An Explicit Model Predictive Control Framework for Turbocharged Diesel Engines

Zhao, Dezong; Liu, Cunjia; Stobart, Richard; Deng, Jiamei; Winward, Edward; Dong, Guangyu

doi:10.1109/tie.2013.2279353

Cited by 56 publications

(43 citation statements)

References 38 publications

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“…Because of the nonlinear behavior over the wide operating region of a diesel engine (engine speed from 800 to 4500 rpm), it is not possible to obtain a single linear model with high precision. The engine operation region is then segregated into several subzones . Within each subzone, a linear model is identified.…”

Section: Modelingmentioning

confidence: 99%

Offset‐free model predictive control of diesel engine by combined design of disturbance model and observer

Ogai

Deng

2018

IEEJ Transactions Elec Engng

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The diesel engine is a typical multi‐input multi‐output system with strong couplings, actuator constraints, and fast dynamics. The control objective is to operate the engine to meet driver's speed demand and reduce exhaust emissions of nitrogen oxides during transient processes. Interactions between the actuators and nonlinear behavior of the system make the problem difficult to handle using classical control design methods. Therefore, we propose an offset‐free model predictive control system in this paper. It is based on the combined design integrating the disturbance model and the state observer. Furthermore, the method of reduced‐order design for H∞ problem is addressed. The proposed approach has low computation requirement and is suitable for implementation in the engine control unit on board. The application of this system in a continuous working process by using dSPACE MicroAutoBox verifies its feasibility and effectiveness for achieving precise reference tracking of engine speed and reducing emissions. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Section: Modelingmentioning

confidence: 99%

Offset‐free model predictive control of diesel engine by combined design of disturbance model and observer

Ogai

Deng

2018

IEEJ Transactions Elec Engng

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“…An explicit MPC framework is introduced in Ref. . The diesel engine air path and fuel path are modeled as linear systems in the form of discrete state‐space equations.…”

Section: Introductionmentioning

confidence: 99%

Extended Guaranteed Cost Control of Diesel Engine via LMI Approach

Ogai

Deng

2018

IEEJ Transactions Elec Engng

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This paper presents a control design method for a nonlinear diesel engine system with strong couplings and actuator constraints. The performance goal is to track the reference of engine speed and reduce exhaust emission of nitrogen oxides (NOx) during transient process. Combustion uncertainties and nonlinear behavior of the system make the problem difficult to handle using classical control design methods. Instead, we propose the extended guaranteed cost control system. The models used for controller design are developed by the state-space format with parameter uncertainties. Exponential stability for the systems is studied. The control laws and observer gains are given from feasible solution of the augmented linear matrix inequality (LMI) to ensure that the cost function value is not more than a specified upper bound for all admissible uncertainties. The proposed approach has low computation requirement and is suitable for implementation in the on board. engine control unit The application of this system in a continuous working process by using dSPACE MicroAutoBox verified its feasibility and effectiveness.

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“…with the cost function (6) where N p , N c are prediction horizon and control horizon with satisfying N c ≤ N p . In order to distinguish clearly between the real system and predicted system, the internal controller variables of the predicted system are denoted by a bar "-".…”

Section: Introductionmentioning

confidence: 99%

Fast Nonlinear Model Predictive Control on FPGA Using Particle Swarm Optimization

Chen

Gong

et al. 2016

IEEE Trans. Ind. Electron.

129

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Nonlinear model predictive control (NMPC) requires repeated online solution of a nonlinear optimal control problem. The computation load remains the main challenge for the real-time practical application of NMPC technique especially for fast systems. This paper presents a fast NMPC algorithm implemented on field programmable gate array (FPGA) that employs a particle swarm optimization (PSO) algorithm to handle the nonlinear optimization. The FPGA is used to explore the possibilities of parallel architecture for the substantial acceleration of NMPC. The PSO is employed to achieve real-time operation due to its naturally parallel capabilities. The proposed FPGA-based NMPC-PSO controller consists of random number generator, fixed-point arithmetic, PSO solver and universal asynchronous receiver/transmitter (UART) communication interface. Then this controller is applied to an engine idle speed control (ISC) problem and demonstrated with an FPGA-in-the-loop testbench. The experimental results indicate that the NMPC-on-FPGAchip strategy has good computational performance and achieves satisfactory control performance.Index Terms-Nonlinear model predictive control, field programmable gate array, particle swarm optimization, engine idle speed control.

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An Explicit Model Predictive Control Framework for Turbocharged Diesel Engines

Cited by 56 publications

References 38 publications

Offset‐free model predictive control of diesel engine by combined design of disturbance model and observer

Offset‐free model predictive control of diesel engine by combined design of disturbance model and observer

Extended Guaranteed Cost Control of Diesel Engine via LMI Approach

Fast Nonlinear Model Predictive Control on FPGA Using Particle Swarm Optimization

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