CRS and PS-optimised PID controller for nonlinear, electrohydraulic suspension systems

The compromise between ride comfort, suspension travel, road holding, vehicle handling and power consumption determines the success of an active vehicle suspension system (AVSS). The simplicity of Proportional-Integral-Derivative (PID) controllers has made it the controller of choice for many mechatronic systems including AVSS. This investigation studies the effectiveness of optimal control policies such as Pattern Search (PS), and Controlled Random Search (CRS)-based PID controllers in dealing with the inherent trade-offs of AVSS. A nonlinear servo-hydraulic quarter-car AVSS is considered in this article. The success of these optimal PID controllers may provide a contemporary foundation in selecting optimal gains PID for a control system, which at the moment is a rather rigorous and time consuming process. The objective function is chosen such that each of the AVSS trade-offs are addressed. The PS routine improved significantly from the manually tuned and uncontrolled cases with an overall improvement in ride comfort, suspension travel, settling time and road holding. However, this was attained at the cost of greater power consumption and actuation force. The CRS routine showed a substantial improvement from the manually tuned case in terms of ride comfort and settling time, but exhibited weaker characteristics in terms of road holding and transient behaviour, which implies that its solution may have been caught in a local minimum.

[1]  D. Hrovat,et al.  Survey of Advanced Suspension Developments and Related Optimal Control Applications, , 1997, Autom..

[2]  Meng Li,et al.  Improved particle swarm optimizer based on adaptive random learning approach , 2009, 2009 IEEE Congress on Evolutionary Computation.

[3]  S. Ramesh,et al.  Stochastic algorithm for PID tuning of bus suspension system , 2009, 2009 International Conference on Control, Automation, Communication and Energy Conservation.

[4]  Gregory D. Buckner,et al.  Multi-objective control optimization for semi-active vehicle suspensions , 2011 .

[5]  Rong-Jong Wai,et al.  Real-Time PID Control Strategy for Maglev Transportation System via Particle Swarm Optimization , 2011, IEEE Transactions on Industrial Electronics.

[6]  W. Price Global optimization by controlled random search , 1983 .

[7]  Johari Halim Shah Osman,et al.  A class of proportional-integral sliding mode control with application to active suspension system , 2004, Syst. Control. Lett..

[8]  Jimoh Olarewaju Pedro,et al.  Neural network based feedback linearization control of a servo-hydraulic vehicle suspension system , 2011, Int. J. Appl. Math. Comput. Sci..

[9]  M. M. ElMadany,et al.  Design evaluation of advanced suspension systems for truck ride comfort , 1990 .

[10]  Sahin Yildirim,et al.  Vibration control of vehicle active suspension system using a new robust neural network control system , 2009, Simul. Model. Pract. Theory.

[11]  C. Storey,et al.  Application of Stochastic Global Optimization Algorithms to Practical Problems , 1997 .

[12]  Lakshmi Ponnusamy,et al.  PSO tuned Adaptive Neuro-fuzzy Controller for Vehicle Suspension Systems , 2012 .

[13]  Jun Zhang,et al.  Adaptive Particle Swarm Optimization , 2008, ANTS Conference.

[14]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[15]  M. Montaz Ali,et al.  A derivative-free variant called DFSA of Dekkers and Aarts' continuous simulated annealing algorithm , 2012, Appl. Math. Comput..

[16]  Jimoh O. Pedro,et al.  PID control of a nonlinear half-car active suspension system via force feedback , 2011, IEEE Africon '11.

[17]  Juing-Shian Chiou,et al.  A PSO-based adaptive fuzzy PID-controllers , 2012, Simul. Model. Pract. Theory.

[18]  Ali Volkan Akkaya,et al.  Simulation and hybrid fuzzy-PID control for positioning of a hydraulic system , 2010 .

[19]  S. Baskar,et al.  Evolutionary algorithms based design of multivariable PID controller , 2009, Expert Syst. Appl..

[20]  Musa Mailah,et al.  Vehicle active suspension system using skyhook adaptive neuro active force control , 2009 .

[21]  Grzegorz Ziomek,et al.  Random search optimization approach for highly multi-modal nonlinear problems , 2005, Adv. Eng. Softw..

[22]  Mohammad Mehdi Fateh,et al.  Impedance control of an active suspension system , 2009 .

[23]  Anil Shirahatt,et al.  Optimal Design of Passenger Car Suspension for Ride and Road , 2008 .