci Engine ◽  
Prediction Time ◽  
Near Term ◽  
Effective Use ◽  
Variable Valve

Recent work in homogeneous charge compression ignition (HCCI) engine control has focused on the use of variable valve timing (VVT) as a near term implementation strategy. Valve timing has a significant influence on combustion phasing and can be implemented with cam-based VVT systems already available in production vehicles. However, these systems introduce cylinder coupling via a shared actuator. This paper presents a model predictive control (MPC) framework that explicitly accounts for this intercylinder coupling as a constraint on the system. The execution time step of this MPC controller is shorter than the prediction time step, enabling consideration of a common actuator across otherwise independent systems as the engine cycle progresses. This enables effective use of the cylinder independent actuators to augment the shared actuator in achieving the control objectives. Experiments on a multicylinder HCCI engine test bed validate this approach to handling coupled actuation and illustrate effective use of cylinder independent actuators in response to limited capabilities of the shared actuator.

A Multi-Cylinder HCCI Engine Model for Control

2004 ◽  
Author(s):  
Jason S. Souder ◽  
Parag Mehresh ◽  
J. Karl Hedrick ◽  
Robert W. Dibble
Keyword(s):  
Design Process ◽  
Control Design ◽  
The Other ◽  
Variable Valve Timing ◽  
Valve Timing ◽  
Coupling Effects ◽  
Hcci Engine ◽  
Engine Model ◽  
Hcci Engines ◽  
Variable Valve

Homogeneous charge compression ignition (HCCI) engines are a promising engine technology due to their low emissions and high efficiencies. Controlling the combustion timing is one of the significant challenges to practical HCCI engine implementations. In a spark-ignited engine, the combustion timing is controlled by the spark timing. In a Diesel engine, the timing of the direct fuel injection controls the combustion timing. HCCI engines lack such direct in-cylinder mechanisms. Many actuation methods for affecting the combustion timing have been proposed. These include intake air heating, variable valve timing, variable compression ratios, and exhaust throttling. On a multi-cylinder engine, the combustion timing may have to be adjusted on each cylinder independently. However, the cylinders are coupled through the intake and exhaust manifolds. For some of the proposed actuation methods, affecting the combustion timing on one cylinder influences the combustion timing of the other cylinders. In order to implement one of these actuation methods on a multi-cylinder engine, the engine controller must account for the cylinder-to-cylinder coupling effects. A multi-cylinder HCCI engine model for use in the control design process is presented. The model is comprehensive enough to capture the cylinder-to-cylinder coupling effects, yet simple enough for the rapid simulations required by the control design process. Although the model could be used for controller synthesis, the model is most useful as a starting point for generating a reduced-order model, or as a plant model for evaluating potential controllers. Specifically, the model includes the dynamics for affecting the combustion timing through exhaust throttling. The model is readily applicable to many of the other actuation methods, such as variable valve timing. Experimental results validating the model are also presented.

scholarly journals Data–Driven Indirect Adaptive Model Predictive Control

2012 ◽  
Author(s):  
Norhaliza Wahab ◽  
Mohamed Reza Katebi ◽  
Mohd Fua’ad Rahmat ◽  
Salinda Bunyamin
Keyword(s):  
Model Predictive Control ◽  
Activated Sludge ◽  
State Space ◽  
Predictive Control ◽  
Sliding Window ◽  
Subspace Identification ◽  
Activated Sludge Process ◽  
Adaptive Model ◽  
Time Step ◽  
Adaptive Model Predictive Control

