Nonlinear Model Predictive Control Of Combustion Engines
Download Nonlinear Model Predictive Control Of Combustion Engines full books in PDF, epub, and Kindle. Read online free Nonlinear Model Predictive Control Of Combustion Engines ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
| Author | : Thivaharan Albin Rajasingham |
| Publisher | : Springer Nature |
| Total Pages | : 330 |
| Release | : 2021-04-27 |
| Genre | : Technology & Engineering |
| ISBN | : 303068010X |
This book provides an overview of the nonlinear model predictive control (NMPC) concept for application to innovative combustion engines. Readers can use this book to become more expert in advanced combustion engine control and to develop and implement their own NMPC algorithms to solve challenging control tasks in the field. The significance of the advantages and relevancy for practice is demonstrated by real-world engine and vehicle application examples. The author provides an overview of fundamental engine control systems, and addresses emerging control problems, showing how they can be solved with NMPC. The implementation of NMPC involves various development steps, including: • reduced-order modeling of the process; • analysis of system dynamics; • formulation of the optimization problem; and • real-time feasible numerical solution of the optimization problem. Readers will see the entire process of these steps, from the fundamentals to several innovative applications. The application examples highlight the actual difficulties and advantages when implementing NMPC for engine control applications. Nonlinear Model Predictive Control of Combustion Engines targets engineers and researchers in academia and industry working in the field of engine control. The book is laid out in a structured and easy-to-read manner, supported by code examples in MATLAB®/Simulink®, thus expanding its readership to students and academics who would like to understand the fundamental concepts of NMPC. Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.
| Author | : Luigi Del Re |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 291 |
| Release | : 2010-03-11 |
| Genre | : Technology & Engineering |
| ISBN | : 1849960704 |
Automotive control has developed over the decades from an auxiliary te- nology to a key element without which the actual performances, emission, safety and consumption targets could not be met. Accordingly, automotive control has been increasing its authority and responsibility – at the price of complexity and di?cult tuning. The progressive evolution has been mainly ledby speci?capplicationsandshorttermtargets,withthe consequencethat automotive control is to a very large extent more heuristic than systematic. Product requirements are still increasing and new challenges are coming from potentially huge markets like India and China, and against this ba- ground there is wide consensus both in the industry and academia that the current state is not satisfactory. Model-based control could be an approach to improve performance while reducing development and tuning times and possibly costs. Model predictive control is a kind of model-based control design approach which has experienced a growing success since the middle of the 1980s for “slow” complex plants, in particular of the chemical and process industry. In the last decades, severaldevelopments haveallowedusing these methods also for “fast”systemsandthis hassupporteda growinginterestinitsusealsofor automotive applications, with several promising results reported. Still there is no consensus on whether model predictive control with its high requi- ments on model quality and on computational power is a sensible choice for automotive control.
| Author | : Kai Hoffmann |
| Publisher | : |
| Total Pages | : 167 |
| Release | : 2010 |
| Genre | : |
| ISBN | : 9783185172083 |
| Author | : Frank Allgöwer |
| Publisher | : Birkhäuser |
| Total Pages | : 463 |
| Release | : 2012-12-06 |
| Genre | : Mathematics |
| ISBN | : 3034884079 |
During the past decade model predictive control (MPC), also referred to as receding horizon control or moving horizon control, has become the preferred control strategy for quite a number of industrial processes. There have been many significant advances in this area over the past years, one of the most important ones being its extension to nonlinear systems. This book gives an up-to-date assessment of the current state of the art in the new field of nonlinear model predictive control (NMPC). The main topic areas that appear to be of central importance for NMPC are covered, namely receding horizon control theory, modeling for NMPC, computational aspects of on-line optimization and application issues. The book consists of selected papers presented at the International Symposium on Nonlinear Model Predictive Control – Assessment and Future Directions, which took place from June 3 to 5, 1998, in Ascona, Switzerland. The book is geared towards researchers and practitioners in the area of control engineering and control theory. It is also suited for postgraduate students as the book contains several overview articles that give a tutorial introduction into the various aspects of nonlinear model predictive control, including systems theory, computations, modeling and applications.
