Pełnotekstowe zasoby PLDML oraz innych baz dziedzinowych są już dostępne w nowej Bibliotece Nauki.
Zapraszamy na https://bibliotekanauki.pl

PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2011 | 21 | 1 | 137-147

Tytuł artykułu

Neural network based feedback linearization control of a servo-hydraulic vehicle suspension system

Treść / Zawartość

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
This paper presents the design of a neural network based feedback linearization (NNFBL) controller for a two degree-offreedom (DOF), quarter-car, servo-hydraulic vehicle suspension system. The main objective of the direct adaptive NNFBL controller is to improve the system's ride comfort and handling quality. A feedforward, multi-layer perceptron (MLP) neural network (NN) model that is well suited for control by discrete input-output linearization (NNIOL) is developed using input-output data sets obtained from mathematical model simulation. The NN model is trained using the Levenberg-Marquardt optimization algorithm. The proposed controller is compared with a constant-gain PID controller (based on the Ziegler-Nichols tuning method) during suspension travel setpoint tracking in the presence of deterministic road disturbance. Simulation results demonstrate the superior performance of the proposed direct adaptive NNFBL controller over the generic PID controller in rejecting the deterministic road disturbance. This superior performance is achieved at a much lower control cost within the stipulated constraints.

Rocznik

Tom

21

Numer

1

Strony

137-147

Opis fizyczny

Daty

wydano
2011
otrzymano
2010-03-30
poprawiono
2010-09-26

Twórcy

  • School of Mechanical, Aeronautical and Industrial Engineering, University of the Witwatersrand, 1 Jan Smuts Ave, Private Bag 03, WITS 2050, Johannesburg, South Africa
  • School of Mechanical, Aeronautical and Industrial Engineering, University of the Witwatersrand, 1 Jan Smuts Ave, Private Bag 03, WITS 2050, Johannesburg, South Africa

