A New Adaptive Partially Decentralized PID Controller for Non-square Discrete-time Linear Parameter Varying Systems
- 7 Downloads
In this paper, a novel partially decentralized adaptive control strategy is presented to deal with a class of Multi Inputs Multi Outputs (MIMO) non-square, Linear Parameter Varying (LPV) systems. The key idea is the design of Proportional Integral Derivative (PID) regulator based on online pairing and tuning of its parameters using the Dynamic Relative Gain Array (DRGA) matrix. The proposed adaptive partially decoupled control scheme operates in a straightforward and systematic way. The convergence of the proposed controller is guaranteed by theoretical analysis, numerical simulations and experimental tests carried out on a non-holonomic two Wheeled Mobile Robot (WMR). Studies of the proposed regulator robustness against additive disturbance and stability over the whole parameter range are given to illustrate the effectiveness of the proposed PID scheme.
KeywordsAdaptive partially decentralized regulation discrete-time state-space linear parameter varying models dynamic relative gain array embedded C code generation microcontrollers stability analysis
Unable to display preview. Download preview PDF.
- T. Chiranjeeevi, I. V. V. Vijetha, B. N. CH. V. Chakravarthi, and M. Karthika, “Tuning and control of multi variable systems,” International Journal of Electronics and Electrical Engineering, vol. 2, no. 4, December 2014. [click]Google Scholar
- P. Dworak, “Squaring down plant model and I/O grouping strategies for a dynamic decoupling of left-invertible MIMO plants,” Bulletin of the Polish Academy of Sciences, Technical Sciences, vol. 62, vo. 3, 2014.Google Scholar
- J. Garridoa, F. Vazqueza, and F. Morillab, “Multivariable PID control by decoupling,” International Journal of Systems Science, 2014.Google Scholar
- Q. G. Wang, Decoupling Control, Springer-Verlag, Berlin-Heidelberg, 2003.Google Scholar
- A. Bagis, “Determination of the PID controller parameters by modified genetic algorithm for improved performance,” Journal of Information Science and Engineering, pp. 1469–1480, 2007.Google Scholar
- F. Kühne, W. F Lages, J. M. Gomes, and Da Silva Jr., “Model predictive control of a mobile robot using linearization,” Mechatronics and Robotics 2004, Aachen, Germany, pp. 525–530, 2004.Google Scholar
- L. Pacheco and N. Luo, “Mobile robot local trajectory tracking with dynamic predictive control technics,” International Journal of innovative Computing, Information and control, vol. 7, no. 6, pp. 3457–3483, June 2011.Google Scholar
- J. Keighobadi and Y. Mohamadi, “Fuzzy robust trajectory tracking control of WMRs,” Intelligent Control and Innovative Computing, Springer, 2012.Google Scholar
- M. F. Witcher and T. J. McAvoy, “Interacting control systems: steadystate and dynamic measurement of interaction,” ISA Transactions, vol. 16, no. 3, pp. 35–41, 1977.Google Scholar
- M. C. Arranz and W. Birk, “A new approach to the dynamic RGA analysis of uncertain systems,” IEEE Multiconference on Systems and Control, San Antonio, Texas, USA, September 3-5, 2008.Google Scholar
- M. Kurien, A. Prayagkar, and V. Rajeshirke, “Overview of different approaches of PID controller tuning,” International Journal of Research in Advent Technology, vol. 2, no. 1, pp. 167–175, January 2014.Google Scholar
- I. Malouche, A. Kheriji, and F. Bouani, “Automatic model predictive control implementation in a high-performance microcontroller,” International Conference on Systems, Analysis and Automatic Control, Mahdia, Tunisia, March 2015.Google Scholar
- STMicroelectronics “RM0090 Reference manual,” 2015. http://www.st.com/web/catalog/mmc/FM141/SC1169/SS1577/LN1806/PF255422.