LQR-Based TS-Fuzzy Logic Controller Design for Inverted Pendulum-Coupled Cart System

In this paper, an effective design technique for heuristic Takagi-Sugeno fuzzy logic controller (TS-FLC) for nonlinear inverted pendulum (IP) and cart system has been proposed. IP is linearized around distinct combinations of localized points and their respective linear quadratic regulator (LQR) gains are obtained. Set of these localized points are used to decide the range of input fuzzy membership function, and the LQR controller gains are used to obtain basic TS rule base for nonlinear model. Angle and angular velocity are used to design the controller for upright stabilization of pendulum. Cart position and cart velocity are the inputs for cart control. The main aim is to control pendulum in upright unstable equilibrium point and cart position at desired value simultaneously. Physical constraints of the system such as cart track length and controller output are considered in the designing of FLC. The results obtained by FLC are compared with LQR. The results show that FLC is better than LQR because it can be further tuned to satisfy the constraints. Simulation results show the effectiveness and robustness of proposed TS-FLC over LQR controller.

[1]  Paul Van Dooren Control system toolbox: A. Grace, A. Laub, J. Little and C. Thompson , 1994, Autom..

[2]  P.D. Olivier Feedback linearization using TSK fuzzy approximants , 2005, Proceedings of the Thirty-Seventh Southeastern Symposium on System Theory, 2005. SSST '05..

[3]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[4]  Hak-Keung Lam,et al.  Design and stability analysis of fuzzy model-based nonlinear controller for nonlinear systems using genetic algorithm , 2003, IEEE Trans. Syst. Man Cybern. Part B.

[5]  Stephen Yurkovich,et al.  Fuzzy Control , 1997 .

[6]  Olfa Boubaker,et al.  The inverted pendulum: A fundamental benchmark in control theory and robotics , 2012, International Conference on Education and e-Learning Innovations.

[7]  Ching-Chang Wong,et al.  A self-generating method for fuzzy system design , 1999, Fuzzy Sets Syst..

[8]  S. S. Nair,et al.  A new self tuning fuzzy controller design and experiments , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[9]  Meng Joo Er,et al.  Hybrid fuzzy control of robotics systems , 2004, IEEE Trans. Fuzzy Syst..

[10]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[11]  Olfa Boubaker,et al.  The Inverted Pendulum Benchmark in Nonlinear Control Theory: A Survey , 2013 .

[12]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

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

[14]  O. Debande,et al.  Information and Communication Technologies: A Tool Empowering and Developing the Horizon of the Learner. , 2004 .

[15]  Aldo Cipriano,et al.  A design method for stable fuzzy LQR controllers , 1997, Proceedings of 6th International Fuzzy Systems Conference.

[16]  M.A. Lee,et al.  Integrating design stage of fuzzy systems using genetic algorithms , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[17]  Zhihong Man,et al.  Design of fuzzy sliding-mode control systems , 1998, Fuzzy Sets Syst..