Design of active suspension controller for train cars based on sliding mode control, uncertainty observer and neuro-fuzzy system

This paper focuses on building a controller for active suspension system of train cars in the case that the sprung mass and model error are uncertainty parameters. The sprung mass is always varied due to many reasons such as changing of the passengers and load or impacting of wind on the operating train while an unknown difference between the suspension model used for survey and the real suspension system also always exists. The controller is built based on an adaptive neuro-fuzzy inference system (ANFIS), sliding mode control, uncertainty observer (NFSmUoC) and a magnetorheological damper (MRD) which can be seen as an actuator for applying active force. A nonlinear uncertainty observer (NUO), a sliding mode controller (SMC) together with an inverse model of the MRD are designed in order to calculate the current value by which the MRD creates the required active control force u(t). An ANFIS and measured MR-damper-dynamic-response data sets are used to identify the MRD as an inverse MRD model (ANFIS-I-MRD). Based on dynamic response of the suspension, firstly the active control force u(t) is calculated by NUO and SMC, in which the impact of the uncertainty load on the system is estimated by the NUO. The ANFIS-I-MRD is then used to estimate applied current for the MRD in order to create the calculated active control force to control vertical vibration status of the train cars. Simulation surveys are carried out to evaluate the effectiveness of the proposed method.

[1]  J. Mendel Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions , 2001 .

[2]  An-Chyau Huang,et al.  Adaptive sliding control of non-autonomous active suspension systems with time-varying loadings , 2005 .

[3]  V. Garg Chapter 4 – Wheel–Rail Rolling Contact Theories , 1984 .

[4]  V K Garg,et al.  Dynamics of railway vehicle systems , 1984 .

[5]  Seung-Bok Choi,et al.  A novel minimum–maximum data-clustering algorithm for vibration control of a semi-active vehicle suspension system , 2013 .

[6]  Hasan Alli,et al.  Neural based sliding-mode control with moving sliding surface for the seismic isolation of structures , 2011 .

[7]  I. Ballo,et al.  Comparison of the properties of active and semiactive suspension , 2007 .

[8]  Shiuh-Jer Huang,et al.  Functional approximation-based adaptive sliding control with fuzzy compensation for an active suspension system , 2005 .

[9]  V. Utkin Variable structure systems with sliding modes , 1977 .

[10]  Xinghuo Yu,et al.  Sliding Mode Control With Mixed Current and Delayed States for Offshore Steel Jacket Platforms , 2014, IEEE Transactions on Control Systems Technology.

[11]  Mohd Rapik Saat,et al.  Analysis of Causes of Major Train Derailment and Their Effect on Accident Rates , 2012 .

[12]  Lingfei Xiao,et al.  Sliding-mode output feedback control for active suspension with nonlinear actuator dynamics , 2015 .

[13]  Seung-Bok Choi,et al.  Hybrid clustering based fuzzy structure for vibration control - Part 1: A novel algorithm for building neuro-fuzzy system , 2015 .

[14]  Hasan Alli,et al.  Application of robust fuzzy sliding-mode controller with fuzzy moving sliding surfaces for earthquake-excited structures , 2007 .

[15]  Mansour A. Karkoub,et al.  Active/semi-active suspension control using magnetorheological actuators , 2006, Int. J. Syst. Sci..

[16]  Toshio Yoshimura,et al.  CONSTRUCTION OF AN ACTIVE SUSPENSION SYSTEM OF A QUARTER CAR MODEL USING THE CONCEPT OF SLIDING MODE CONTROL , 2001 .

[17]  M. Corless,et al.  Continuous state feedback guaranteeing uniform ultimate boundedness for uncertain dynamic systems , 1981 .

[18]  Seung-Bok Choi,et al.  An optimal design of interval type-2 fuzzy logic system with various experiments including magnetorheological fluid damper , 2014 .

[19]  Simon Iwnicki,et al.  Handbook of railway vehicle dynamics , 2006 .

[20]  Shrivijay B. Phadke,et al.  Active suspension systems for vehicles based on a sliding-mode controller in combination with inertial delay control , 2013 .

[21]  Sreenivasa Rao,et al.  ANALYSIS OF PASSIVE AND SEMI ACTIVE CONTROLLED SUSPENSION SYSTEMS FOR RIDE COMFORT IN AN OMNIBUS PASSING OVER A SPEED BUMP , 2010 .

[22]  Jia-ling Yao,et al.  Development of a sliding mode controller for semi-active vehicle suspensions , 2013 .

[23]  Nastaran Aghakhani,et al.  Fuzzy sliding mode control for applying to active vehicle suspentions , 2010 .

[24]  Seung-Bok Choi,et al.  A new neuro-fuzzy training algorithm for identifying dynamic characteristics of smart dampers , 2012 .

[25]  Shiuh-Jer Huang,et al.  A new model-free adaptive sliding controller for active suspension system , 2008, Int. J. Syst. Sci..

[26]  Rahmi Guclu,et al.  Active vibration control with comparative algorithms of half rail vehicle model under various track irregularities , 2011 .

[27]  Abdelaziz Hamzaoui,et al.  Type-2 fuzzy sliding mode control without reaching phase for nonlinear system , 2011, Eng. Appl. Artif. Intell..

[28]  Wen-Hua Chen,et al.  Nonlinear Disturbance Observer-Enhanced Dynamic Inversion Control of Missiles , 2003 .