Evaluation-Function-based Model-free Adaptive Fuzzy Control
Designs of adaptive fuzzy controllers (AFC) are commonly based on the Lyapunov approach, which requires a known model of the controlled plant. They need to consider a Lyapunov function candidate as an evaluation function to be minimized. In this study these drawbacks were handled by designing a model-free adaptive fuzzy controller (MFAFC) using an approximate evaluation function defined in terms of the current state, the next state, and the control action. MFAFC considers the approximate evaluation function as an evaluative control performance measure similar to the state-action value function in reinforcement learning. The simulation results of applying MFAFC to the inverted pendulum benchmark veriﬁed the proposed scheme’s efficacy.
Wang, L-X., A Course In Fuzzy Systems And Control, New Jersey, United States, Prentice-Hall International, Inc., 1997.
Labiod, S. & Guerra, T.M., Adaptive Fuzzy Control for a Class of SISO nonaffine Nonlinear Systems, Fuzzy Sets and Systems, 158(10), pp. 1098-1126, 2007a.
Labiod, S. & Guerra, T.M., Direct Adaptive Fuzzy Control for a Class of MIMO Nonlinear Systems, International Journal of System Science, 38(8), pp. 665-675.
Labiod, S. & Guerra, T.M., Indirect Adaptive Fuzzy Control for a Class of Nonaffine Nonlinear Systems with Unknown Control Directions, International Journal of Control, Automation, and Systems, 8(4), pp. 903-907, 2010.
Bhasin, S., Reinforcement Learning and Optimal Control Methods for Uncertain Nonlinear Systems, Ph.D. Dissertation, University of Florida, United States, 2011.
Sutton, R.S. & Barto, A.G., Reinforcement Learning: An Introduction, Cambridge, MA, United States, MIT Press, 1998.
Åström, K.J. & Witternmark, B., Adaptive Control. Addison-Wesley Publishing Company, 1989.
Lin, C-K., A Reinforcement Learning Adaptive Fuzzy Controller for Robots, Fuzzy sets and system, 137(3), pp. 339-352, 2003.
Nazaruddin, Y.Y., Naba, A. & Liong, T.H., Modified Adaptive Fuzzy Control System Using Universal Supervisory Controller, in Proc. of SCI/ISAS 2000, Orlando, USA, vol. IX, 2000.
Oh, S.K., Pedrycz, W., Rho, S.B. & Ahn, T.C., Parameters Estimation of Fuzzy Controller and its Application to Inverted Pendulum,” Engineering Applications of Artificial Intelligence, 17(1), pp. 37-60, 2004.
Park, J., Park, G., Kim, S. & Moon, C., Direct Adaptive Self-Structuring Fuzzy Controller for Nonaffine Nonlinear System, Fuzzy Sets and Systems, 153(3), pp. 429-445, 2005.
Liuzzo, S., Marino, R. & Tomei, P., Adaptive Learning Control of Linear Systems by Output Error Feedback, Automatica, 43(4), pp. 669-676, 2007.
Wu, L-B. & Yang, G-H., Adaptive Fuzzy Tracking Control for a Class of Uncertain Nonaffine Nonlinear Systems with Dead-Zone Inputs,” Fuzzy Sets and Systems, 290(C), pp. 1-21, May 2016.
Chen, Y., Wei, Y., Liang, S., & Wang, Y., Indirect Model Reference Adaptive Control for a Class of Fractional Order Systems, Commun. Nonlinear Sci. Numer. Simul., 39, pp. 458-471, 2016.
Naba, A. & Miyashita, K., Tuning Fuzzy Controller Using Approximated Evaluation Function, in Proc. of the 4th IEEE International Workshop WSTST05, Muroran, Japan, pp. 113-122, 2005.
Naba, A. & Miyashita, K., Gradient-based Tuning of Fuzzy Controller with Approximated Evaluation Function, in Proc. of Eleventh International Fuzzy Systems Association (IFSA) World Congress, Beijing, China, pp. 671-676, 2005.
Naba, A. & Miyashita, K., FCAPS: Fuzzy Controller with Approximated Policy Search Approach, Journal of Adv. Comput. Intelligence and Intelligent Informatics, 1(1), pp. 84-92, 2006.
Naba, A., Adaptive Control with Approximated Policy Search Approach, ITB Journal of Engineering Science, 42(1), pp. 17-38, 2010.
Sutton, R.S., Learning to Predict by the Methods of Temporal Differences, Machine Learning, 3(1), pp. 9-44, 1988.
Berenji, H.R. & Khedkar, P., Learning and Tuning Fuzzy Logic Controllers Through Reinforcement, IEEE, 3(5), pp. 724–740, 1992.
Berenji, H.R. & Khedkar, P., Using Fuzzy Logic for Performance Evaluation in Reinforcement Learning, International Journal of Approximate Reasoning, 18, pp. 131–144, 1998.
Sutton, R.S., McAllester, D., Singh, S. & Mansour, Y., Policy Gradient Methods for Reinforcement Learning with Function Approximation, Advances in Neural Information Processing System, 12, pp. 1057-1063, 2000.
Santamaria, J.C., Sutton, R.R. & Ram, A., Experiment with Reinforcement Learning in Problems with Continuous State and Action Spaces, Adaptive Behavior, 6(2), pp. 163-218, 1998.
Slotine, J-J.E. & Li, W., Applied Nonlinear Control, Englewood Cliffs New Jersey, United States, Prentice Hall, 1991.
Baird, L.C. & Moore, A.W., Gradient Descent for General Reinforcement Learning, Advances in Neural Information Processing Systems 11, 1999.
- There are currently no refbacks.