Kertas kerja ini membincangkan tentang reka bentuk Pengawal Ramalan Model Suai menggunakan kaedah Pengenalpastian Model Keadaan Ruang Sub–ruang bagi proses enapcemar teraktif. Penggunaan teknik Pengenalpastian Model Keadaan Ruang Sub–ruang di dalam kaedah kawalan tingkat gelangsar suai dibincangkan di mana pengenalpastian sub–ruang dalam talian menggunakan algoritma N4SID di perkenalkan bersama dengan rekabentuk Pengawal ramalan model. Pembangunan N4SID dalam talian di dalam kertas kerja ini menggunakan pengemaskini QR di mana gabungan di antara teknik kemaskini dan kemasbawah membolehkan pengadaptasi tingkap gelangsar. Di sini, untuk setiap langkah masa, bagi setiap data baru akan dimasukkan ke faktor R manakala data yang lama dibuang. Begitu juga, strategi bagi uraian nilai tunggal diperkenalkan ke dalam Pengawal Ramalan Model Suai tak langsung untuk masukan tambahan kawalan bagi sistem terkekang tak lelurus. Beberapa kajian simulasi bagi parameter kawalan berlainan di dalam pengawal/pengenalpastian algoritma dilaksanakan. Bagi reka bentuk Pengawal Ramalan Model Suai tak langsung, pengiraan masa yang terlibat dengan menggunakan pendekatan uraian nilai tunggal kurang berbanding dengan kaedah perancangan kuadratik dan keputusan yang memberangsangkan ini adalah sumbangan utama di dalam kertas kerja ini. Kata kunci: Pengawal suai; proses enapcemar teraktif; pengawal ramalan model; pengenalpastian sub–ruang This paper explores the design of Adaptive Model Predictive Control (AMPC) using Subspace State–space Model Identification (SMI) techniques for an activated sludge process. The implementation of SMI techniques in the adaptive sliding window control methods are discussed where the online subspace identification using Numerical State–space Subspace System Identification (N4SID) algorithm is proposed along with Model Predictive Control (MPC) design method. The online N4SID algorithm developed in this study makes use of the QR–updating where the combination of update and down date techniques enables sliding window adaptation. Here, at each time step, for the new experimental data added into R factor, the oldest data are removed. Also, the Singular Value Decomposition (SVD–based) strategy is proposed into Indirect AMPC (IAMPC) for the control increment input constrained nonlinear system. Several simulation studies for different control parameters in control/identification algorithm are performed. For the IAMPC control design, the computational times involved using an SVD approach shows less burdensome compared to Quadratic Programming (QP) method and such an interesting result is considered as one of the main contribution in this paper. Key words: Adaptive control; activated sludge process; model predictive control; subspace identification

Nonlinear model predictive control of a discrete-cycle gasoline-controlled auto ignition engine model: Simulative analysis

2019 ◽  
Vol 20 (10) ◽  
pp. 1025-1036 ◽  
Author(s):  
Eugen Nuss ◽  
Maximilian Wick ◽  
Jakob Andert ◽  
Jochem De Schutter ◽  
Moritz Diehl ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Measurement Data ◽  
Nonlinear Model Predictive Control ◽  
Test Bed ◽  
Efficient Operation ◽  
Stable Combustion ◽  
Engine Model ◽  
Auto Ignition

Gasoline-controlled auto ignition is a promising technology capable of reducing both fuel consumption and emissions at the same time. There are, however, challenges to overcome in order to make practical use of it. One area of research addresses methods that guarantee stable combustion as gasoline-controlled auto ignition is very sensitive to disturbances. This article investigates the capability of nonlinear model predictive control to ensure stable combustion while maintaining efficient operation. For this purpose, a suitable gasoline-controlled auto ignition model is selected and identified using measurement data of a single-cylinder test bed. Building upon this model, a controller based on nonlinear model predictive control is derived and analyzed by means of simulation. The investigation shows that the control manages to follow prescribed set points, also for late combustion, and indicates promising results with respect to real-time computation constraints.

Control Development for Smoke Reduction Through Inlet Manifold Air Injection During Transient Loading of Marine Diesel Engines

2009 ◽  
Author(s):  
G. Papalambrou ◽  
N. P. Kyrtatos
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Air Injection ◽  
Intake Manifold ◽  
Test Bed ◽  
Engine Operation ◽  
Marine Diesel ◽  
Test Engine ◽  
Inlet Manifold ◽  
Intake Manifold Pressure

This paper addresses the reduction of smoke emissions and improvement of load acceptance in a turbocharged marine diesel engine, during transient operation involving rapid load increases. Model Predictive Control (MPC) provided the optimal quantity of injected air in the engine while minimizing smoke density (opacity), with constraint not to exceed a limit in intake manifold pressure, in order to avoid surge in the compressor. System identification methods were used to determine control models at various operating points of the engine. Transient response experiments were performed on a full-scale marine diesel test engine on a transient test bed, using real-time MPC configuration. Results comparing the opacity under air injection model predictive control with the standard engine operation without air injection, during the same transient, show reduction in opacity level while avoiding surge.

scholarly journals Multi-objective model predictive control for microgrids

2020 ◽  
Vol 68 (8) ◽  
pp. 687-702
Author(s):  
Thomas Schmitt ◽  
Tobias Rodemann ◽  
Jürgen Adamy
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Pareto Front ◽  
Optimization Problem ◽  
German Industry ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Time Step ◽  
Economic Model Predictive Control ◽  
Objective Model

AbstractEconomic model predictive control is applied to a simplified linear microgrid model. Monetary costs and thermal comfort are simultaneously optimized by using Pareto optimal solutions in every time step. The effects of different metrics and normalization schemes for selecting knee points from the Pareto front are investigated. For German industry pricing with nonlinear peak costs, a linear programming trick is applied to reformulate the optimization problem. Thus, together with an efficient weight determination scheme, the Pareto front for a horizon of 48 steps is determined in less than 4 s.

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