Robust air/fuel ratio control with adaptive DRNN model and AD tuning

Zhai, Yujia; Yu, Ding-Wen; Guo, Hongyu; Yu, Dingli

doi:10.1016/j.engappai.2009.12.006

Cited by 30 publications

(43 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the development of modern micro-processors, it has been possible to solve the optimization problems associated with MPC online effectively, which makes MPC applicable to systems with fast dynamics (Wang et al, 2010;Zhai et al, 2010). Many researchers utilized linear MPC to control helicopter systems (Witt et al, 2007;Maia et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Model predictive control of a 3-DOF helicopter system using successive linearization

Zhai¹,

Nounou²,

Nounou³

et al. 2011

Int. J. Eng. Sci. Tech

View full text Add to dashboard Cite

Helicopter dynamics are in general nonlinear, time-varying, and may be highly uncertain. Traditional control schemes such as proportional-integral-derivative (PID) control, linear quadratic regulator (LQR), and eigen-structure assignment are usually not effective when a linearized model is used and the helicopter moves away from the design trim point. This paper presents a nonlinear model predictive control (NMPC) method to control the elevation and travel of a three degree of freedom (DOF) laboratory helicopter using successive linearization to approximate the internal model of the system. The developed algorithm is evaluated by simulation, and its performance is compared with that achieved by linear model predictive control (LMPC).

show abstract

Section: Introductionmentioning

confidence: 99%

Model predictive control of a 3-DOF helicopter system using successive linearization

Zhai¹,

Nounou²,

Nounou³

et al. 2011

Int. J. Eng. Sci. Tech

View full text Add to dashboard Cite

show abstract

“…So far, there are only a few papers focusing on airratio control, but some control strategies were developed for air-fuel ratio (AFR) control in the past decade, including the sliding mode control [4], radial basis function neuralnetwork feed-forward feedback control [5], and model predictive control (MPC) using neural network-based models [6][7][8]. In the aforementioned researches, the most appropriate and the latest technique is MPC based on diagonal recurrent neural network (DRNN) [8] because of its fast computational time.…”

Section: Introductionmentioning

confidence: 99%

“…In the aforementioned researches, the most appropriate and the latest technique is MPC based on diagonal recurrent neural network (DRNN) [8] because of its fast computational time. The MPC is very robust and suitable for a multivariable, time-varying, and delay system that matches the characteristic of modern engine AFR control systems [9].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Model Predictive Engine Air-Ratio Control Using Online Sequential Relevance Vector Machine

Wong

Vong

2012

Journal of Control Science and Engineering

View full text Add to dashboard Cite

Engine power, brake-specific fuel consumption, and emissions relate closely to air ratio (i.e., lambda) among all the engine variables. An accurate and adaptive model for lambda prediction is essential to effective lambda control for long term. This paper utilizes an emerging technique, relevance vector machine (RVM), to build a reliable time-dependent lambda model which can be continually updated whenever a sample is added to, or removed from, the estimated lambda model. The paper also presents a new model predictive control (MPC) algorithm for air-ratio regulation based on RVM. This study shows that the accuracy, training, and updating time of the RVM model are superior to the latest modelling methods, such as diagonal recurrent neural network (DRNN) and decremental least-squares support vector machine (DLSSVM). Moreover, the control algorithm has been implemented on a real car to test. Experimental results reveal that the control performance of the proposed relevance vector machine model predictive controller (RVMMPC) is also superior to DRNNMPC, support vector machine-based MPC, and conventional proportionalintegral (PI) controller in production cars. Therefore, the proposed RVMMPC is a promising scheme to replace conventional PI controller for engine air-ratio control.

show abstract

“…However, sliding mode controllers suffer from the chattering issue, where the oscillations may adversely affect the conversion efficiency of the TWCCs. Model predictive controllers were also designed in combination with a diagonal recurrent neural network (DRNN) [8] and using a relevance vector machine (RVM) technique [9].…”

mentioning

confidence: 99%

Adaptive air-fuel ratio control for spark ignition engines with time-varying parameter estimation

Chen

Herrmann

et al. 2017

2017 9th International Conference on Modelling, Identification and Control (ICMIC)

View full text Add to dashboard Cite

Abstract-This paper presents a novel adaptive controller of air-fuel ratio (AFR) in spark ignition (SI) engines. The controller robustly estimates unknown time-varying engine parameters and thus improves both the transient and steady-state performance. The objective is to regulate the AFR in the combustion chamber around the stoichiometric value by manipulating the injected fuel mass flow rate so as to improve fuel economy and to reduce emissions. The AFR regulation problem is first reformulated into a tracking control problem of the fuel mass flow. This simplifies the control synthesis, i.e. the number of parameters to be online updated can be reduced. Then a representation of the parameter estimation error is derived by using auxiliary filter operations, which is used as a new leakage term in the adaptive law. In this case, exponential convergence of the AFR error and the estimation of the time-varying parameters can be proved simultaneously. The proposed controller is compared with a generic adaptive controller using the gradient descent method based on a well-calibrated mean value engine model (MVEM). Finally, the proposed controller is also validated with a commercial engine simulation software, GT-Power, demonstrating better results for the suggested adaptive controller than for the gradient descent approach.

show abstract

Robust air/fuel ratio control with adaptive DRNN model and AD tuning

Cited by 30 publications

References 15 publications

Model predictive control of a 3-DOF helicopter system using successive linearization

Model predictive control of a 3-DOF helicopter system using successive linearization

Model Predictive Engine Air-Ratio Control Using Online Sequential Relevance Vector Machine

Adaptive air-fuel ratio control for spark ignition engines with time-varying parameter estimation

Contact Info

Product

Resources

About