Bibliografia

  • Al-Holou, N., Lahdhiri, T., Joo, D.S., Weaver, J. and Al-Abbas, F. (2002). Sliding mode neural network inference fuzzy logic control for active suspension system, IEEE Transactions on Fuzzy Systems 10(2): 234-245.
  • Biglarbegian, M., Melek, W. and Golnaraghi, F. (2008). A novel neuro-fuzzy controller to enhance the performance of vehicle semi-active suspension systems, Vehicle System Dynamics 46(8): 691-711.
  • Boukezzoula, R., Galichet, S. and Foulloy, L. (2007). Fuzzy feedback linearizing controller and its equivalence with fuzzy nonlinear internal model control structure, International Journal of Applied Mathematics and Computer Science 17(2): 233-248, DOI: 10.2478/v10006-007-0021-4.
  • Boutalis, Y.S. (2004). Neural network approaches for feedback linearization, Journal of Control Engineering and Applied Informatics 6(1): 15-26.
  • Buckner, G.D., Schuetze, K.T. and Beno, J.H. (2001). Intelligent feedback linearization for active vehicle suspension control, Journal of Dynamic Systems, Measurement and Control: Transactions of ASME 123(4): 727-733.
  • Cao, J., Liu, H., Li, P. and Brown, D. (2008). State of the art in vehicle active suspension adaptive control systems based on intelligent methodologies, IEEE Transactions of Intelligent Transportation Systems 9(3): 392-405.
  • Chantranuwathana, S. and Peng, H. (2004). Adaptive robust force control for vehicle active suspension, International Journal of Adaptive Control and Signal Processing 18(2): 83-102.
  • Christophe, L., Swevers, J. and Sas, P. (2005). Robust linear control of an active suspension on a quarter car test-rig, Control Engineering Practice 13(5): 577-586.
  • Dahunsi, O.A., Pedro, J.O. and Nyandoro, O.T. (2009). Neural network-based model predictive control of a servohydraulic vehicle suspension system, Proceedings of 2009 IEEE AFRICON, Nairobi, Kenya, pp. 1-6, DOI: 10.1109/AFRCON.2009.5308111.
  • Dahunsi, O.A., Pedro, J.O. and Nyandoro, O.T. (2010a). Neural network-based PID control of a servo-hydraulic vehicle suspension system, Proceedings of the 7th South African Conference on Computational and Applied Mechanics (SACAM10), Pretoria, South Africa, pp. 1-6.
  • Dahunsi, O.A., Pedro, J.O. and Nyandoro, O.T. (2010b). System identification and neural network based PID control of servo-hydraulic vehicle suspension system, SAIEE Africa Research Journal 101(3): 93-105.
  • D'Amato, F.J. and Viassolo, D.E. (2000). Fuzzy control for active suspensions, Mechatronics 10(1): 897-920.
  • Deng, J., Becerra, V.M. and Stobart, R. (2009). Input constraints handling in an MPC/feedback linearization scheme, International Journal of Applied Mathematics and Computer Science 19(2): 219-232, DOI: 10.2478/v10006-009-00182.
  • Du, H. and Zhang, N. (2008). Multiobjective static output feedback control design for vehicle suspensions, Journal of System Design and Dynamics 2(1): 228-239.
  • Du, H. and Zhang, N. (2009). Static output feedback control for electrohydraulic active suspensions via T-S fuzzy model approach, Journal of Dynamic Systems, Measurement and Control: Transactions of ASME 131(5): 0510041-051004-11.
  • Ehtiwesh, I.A.S. and Dorovic, Z. (2009). Comparative analysis of different control strategies for electro-hydraulic servo systems, Proceedings of the World Academy of Science, Engineering and Technology 56(6): 906-909.
  • Eski, I. and Yildrim, S. (2009). Vibration control of vehicle active suspension system using a new robust neural network control system, Simulation Modelling Practice and Theory 17(5): 778-793.
  • Fallah, M.S., Bhat, R. and Xie, W. (2009). $H_∞$ robust control of active suspensions: A practical point of view, Proceedings of the 2009 American Control Conference, St Louis, MO, USA, pp. 1385-1390.
  • Feng, J.Z., Li, J. and Yu, F. (2003). GA-based PID and fuzzy logic control for active vehicle suspension system, International Journal of Automotive Technology 4(4): 181-191.
  • Fiahlo, I. and Balas, G.J. (2002). Road adaptive active suspension using linear parameter-varying gain-scheduling, IEEE Transactions on Control Systems Technology 10(1): 43-54.
  • Gao, B., Tilley, D.G., Williams, R.A., Bean, A. and Donahue, J. (2006). Control of hydropneumatic active suspension based on a non-linear quarter-car model, Proceedings of the Institute of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 220(1): 75-31.
  • Gao, Z. (2002). From linear to nonlinear control means: A practical progression, ISA Transactions 41(2): 177-189.
  • Garces, F.R., Becerra, V.M., Kambhampti, C. and Warwick, K. (2003). Strategies for Feedback Linearisation: A Dynamic Neural Network Approach, Springer, London.
  • Gaspar, P., Szaszi, I. and Bokor, J. (2003). Active suspension design using linear parameter varying control, International Journal of Autonomous Systems 1(2): 206-221.
  • Goodwin, G.C., Rojas, O. and Takata, H. (2001). Nonlinear control via generalized feedback linearization using neural networks, Asian Journal of Control 3(2): 79-88.
  • Hada, M.K., Menon A. and Bhave S.Y. (2007). Optimisation of an active suspension force controller using genetic algorithm for random input, Defence Science Journal 57(5): 691-706.
  • Hagan, M.T. and Demuth, H.B. (1999). Neural networks for control, American Control Conference, San Diego, CA, USA, pp. 1642-1656.
  • Hassanzadeh, I., Alizadeh, G., Shiirjoposht, N. P. and Hashemzadeh, F. (2010). A new optimal nonlinear approach to half car active suspension control, IACSIT International Journal of Engineering and Technology 2(1): 78-84.
