Review and current study on new approach using PID Active Force Control (PIDAFC) of twin rotor multi input multi output system (TRMS)

This paper presents initial study of new control PID-Active Force Control (PIDAFC) in controlling twin rotor multi-input multi-output System (TRMS). The objective is to control the TRMS response in order to obey the desired output and rejecting external disturbances. To control the TRMS was challenging since the rotor interact badly at the beam between yaw and pitch. For this reason, it considers as multi input and multi output (MIMO) with nonlinear behavior. The new approach using PID-AFC integrate with neural network and fuzzy logic was proposed to compensate the disturbance occur on TRMS. AFC scheme refer as based controller was used to estimate the disturbance torque while neural network and fuzzy logic act as an optimization device. In initial stage PID-AFC can still be used in minimize the tracking error and rejecting the disturbances. The proposed approach is exhibited through the simulation.

[1]  Siti Fauziah Toha,et al.  Real-coded genetic algorithm for parametric modelling of a TRMS , 2009, 2009 IEEE Congress on Evolutionary Computation.

[2]  M. Osman Tokhi,et al.  Modelling of a Flexible Manoeuvring System Using ANFIS Techniques , 2010, 2010 12th International Conference on Computer Modelling and Simulation.

[3]  M. O. Tokhi,et al.  Dynamic modelling and linear quadratic Gaussian control of a twin-rotor multi-input multi-output system , 2003 .

[4]  Liang-Rui Chen,et al.  Improvement of the Twin Rotor MIMO System Tracking and Transient Response Using Fuzzy Control Technology , 2006, 2006 1ST IEEE Conference on Industrial Electronics and Applications.

[5]  M.O. Tokhi,et al.  MLP and Elman recurrent neural network modelling for the TRMS , 2008, 2008 7th IEEE International Conference on Cybernetic Intelligent Systems.

[6]  F.M. Aldebrez,et al.  Input-Shaping with GA-Tuned PID for Target Tracking and Vibration Reduction , 2005, Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation Intelligent Control, 2005..

[7]  Musa Mailah,et al.  DEVELOPMENT OF A SOFTWARE FOR SIMULATING ACTIVE FORCE CONTROL SCHEMES OF A TWO-LINK PLANAR MANIPULATOR , 2005 .

[8]  M. O. Tokhi,et al.  Dynamic modelling and linear quadratic Gaussian control of a twin-rotor multi-output system , 2003 .

[9]  M.H. Shaheed,et al.  Hybrid Fuzzy-PID-based Control of a Twin Rotor MIMO System , 2006, IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics.

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

[11]  Jih-Gau Juang,et al.  A single neuron PID control for twin rotor MIMO system , 2009, 2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

[12]  Jih-Gau Juang,et al.  Optimal Fuzzy Switching Grey Prediction with RGA for TRMS Control , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[13]  Bidyadhar Subudhi,et al.  Nonlinear system identification of a twin rotor MIMO system , 2009, TENCON 2009 - 2009 IEEE Region 10 Conference.

[14]  Jih-Gau Juang,et al.  Comparison of classical control and intelligent control for a MIMO system , 2008, Appl. Math. Comput..

[15]  Shubhi Purwar,et al.  A Nonlinear State Observer Design for 2-DOF Twin Rotor System Using Neural Networks , 2009, 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies.

[16]  Akbar Rahideh,et al.  Dynamic modelling of a TRMS using analytical and empirical approaches , 2008 .

[17]  Jih-Gau Juang,et al.  Intelligent control scheme for twin rotor MIMO system , 2005, IEEE International Conference on Mechatronics, 2005. ICM '05..