Titlebook: Nonlinear Model Predictive Control of Combustion Engines; From Fundamentals to Thivaharan Albin Rajasingham Book 2021 Springer Nature Switz

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Linear Model Predictive Controltates along with a quadratic cost function and linear constraints. This chapter presents an overview of various aspects of LMPC. First, the unconstrained LMPC will be explained, including the solution of the optimization problem and the resulting control structure. Based on these results, the extens

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Nonlinear Model Predictive Controll predictive control (LMPC) method. For engine control problems, the consideration of their nonlinear process behavior is particularly important as their treatment as a linearized system is often not sufficient. In this chapter, the fundamentals of NMPC are presented. A special focus is set on the r

從容

多產(chǎn)子

SI and CI Engine Control Architecturesengine control is given. A suitable architecture is presented which can be used to handle the demanding requirements on process control. By the use of hierarchization and modularization, the complex interaction of the various components can be tackled. For both the SI and the CI engine, a typical ha

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Low-Temperature Combustion Engine Controlative combustion concept for use in engine systems. They hold promise to lower pollutant emissions while achieving a high efficiency. The working principle is based on the autoignition of a highly premixed charge. These properties come along with increased demands for the process control. In this ch

網(wǎng)絡(luò)添麻煩

Fundamentals of Control-Oriented Air Path Modelingiven. To realize appropriate real-time optimization, a suitable trade-off is needed between the accuracy of a model and its computational complexity. Additionally, if gradient-based methods are applied for a numerical solution of the optimization problem, models are required that are continuously di

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