| Author | : Rolf Findeisen |
| Publisher | : Springer |
| Total Pages | : 644 |
| Release | : 2007-09-08 |
| Genre | : Technology & Engineering |
| ISBN | : 3540726993 |
Thepastthree decadeshaveseenrapiddevelopmentin the areaofmodelpred- tive control with respect to both theoretical and application aspects. Over these 30 years, model predictive control for linear systems has been widely applied, especially in the area of process control. However, today’s applications often require driving the process over a wide region and close to the boundaries of - erability, while satisfying constraints and achieving near-optimal performance. Consequently, the application of linear control methods does not always lead to satisfactory performance, and here nonlinear methods must be employed. This is one of the reasons why nonlinear model predictive control (NMPC) has - joyed signi?cant attention over the past years,with a number of recent advances on both the theoretical and application frontier. Additionally, the widespread availability and steadily increasing power of today’s computers, as well as the development of specially tailored numerical solution methods for NMPC, bring thepracticalapplicabilityofNMPCwithinreachevenforveryfastsystems.This has led to a series of new, exciting developments, along with new challenges in the area of NMPC.
| Author | : Alexandra Grancharova |
| Publisher | : Springer |
| Total Pages | : 241 |
| Release | : 2012-03-22 |
| Genre | : Technology & Engineering |
| ISBN | : 3642287808 |
Nonlinear Model Predictive Control (NMPC) has become the accepted methodology to solve complex control problems related to process industries. The main motivation behind explicit NMPC is that an explicit state feedback law avoids the need for executing a numerical optimization algorithm in real time. The benefits of an explicit solution, in addition to the efficient on-line computations, include also verifiability of the implementation and the possibility to design embedded control systems with low software and hardware complexity. This book considers the multi-parametric Nonlinear Programming (mp-NLP) approaches to explicit approximate NMPC of constrained nonlinear systems, developed by the authors, as well as their applications to various NMPC problem formulations and several case studies. The following types of nonlinear systems are considered, resulting in different NMPC problem formulations: ؠ Nonlinear systems described by first-principles models and nonlinear systems described by black-box models; - Nonlinear systems with continuous control inputs and nonlinear systems with quantized control inputs; - Nonlinear systems without uncertainty and nonlinear systems with uncertainties (polyhedral description of uncertainty and stochastic description of uncertainty); - Nonlinear systems, consisting of interconnected nonlinear sub-systems. The proposed mp-NLP approaches are illustrated with applications to several case studies, which are taken from diverse areas such as automotive mechatronics, compressor control, combustion plant control, reactor control, pH maintaining system control, cart and spring system control, and diving computers.
| Author | : Ferdinando Taglialatela-Scafati |
| Publisher | : Springer |
| Total Pages | : 89 |
| Release | : 2017-10-31 |
| Genre | : Technology & Engineering |
| ISBN | : 3319671405 |
This brief provides an overview on the most relevant nonlinear phenomena in internal combustion engines with a particular emphasis on the use of nonlinear circuits in their modelling and control. The brief contains advanced methodologies —based on neural networks and soft-computing approaches among others— for the compensation of engine nonlinearities by using the combustion pressure signal and proposes several techniques for the reconstruction of this signal on the basis of different engine parameters, including engine-block vibration and crankshaft rotational speed. Another topic of the book is the diagnosis of the nonlinearities of injection systems and their balancing, which is a mandatory task for the new generation of gasoline direct injection engines. The authors come from both industrial and academic backgrounds, so the brief represents an important tool both for researchers and practitioners in the automotive industry.
