# American Institute of Mathematical Sciences

• Previous Article
Application of support vector machine model in wind power prediction based on particle swarm optimization
• DCDS-S Home
• This Issue
• Next Article
Research on the optimal initial shunt strategy of Jiuzhaigou based on the optimization model
December  2015, 8(6): 1251-1266. doi: 10.3934/dcdss.2015.8.1251

## Implementation of Mamdami fuzzy control on a multi-DOF two-wheel inverted pendulum robot

 1 NO.5 Zhongguancun South Street, Beijing Institute of Technology, Beijing 100081, China, China 2 NO.3 Xueyaun Road Fujian Univerity of Technology Xueyuan, Fuzhou 350118, Fujian, China

Received  July 2015 Revised  September 2015 Published  December 2015

These days a Two-wheel inverted pendulum (TWIP) robot attracts public attention as it is an efficient ergonomics and easy to operate by nuance personals. Furthermore it has attractive design features like compact in size and zero turning radius. However, the traditional TWIP robots have to change its posture to reach the desired speedup and deceleration by changing the robot posture forward and backward make it difficult to control its motion process. Thus, this paper presents here the Mamdami fuzzy control logic to overcome the motion control of Multi-DOF TWIP robot and make its motion smooth and steady control. By introducing two additional DOFs the slider and the swinging configuration, the robot can maintain its vertical posture even climbing and descending on slopes. To validate the robustness of the proposed method, classic PID controller is introduced for comparison in simulations and experiments. The simulation results demonstrate the effectiveness of the system design and the better performance in robustness over classic PID control strategy. Finally, the control scheme is implemented on the practical self-designed hardware.
Citation: Yubai Liu, Xueshan Gao, Fuquan Dai. Implementation of Mamdami fuzzy control on a multi-DOF two-wheel inverted pendulum robot. Discrete & Continuous Dynamical Systems - S, 2015, 8 (6) : 1251-1266. doi: 10.3934/dcdss.2015.8.1251
##### References:

show all references

##### References:
 [1] Xianwei Chen, Zhujun Jing, Xiangling Fu. Chaos control in a pendulum system with excitations. Discrete & Continuous Dynamical Systems - B, 2015, 20 (2) : 373-383. doi: 10.3934/dcdsb.2015.20.373 [2] Mari Paz Calvo, Jesus M. Sanz-Serna. Carrying an inverted pendulum on a bumpy road. Discrete & Continuous Dynamical Systems - B, 2010, 14 (2) : 429-438. doi: 10.3934/dcdsb.2010.14.429 [3] Roman Srzednicki. On periodic solutions in the Whitney's inverted pendulum problem. Discrete & Continuous Dynamical Systems - S, 2019, 12 (7) : 2127-2141. doi: 10.3934/dcdss.2019137 [4] Ivan Polekhin. On motions without falling of an inverted pendulum with dry friction. Journal of Geometric Mechanics, 2018, 10 (4) : 411-417. doi: 10.3934/jgm.2018015 [5] Xianwei Chen, Xiangling Fu, Zhujun Jing. Chaos control in a special pendulum system for ultra-subharmonic resonance. Discrete & Continuous Dynamical Systems - B, 2021, 26 (2) : 847-860. doi: 10.3934/dcdsb.2020144 [6] Tayel Dabbous. Adaptive control of nonlinear systems using fuzzy systems. Journal of Industrial & Management Optimization, 2010, 6 (4) : 861-880. doi: 10.3934/jimo.2010.6.861 [7] Leonid Shaikhet. Improved condition for stabilization of controlled inverted pendulum under stochastic perturbations. Discrete & Continuous Dynamical Systems, 2009, 24 (4) : 1335-1343. doi: 10.3934/dcds.2009.24.1335 [8] Ciro D’Apice, Umberto De Maio, Peter I. Kogut. Boundary velocity suboptimal control of incompressible flow in cylindrically perforated domain. Discrete & Continuous Dynamical Systems - B, 2009, 11 (2) : 283-314. doi: 10.3934/dcdsb.2009.11.283 [9] Yong Zhao, Qishao Lu. Periodic oscillations in a class of fuzzy neural networks under impulsive control. Conference Publications, 2011, 2011 (Special) : 1457-1466. doi: 10.3934/proc.2011.2011.1457 [10] Peng Cheng, Yanqing Liu, Yanyan Yin, Song Wang, Feng Pan. Fuzzy event-triggered disturbance rejection control of nonlinear systems. Journal of Industrial & Management Optimization, 2020  doi: 10.3934/jimo.2020119 [11] Omid S. Fard, Javad Soolaki, Delfim F. M. Torres. A necessary condition of Pontryagin type for fuzzy fractional optimal control problems. Discrete & Continuous Dynamical Systems - S, 2018, 11 (1) : 59-76. doi: 10.3934/dcdss.2018004 [12] Aliki D. Muradova, Georgios K. Tairidis, Georgios E. Stavroulakis. Adaptive Neuro-Fuzzy vibration control of a smart plate. Numerical Algebra, Control & Optimization, 2017, 7 (3) : 251-271. doi: 10.3934/naco.2017017 [13] Tien-Fu Liang, Hung-Wen Cheng. Multi-objective aggregate production planning decisions using two-phase fuzzy goal programming method. Journal of Industrial & Management Optimization, 2011, 7 (2) : 365-383. doi: 10.3934/jimo.2011.7.365 [14] Xingyue Liang, Jianwei Xia, Guoliang Chen, Huasheng Zhang, Zhen Wang. $\mathcal{H}_{\infty}$ control for fuzzy markovian jump systems based on sampled-data control method. Discrete & Continuous Dynamical Systems - S, 2021, 14 (4) : 1329-1343. doi: 10.3934/dcdss.2020368 [15] Kuang Huang, Xuan Di, Qiang Du, Xi Chen. A game-theoretic framework for autonomous vehicles velocity control: Bridging microscopic differential games and macroscopic mean field games. Discrete & Continuous Dynamical Systems - B, 2020, 25 (12) : 4869-4903. doi: 10.3934/dcdsb.2020131 [16] Mostafa Ghelichi, A. M. Goltabar, H. R. Tavakoli, A. Karamodin. Neuro-fuzzy active control optimized by Tug of war optimization method for seismically excited benchmark highway bridge. Numerical Algebra, Control & Optimization, 2021, 11 (3) : 333-351. doi: 10.3934/naco.2020029 [17] Yuan Li, Ruxia Zhang, Yi Zhang, Bo Yang. Sliding mode control for uncertain T-S fuzzy systems with input and state delays. Numerical Algebra, Control & Optimization, 2020, 10 (3) : 345-354. doi: 10.3934/naco.2020006 [18] Enrique Fernández-Cara, Juan Límaco, Laurent Prouvée. Optimal control of a two-equation model of radiotherapy. Mathematical Control & Related Fields, 2018, 8 (1) : 117-133. doi: 10.3934/mcrf.2018005 [19] Roberta Ghezzi, Benedetto Piccoli. Optimal control of a multi-level dynamic model for biofuel production. Mathematical Control & Related Fields, 2017, 7 (2) : 235-257. doi: 10.3934/mcrf.2017008 [20] Urszula Ledzewicz, Heinz Schättler, Mostafa Reisi Gahrooi, Siamak Mahmoudian Dehkordi. On the MTD paradigm and optimal control for multi-drug cancer chemotherapy. Mathematical Biosciences & Engineering, 2013, 10 (3) : 803-819. doi: 10.3934/mbe.2013.10.803

2019 Impact Factor: 1.233