MacPherson suspension system modeling and control with MDP

Simulation-based non-linear active suspension control design for MacPherson systems is considered. A nonlinear dynamic model for the MacPherson suspension system is derived. The model nonlinearities and the dynamic behaviour of the system is illustrated by simulations. The design of controllers and state estimators using finite state Markov models is briefly outlined, and applied for nonlinear active suspension control system. The study illustrates the potential of the finite Markov chains approach in non-linear active suspension control, emphasizing the possibility to move computational load due to simulations to off-line design.

[1]  Wen-Fang Xie,et al.  New model and simulation of Macpherson suspension system for ride control applications , 2009 .

[2]  H. Eric Tseng,et al.  State of the art survey: active and semi-active suspension control , 2015 .

[3]  Kaddour Najim,et al.  Multiple Model-Based Control Using Finite Controlled Markov Chains , 2009, Cognitive Computation.

[4]  D. J. White,et al.  A Survey of Applications of Markov Decision Processes , 1993 .

[5]  Kaddour Najim,et al.  Process control using finite Markov chains with iterative clustering , 2016, Comput. Chem. Eng..

[6]  B. Nordstrom.  FINITE MARKOV CHAINS , 2005 .

[7]  Alfonso Garcia-Cerezo,et al.  Kinematic and dynamic analysis of the McPherson suspension with a planar quarter-car model , 2013 .

[8]  Leslie Pack Kaelbling,et al.  Planning and Acting in Partially Observable Stochastic Domains , 1998, Artif. Intell..

[9]  C. S. Hsu,et al.  Cell-to-Cell Mapping , 1987 .

[10]  Keyu Li,et al.  Bayesian state estimation of nonlinear systems using approximate aggregate markov chains , 2006 .

[11]  Honghai Liu,et al.  State of the Art in Vehicle Active Suspension Adaptive Control Systems Based on Intelligent Methodologies , 2008, IEEE Transactions on Intelligent Transportation Systems.

[12]  Panos M. Pardalos,et al.  Approximate dynamic programming: solving the curses of dimensionality , 2009, Optim. Methods Softw..

[13]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[14]  C. Hsu,et al.  Cell-To-Cell Mapping A Method of Global Analysis for Nonlinear Systems , 1987 .

[15]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[16]  Warren B. Powell,et al.  Approximate Dynamic Programming - Solving the Curses of Dimensionality , 2007 .