Online Optimizing Positioning Control With Model Error Compensator for LEGRV System

In this article, a precision positioning control approach of a linear exhaust gas recirculation valve (LEGRV) system using online optimization technique is proposed. As both dynamics and nonlinear factors, including hysteretic features, are inherent in LEGRV systems, the modeling of system is not an easy process. In order to avoid the complex obstacle of modeling, in this method, a simplified model, for e.g., linear or linearized dynamic model, is developed to approximate the essential motion feature of pintle position of the linear valve. For compensating the effect of model uncertainty caused by unmodeled nonlinear and dynamic factors, a model error compensator (MEC) is introduced. Then, an online optimizing positioning control (OPC) scheme is applied to the control of pintle position of valve. In this control scheme, the estimated gradient of MEC with respect to control variable is also included in the control strategy to suppress the influence of model uncertainty. Considering the effect of nonsmoothness of hysteresis inherent in the system, a nonsmooth optimization-search method based on the technique of generalized gradient is proposed. Afterward, the stability analysis of the OPC+MEC system is presented. Finally, the experimental results to evaluate the performance of proposed control method are presented.

[1]  Wei Sun,et al.  RBF Networks-Based Adaptive Inverse Model Control System for Electronic Throttle , 2010, IEEE Transactions on Control Systems Technology.

[2]  J. Doyle,et al.  Essentials of Robust Control , 1997 .

[3]  Robert Prucka,et al.  Nonlinear model predictive air path control for turbocharged SI engines with low pressure EGR and a continuous surge valve , 2017, 2017 American Control Conference (ACC).

[4]  Xinkai Chen,et al.  Adaptive Control for Plants in the Presence of Actuator and Sensor Uncertain Hysteresis , 2011, IEEE Transactions on Automatic Control.

[5]  R.H. Lasseter,et al.  Microgrid: a conceptual solution , 2004, 2004 IEEE 35th Annual Power Electronics Specialists Conference (IEEE Cat. No.04CH37551).

[6]  Jung-Hyo Lee,et al.  Low Cost Position Controller for Exhaust Gas Recirculation Valve System , 2018 .

[7]  Mohammad Al Janaideh,et al.  Further results on open-loop compensation of rate-dependent hysteresis in a magnetostrictive actuator with the Prandtl-Ishlinskii model , 2018 .

[8]  Xiang Chen,et al.  Model-Guided Data-Driven Optimization for Automotive Compression Ignition Engine Systems , 2019 .

[9]  Yaonan Wang,et al.  SVM-Based Approximate Model Control for Electronic Throttle Valve , 2008, IEEE Transactions on Vehicular Technology.

[10]  Xiang Chen,et al.  Wiener structure based model identification for an electronic throttle body , 2017, 2017 13th IEEE International Conference on Control & Automation (ICCA).

[11]  Mohd Hanif Mohd Ramli,et al.  A new phenomenological-based rate-dependent hysteresis operator for hysteresis characterization , 2018 .

[12]  Brian D. O. Anderson,et al.  Challenges of adaptive control-past, permanent and future , 2008, Annu. Rev. Control..

[13]  Klaus Janschek,et al.  Nonsmooth Predictive Control for Wiener Systems With Backlash-Like Hysteresis , 2016, IEEE/ASME Transactions on Mechatronics.

[14]  Wang Yaonan,et al.  RBF Networks-Based Adaptive Inverse Model Control System for Electronic Throttle , 2010, IEEE Transactions on Control Systems Technology.

[15]  Yonghong Tan,et al.  Nonlinear Internal Model Control of EGR Valve* , 2019, 2019 12th Asian Control Conference (ASCC).

[16]  Jang-Mok Kim,et al.  Improvement of position control performance of EGR valve system with low control frequency , 2017, 2017 IEEE 3rd International Future Energy Electronics Conference and ECCE Asia (IFEEC 2017 - ECCE Asia).

[17]  O M Braun,et al.  Transition from smooth sliding to stick-slip motion in a single frictional contact. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[18]  Mayergoyz,et al.  Mathematical models of hysteresis. , 1986, Physical review letters.

[19]  Bojana Drincic,et al.  Mechanical Models of Friction That Exhibit Hysteresis, Stick-Slip, and the Stribeck Effect. , 2012 .

[20]  Carlos Sagues,et al.  Hybrid Dynamical Model for Reluctance Actuators Including Saturation, Hysteresis, and Eddy Currents , 2019, IEEE/ASME Transactions on Mechatronics.

[21]  Hui Chen,et al.  A neural networks based model for rate-dependent hysteresis for piezoceramic actuators , 2008 .

[22]  Ken Butts,et al.  A cascaded economic model predictive control strategy for a diesel engine using a non-uniform prediction horizon discretization , 2017, 2017 IEEE Conference on Control Technology and Applications (CCTA).

[23]  Paolo Mercorelli,et al.  A switching model predictive control for overcoming a hysteresis effect in a hybrid actuator for camless internal combustion engines , 2011, 2011 Workshop on Predictive Control of Electrical Drives and Power Electronics.

[24]  M. Bazrafshan,et al.  The effect of adhesion and roughness on friction hysteresis loops , 2019, International Journal of Mechanical Sciences.

[25]  Y. F. Liu,et al.  Experimental comparison of five friction models on the same test-bed of the micro stick-slip motion system , 2015 .

[26]  L. Nicolas,et al.  Sliding mode control for Turbocharged Diesel Engine , 2012, 2012 20th Mediterranean Conference on Control & Automation (MED).

[27]  Xiang Chen,et al.  Model-Guided Extremum Seeking for Diesel Engine Fuel Injection Optimization , 2018, IEEE/ASME Transactions on Mechatronics.

[28]  Johan Lindberg,et al.  Air-Path Model Predictive Control of a Heavy-Duty Diesel Engine with Variable Valve Actuation , 2014 .

[29]  Xiang Chen,et al.  Exhaust gas recirculation control through extremum seeking in a Low Temperature Combustion diesel engine , 2014, 2014 American Control Conference.

[30]  A. Visintin Models of hysteresis , 1993 .

[31]  L. Guzzella,et al.  Model-based feedback control of the air-to-fuel ratio in diesel engines based on an empirical model , 2006, 2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control.