On the simulation of RFPT-based adaptive control of systems of 4th order response

As an alternative of Lyapunov functions based design methods the “Robust Fixed Point Transformations (RFPT)”-based adaptive control design was developed in the past years. The traditional approaches emphasize the global stability of the controlled phenomena while leaving the details of the trajectory tracking develop as a not very clear consequence of the control settings the novel design directly concentrates on the observable response of the controlled system therefore it can concentrate on the tracking details as a primary design intent. Whenever a Classical Mechanical system that normally produces 2nd order response (i.e. acceleration) is forced through an elastic component its immediate response becomes 4th order one. Practical observation of the 4th order derivatives of a variable may suffer from measurement noises. Furthermore, when in simulation studies the higher order derivatives are numerically integrated and later numerically differentiated to provide the appropriate feedback signals the non-smooth jumps in the numerical integrator can destroy the simulation results. By the use of a simple 4th order model in this paper it is shown that the chained use of the built-in differentiators of the simulation package SCILAB is inappropriate for simulation purposes. It is also shown that by the use of a simple 4th order polynomial differentiator this problem can be solved. This statement is substantiated by simulation results.

[1]  Rolf Isermann,et al.  Adaptive control systems , 1991 .

[2]  Farrokh Janabi-Sharifi,et al.  Model Reference Adaptive Control Design for a Teleoperation System with Output Prediction , 2010, J. Intell. Robotic Syst..

[3]  T. A. Varkonyi Fuzzyfied Robust Fixed Point Transformations , 2012, 2012 IEEE 16th International Conference on Intelligent Engineering Systems (INES).

[4]  Kok Kiong Tan,et al.  Adaptive robust control for servo manipulators , 2003, Neural Computing & Applications.

[5]  Vladimír Gašpar,et al.  Intelligent Supervisory System for Small Turbojet Engines , 2013 .

[6]  L. Kovacs Modern robust control in Patophysiology from theory to application , 2013, 2013 IEEE 11th International Symposium on Applied Machine Intelligence and Informatics (SAMI).

[7]  J. K. Tar,et al.  Chaos formation and reduction in robust fixed point transformations based adaptive control , 2012, 2012 IEEE 4th International Conference on Nonlinear Science and Complexity (NSC).

[8]  Jozsef K. Tar,et al.  Improvement of the stability of RFPT-based adaptive controllers by observing “precursor oscillations” , 2013, 2013 IEEE 9th International Conference on Computational Cybernetics (ICCC).

[9]  Tadej Bajd,et al.  Application of Model Reference Adaptive Control to Industrial Robot Impedance Control , 1998, J. Intell. Robotic Syst..

[10]  Sandor Szenasi,et al.  Implementation of a Distributed Genetic Algorithm for Parameter Optimization in a Cell Nuclei Detection Project , 2013 .

[11]  József K. Tar,et al.  VS-type stabilization of MRAC controllers using robust fixed point transformations , 2012, 2012 7th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI).

[12]  Imre J Rudas,et al.  Replacement of Lyapunov's direct method in Model Reference Adaptive Control with Robust Fixed Point Transformations , 2010, 2010 IEEE 14th International Conference on Intelligent Engineering Systems.

[13]  Ladislav Madarász,et al.  Intelligent Technologies in Modeling and Control of Turbojet Engines , 2010 .

[14]  J.-J.E. Slotine,et al.  Adaptive control with multiresolution bases , 1997, Proceedings of the 36th IEEE Conference on Decision and Control.

[15]  J. Tar,et al.  Application of local deformations in adaptive control — A comparative survey , 2009, 2009 IEEE International Conference on Computational Cybernetics (ICCC).

[16]  S. Banach Sur les opérations dans les ensembles abstraits et leur application aux équations intégrales , 1922 .

[17]  Jozsef K. Tar,et al.  Robust Fixed Point Transformations in the Model Reference Adaptive Control of a Three DoF Aeroelastic Wing , 2013 .

[18]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .

[19]  József K. Tar,et al.  Improved neural network control of inverted pendulums , 2013, Int. J. Adv. Intell. Paradigms.

[20]  Balth. van der Pol,et al.  VII. Forced oscillations in a circuit with non-linear resistance. (Reception with reactive triode) , 1927 .

[21]  J. K. Tar,et al.  Simulation Studies on Various Tuning Methods for Convergence Stabilization in a Novel Approach of Model Reference Adaptive Control Based on Robust Fixed Point Transformations , 2011 .

[22]  Ivan Sekaj,et al.  Robust output feedback controller design: genetic algorithm approach , 2005, IMA J. Math. Control. Inf..

[23]  József K. Tar,et al.  Robust fixed point transformations in adaptive control using local basin of attraction , 2009 .

[24]  J. K. Tar,et al.  Chaos patterns in a 3 Degree of Freedom control with Robust Fixed Point Transformation , 2012, 2012 IEEE 13th International Symposium on Computational Intelligence and Informatics (CINTI).

[25]  A. Fuller,et al.  Stability of Motion , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[26]  József Tick,et al.  Fuzzy implications and inference processes , 2005, IEEE 3rd International Conference on Computational Cybernetics, 2005. ICCC 2005..

[27]  Richard M. Murray,et al.  A Mathematical Introduction to Robotic Manipulation , 1994 .

[28]  Charles C. Nguyen,et al.  Adaptive control of a stewart platform-based manipulator , 1993, J. Field Robotics.

[29]  Ladislav Madarász,et al.  Situational Control, Modeling and Diagnostics of Large Scale Systems , 2009, Towards Intelligent Engineering and Information Technology.

[30]  Jozsef K. Tar,et al.  Towards Replacing Lyapunov’s “Direct” Method in Adaptive Control of Nonlinear Systems , 2014 .