Multivariable PI tuning for disturbance rejection and application to engine idle speed control simulation

Gangopadhyay, Anupam; Meckl, Peter H.

doi:10.1080/00207170110049486

Cited by 12 publications

(6 citation statements)

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“…[16,17,18]. Multivariable PI controllers are designed for and tested with a linearized model in [14]. A sliding mode controller for both control inputs is proposed in [19].…”

Section: A Mimo Control Frameworkmentioning

confidence: 99%

“…Often, the throttle command input is seen as the only control input, and the spark command input is not considered [2,[8][9][10][11][12]. The air-fuel ratio has been used in [13,14] as a control input although this is unusual for a spark-ignited engine, since emissions requirements demand stoichiometric mixture in most operation conditions. Due to this, the suitability of these models for control design within modern engine management systems is limited.…”

Section: Introductionmentioning

confidence: 99%

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A nonlinear model for design and simulation of automotive idle speed control strategies

Bøhn

Böhme

Staate

et al. 2006

2006 American Control Conference

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A nonlinear model of a spark-ignited internal combustion engine for the design and simulation of idle speed controllers is presented. Due to the torque-based software architecture of modern engine control systems, the model incorporates the underlying physical relationships as well as parts of the engine control software functions. All parameters for the engine model are provided. The control inputs are the throttle command signal and the spark advance control signal. As output signals, the engine speed and the torque reserve are used. The suitability of this model for controller design is shown through an exemplary MIMO H design.

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“…[16,17,18]. Multivariable PI controllers are designed for and tested with a linearized model in [14]. A sliding mode controller for both control inputs is proposed in [19].…”

Section: A Mimo Control Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A nonlinear model for design and simulation of automotive idle speed control strategies

Bøhn

Böhme

Staate

et al. 2006

2006 American Control Conference

View full text Add to dashboard Cite

show abstract

“…Although this is not the same problem, some of the control methods can also be employed in speed tracking control; for example, traditional PID control. 3,4 There are, however, newer idle speed control strategies, such as fuzzy control 5,6 and linear quadratic (LQ) control. 7,8 The biggest weakness of the PID and fuzzy control methods is that they cannot deal with simultaneous multiple targets, such as speed tracking response and fuel economy.…”

Section: Introductionmentioning

confidence: 99%

Design of a speed tracking controller for heavy-duty vehicles with an all-speed governor based on a model predictive control strategy

Jiang

Huang

et al. 2017

International Journal of Engine Research

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In this article, a model predictive control strategy is presented for an all-speed governor of heavy-duty vehicles that satisfies the requirements of fast tracking and fuel economy. The control-oriented torque-based engine model is used for the design of a model predictive control-based speed tracking control algorithm. Two methods for improving the speed tracking and fuel economy synthesis are presented, which include engine load estimation and variable weighting factor. The engine speed and fuel mass are used to estimate the real-time engine torque. The variable weighting factor based on the driver's intention is used to adjust the control algorithm in MATLAB/Simulink. The simulation results show that the tracking performance and fuel economy of the model predictive controller are better than that of a proportional-integral-derivative controller.

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“…Fuzzy rules simulate the though of mankind, so it can deal with complex system, even ill system. Classical control methods [3] are suitable for linear system. For the aeroengine is a complex and strong nonlinear system, classical control has hardly satisfied the control requirements.…”

Section: Introductionmentioning

confidence: 99%

Study of fuzzy control with smith forecast and compensation for aeroengine

Zhang

2010

The 2010 IEEE International Conference on Information and Automation

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It is a difficult problem to build the aeroengine model. And it is hard to meet aeroengine control demand by traditional methods. So according these problems, aeroengine models were built including "little deflection" model and "large deflection" models. A fuzzy controller was designed which had the parts of parameter adjustment control and smith forecast and compensation control. The simulation of aeroengine control system which considering actuation mechanism and time delay was carried out. The simulation results show that smith forecast and compensation control can effectively solve time delay influence. The control system has great robustness with the part of parameter adjustment control. The control effect is great for "little deflection" model and acceleration model.

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Multivariable PI tuning for disturbance rejection and application to engine idle speed control simulation

Cited by 12 publications

References 0 publications

A nonlinear model for design and simulation of automotive idle speed control strategies

A nonlinear model for design and simulation of automotive idle speed control strategies

Design of a speed tracking controller for heavy-duty vehicles with an all-speed governor based on a model predictive control strategy

Study of fuzzy control with smith forecast and compensation for aeroengine

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