  • He, Y. and McPhee, J. (2005). A design methodology for mechatronic vehicles: Application of multidisciplinary optimization, multibody dynamics and genetic algorithms, Vehicle Systems Dynamics 43(10): 697-733.
  • Hrovat, D. (1997). Survey of advanced suspension developments and related optimal control applications, Automatica 33(10): 1781-1817.
  • Isidori, A. (1989). Nonlinear Control System, Springer-Verlag, Berlin.
  • Jelali, M. and Kroll, A. (2003). Hydraulic Servo-Systems: Modelling, Identification and Control, Springer-Verlag, London.
  • Jin, Y. and Yu, D.J. (2008). Adaptive neuron control using an integrated error approach with application to active suspensions, International Journal of Automotive Technology 9(3): 329-335.
  • Kar, I. and Behera, L. (2009). Direct adaptive neural control for affine nonlinear systems, Applied Soft Computing 9(2): 756-764.
  • Koshkouei, A.J. and Burnham, K.J. (2008). Sliding mode controllers for active suspensions, Proceedings of the 17th IFAC World Congress, COEX, Seoul, Korea, pp. 1-6.
  • Kumar, M.S. (2008). Development of active suspension system for automobiles using PID controller, Proceedings of the World Congress on Engineering, WCE 2008, London, UK, pp. 1472-1477.
  • Kumar, M.S. and Vijayarangan, S. (2007). Analytical and experimental studies on active suspension system of light passenger vehicle to improve ride comfort, Mechanika 65(3): 34-41.
  • Kuo, Y. and Li, T.S. (1999). GA-based fuzzy PI/PD controller for automotive active suspension system, IEEE Transactions on Industrial Electronics 46(6): 88-100.
  • Lufty, O.F., Noor, S.B.M., Marhaban, M.H. and Abbas, K.A. (2009). A geneticatlly trained adaptive neuro-fuzzy inference system network utilized as a PID-like feedback controller for non-linear systems, Proceedings of the Institute of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 223(3): 289-429.
  • Nakkarat, P. and Kuntanapreeda, S. (2009). Observer-based backstepping force control of an electrohydraulic actuator, Control Engineering Practice 17(8): 895-902.
  • Norgaard, M., Ravn, O., Poulsen, N.K. and Hansen, L.K. (2003). Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook, Springer, Boston, MA.
  • O'Dwyer, A. (2006). Handbook of PI and PID Controller Tuning Rules, Imperial College Press, London.
  • Pedro, J.O. (2003). Design and performance of an active vehicle suspension system, Proceedings of the 2nd International Conference on Applied Mechanics and Materials, ICAMM 2003, Durban, South Africa, pp. 203-209.
  • Pedro, J.O. (2007). H₂-LQG/LTR controller design for active suspension systems, R and D Journal of the South African Institution of Mechanical Engineering 23(2): 32-41.
  • Pedro, J.O. and Mgwenya, T.R. (2004). LQR control of a full car active suspension with actuator dynamics, Proceedings of the 4th South African Conference on Applied Mechanics, SACAM'04, Johannesburg, South Africa, pp. 1-9.
  • Poursamad, A. (2009). Adaptive feedback linearization control of antilock braking system using neural networks, Mechatronics 19(5): 767-773.
  • Poussot-Vassal, C., Sename, O., Dugard, L., Gaspar, P., Szabo, Z. and Bokor, J. (2006). Multi-objective qLPV $H_∞$/H₂ control of a half vehicle, Proceedings of the 10th MINI Conference on Vehicle System Dynamics, Identification and Anomalies, Budapest, Hungary, pp. 1-6.
  • Ryu, S., Kim, Y. and Park, Y. (2008). Robust $H_∞$ preview control of an active suspension system with norm-bounded uncertainties, International Journal of Automotive Technology 9(5): 585-592.
  • Salem, A.A.A. and Aly, A.A. (2009). Fuzzy control of a quarter-car suspension systems, Proceedings of the World Academy of Science, Engineering and Technology (53): 258-263.
  • Seo, J., Venugopal, R. and Kenne, J. (2007). Feedback linearization based control of a rotational hydraulic drive, Control Engineering Practice 15(12): 1495-1507.
  • Sharkawy, A.B. (2005). Fuzzy and adaptive fuzzy control for the automobiles' active suspension system, Vehicle Systems Dynamics 43(10): 795-806.
  • Shen, X. and Peng, H. (2003). Analysis of active suspension systems with hydraulic actuators, Proceedings of the 2003 IAVSD Conference, Atsuigi, Japan, pp. 1-10.
  • Shi, J., Li, X.W. and Zhang, J.W. (2010). Feedback linearization and sliding mode control for active hydropneumatic suspension of a special-purpose vehicle, Proceedings of the Institute of Mechanical Engineers, Part D: Journal of Automobile Engineering 211(3): 171-181.
  • Shirahatt, A., Prasad, P.S.S., Panzade, P. and Kulkarni, M.M. (2008). Optimal design of passenger car suspension for ride and road holding, Journal of the Brazillian Society of Mechanical Science and Engineering 30(1): 66-76.
  • He, S., Reif, K. and Unbehauen, R. (1998). A neural approach for control of nonlinear systems with feedback linearization, Control Engineering Practice 9(6): 1409-1421.
  • Slotine, J.J. and Li, W. (1991). Applied Nonlinear Control, Prentice Hall, Englewood Cliffs, NJ.
  • Yagiz, N., Sakman, L.E. and Guclu, R. (2008). Different control applications on a vehicle using fuzzy logic control, Sadhana 33(1): 15-25.
  • Yesildirek, A. and Lewis, F.L. (1995). Feedback linearization using neural networks, Automatica 31(11): 1659-1664.
  • Yoshimura, T. and Teramura, I. (2005). Active suspension control of a one-wheel car model using single input rule modules fuzzy reasoning and a disturbance observer, Journal of Zhejiang University: Science 6A(4): 251-256.

Typ dokumentu

Bibliografia

Identyfikatory

Identyfikator YADDA

bwmeta1.element.bwnjournal-article-amcv21i1p137bwm
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.