| Author | : |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2023 |
| Genre | : |
| ISBN | : |
Abstract : Low temperature combustion (LTC) offers high thermal efficiency and low engine-out nitrogen oxides (NOx) and particulate matter (PM) emissions. Homogeneous charge compression ignition (HCCI), partially premixed charge compression ignition (PPCI) and reactivity-controlled compression ignition (RCCI) are the common LTC modes studied in this research. The primary barrier to implementing the LTC modes in on-road vehicles is their limited operating range due to high cyclic variability and excessive pressure rise rates. The feasible operating range of the LTC modes is only a subset of the speed-load range of the conventional spark ignition (SI) engine. Therefore, a multi-mode engine concept operating in one or more LTC modes and SI mode is a viable option to improve engine performance in terms of efficiency and emissions. The goal of this dissertation is to develop model-based closed loop control of an SI-RCCI-SI multi-mode engine. Control-oriented models and predictive controllers for HCCI, PPCI and RCCI modes are developed to simultaneously control combustion phasing and engine load for an optimal operation of a multi-mode engine. Cyclic variability in HCCI and RCCI modes are modeled using machine learning classification algorithms. Nonlinear model predictive controllers are developed for HCCI and RCCI modes to control combustion phasing and engine load while constraining cyclic variability below 3%. Furthermore, LTC engine operation faces challenges of excessive pressure rise rates that can damage the hardware. To this end, supervised machine learning classification algorithms are developed to model the heat release type which is used as a scheduling variable to develop data-driven model for an LTC engine. Model predictive controller is then developed to control combustion phasing and engine load while constraining maximum pressure rise rate below 8 bar/CAD. RCCI mode offers good control over the combustion event by modulating the start of injection timing of high reactivity fuel and adjusting the premixed ratio of the dual fuels. Therefore, this research focuses on SI-RCCI-SI multi-mode engine concept. The aim of this research is to achieve smooth SI-RCCI-SI mode switching operation at different engine loads and speed. A dynamic model for SI-RCCI-SI multi-mode engine is developed and validated for different transient conditions. The model includes the mode switching dynamics as well as actuator dynamics. A model-based predictive controller framework is developed for SI-RCCI-SI mode switching. The mode switching controller showed good performance during mode transitions and steady state engine operation. The controller is capable of tracking the desired combustion phasing and engine load during mode switching while maintaining $\lambda$ near stoichiometry in SI mode and constraining maximum pressure rise rate below 8 bar/CAD in RCCI mode.
| Author | : Lars Grüne |
| Publisher | : Springer |
| Total Pages | : 463 |
| Release | : 2016-11-09 |
| Genre | : Technology & Engineering |
| ISBN | : 3319460242 |
This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. The second edition has been substantially rewritten, edited and updated to reflect the significant advances that have been made since the publication of its predecessor, including: • a new chapter on economic NMPC relaxing the assumption that the running cost penalizes the distance to a pre-defined equilibrium; • a new chapter on distributed NMPC discussing methods which facilitate the control of large-scale systems by splitting up the optimization into smaller subproblems; • an extended discussion of stability and performance using approximate updates rather than full optimization; • replacement of the pivotal sufficient condition for stability without stabilizing terminal conditions with a weaker alternative and inclusion of an alternative and much simpler proof in the analysis; and • further variations and extensions in response to suggestions from readers of the first edition. Though primarily aimed at academic researchers and practitioners working in control and optimization, the text is self-contained, featuring background material on infinite-horizon optimal control and Lyapunov stability theory that also makes it accessible for graduate students in control engineering and applied mathematics.
| Author | : Basil Kouvaritakis |
| Publisher | : IET |
| Total Pages | : 277 |
| Release | : 2001-10-26 |
| Genre | : Mathematics |
| ISBN | : 0852969848 |
The advantage of model predictive control is that it can take systematic account of constraints, thereby allowing processes to operate at the limits of achievable performance. Engineers in academia, industry, and government from the US and Europe explain how the linear version can be adapted and applied to the nonlinear conditions that characterize the dynamics of most real manufacturing plants. They survey theoretical and practical trends, describe some specific theories and demonstrate their practical application, derive strategies that provide appropriate assurance of closed-loop stability, and discuss practical implementation. Annotation copyrighted by Book News, Inc., Portland, OR
