Design of robust proportional–integral–derivative controller for generalized decoupled twin rotor multi-input-multi-output system with actuator non-linearity

This work addresses the problem of robust stabilization of twin rotor multi-input-multi-output system in the presence of inherent actuator non-linearity and plant structured uncertainties due to parameter variation. In order to achieve so, first a generalized decoupling technique is implemented to eliminate the cross coupling effect which are present in between the input and output of main and tail rotors of twin rotor multi-input-multi-output system. Next, two proportional–integral–derivative controllers are employed to control the main and tail rotors independently. The ranges of the parameters of the controllers are evaluated based on the Kharitonov theorem so as to ensure robust stability in the presence of structured uncertainties. The values of the controller parameters are finally sought out of these robust ranges with bacterial foraging optimization technique such that satisfactory time domain criteria are met. Furthermore, the compensated system is analyzed in frequency domain with common actuator non-linearity dead-zone and saturation together. The performance of the designed controller is evaluated through both simulation and experimental results. The controllers are found to perform efficiently ensuring adequate robust stability even in the presence of uncertainty, disturbance and actuator non-linearity.

[1]  Jinkun Liu,et al.  Adaptive RBF neural network control of robot with actuator nonlinearities , 2010 .

[2]  Peng Wen,et al.  Decoupling control of a twin rotor mimo system using robust deadbeat control technique , 2008 .

[3]  Arun Ghosh,et al.  Design and implementation of decoupled compensation for a twin rotor multiple-input and multiple-output system , 2013 .

[4]  Akbar Rahideh,et al.  Mathematical dynamic modelling of a twin-rotor multiple input-multiple output system , 2007 .

[5]  Zwe-Lee Gaing A particle swarm optimization approach for optimum design of PID controller in AVR system , 2004, IEEE Transactions on Energy Conversion.

[6]  Mohammad Farrokhi,et al.  Robust adaptive fuzzy control of twin rotor MIMO system , 2013, Soft Comput..

[7]  Peter Xiaoping Liu,et al.  Adaptive Neural Synchronization Control for Bilateral Teleoperation Systems With Time Delay and Backlash-Like Hysteresis , 2017, IEEE Transactions on Cybernetics.

[8]  Bing Chen,et al.  Fuzzy Approximation-Based Adaptive Control of Nonlinear Delayed Systems With Unknown Dead Zone , 2014, IEEE Transactions on Fuzzy Systems.

[9]  Maria Letizia Corradini,et al.  Robust stabilization of nonlinear uncertain plants with backlash or dead zone in the actuator , 2002, IEEE Trans. Control. Syst. Technol..

[10]  Jianwu Dang,et al.  Analysis and Improvement of the Bacterial Foraging Optimization Algorithm , 2014, J. Comput. Sci. Eng..

[11]  V. K. Pandey,et al.  Sliding mode controller design for Twin Rotor MIMO system with a nonlinear state observer , 2013, 2013 International Mutli-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s).

[12]  Masaki Yamakita,et al.  Tracking control of nonlinear stochastic systems with actuator nonlinearity , 2014, 2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

[13]  ASYMPTOTIC STABILITY OF AN EQUILIBRIUM P . OSITION OF A FAMILY OF SYSTEMS OF LINEAR DIFFERENTIAL EQUATIONS , 2022 .

[14]  Weiguo Xia,et al.  Adaptive Fuzzy Hierarchical Sliding-Mode Control for a Class of MIMO Nonlinear Time-Delay Systems With Input Saturation , 2017, IEEE Transactions on Fuzzy Systems.

[15]  G. Ray,et al.  A set of decentralized PID controllers for an n – link robot manipulator , 2012 .

[16]  M. L. Corradini,et al.  Robust stabilization of nonlinear plants with uncertain hysteresis-like actuator nonlinearities , 2003, 2003 European Control Conference (ECC).

[17]  Tong Heng Lee,et al.  Composite nonlinear feedback control for linear systems with input saturation: theory and an application , 2003, IEEE Trans. Autom. Control..

[18]  Shankar P. Bhattacharyya,et al.  Robust Control: The Parametric Approach , 1994 .

[19]  M. J. Grimble,et al.  Robust MIMO control-system design using eigenstructure assignment and QFT , 2004 .

[20]  Peng Shi,et al.  Adaptive tracking control for switched stochastic nonlinear systems with unknown actuator dead-zone , 2015, Autom..

[21]  Peng Chen,et al.  Automatic Design of Robust Optimal Controller for Interval Plants using Genetic Programming and Kharitonov Theorem , 2011, International Journal of Computational Intelligence Systems.

[22]  Zhiqiang Gao,et al.  On model-free accommodation of actuator nonlinearities , 2012, Proceedings of the 10th World Congress on Intelligent Control and Automation.

[23]  Mohamed Chemachema,et al.  Model reference adaptive control for twin rotor multiple-input and multiple-output system via minimal controller synthesis , 2014, J. Syst. Control. Eng..

[24]  Jayati Dey,et al.  Design and real-time implementation of robust PID controller for Twin Rotor MIMO System (TRMS) based on Kharitonov's theorem , 2016, 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES).

[25]  M. O. Tokhi,et al.  Dynamic modelling and open-loop control of a twin rotor multi-input multi-output system , 2002 .

[26]  Bing Chen,et al.  Robust Adaptive Fuzzy Tracking Control for Pure-Feedback Stochastic Nonlinear Systems With Input Constraints , 2013, IEEE Transactions on Cybernetics.