Journal ArticleDOI
A Markov Game-Adaptive Fuzzy Controller for Robot Manipulators
Rajneesh Sharma,M. Gopal +1 more
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TLDR
The proposed Markov game-adaptive fuzzy controller outperformed other controllers in terms of tracking errors and control torque requirements, over different desired trajectories, and demonstrates the viability of FISs for accelerating learning in Markov games and extending Markovgame-based control to continuous state-action space problems.Abstract:
This paper develops an adaptive fuzzy controller for robot manipulators using a Markov game formulation. The Markov game framework offers a promising platform for robust control of robot manipulators in the presence of bounded external disturbances and unknown parameter variations. We propose fuzzy Markov games as an adaptation of fuzzy Q-learning (FQL) to a continuous-action variation of Markov games, wherein the reinforcement signal is used to tune online the conclusion part of a fuzzy Markov game controller. The proposed Markov game-adaptive fuzzy controller uses a simple fuzzy inference system (FIS), is computationally efficient, generates a swift control, and requires no exact dynamics of the robot system. To illustrate the superiority of Markov game-adaptive fuzzy control, we compare the performance of the controller against a) the Markov game-based robust neural controller, b) the reinforcement learning (RL)-adaptive fuzzy controller, c) the FQL controller, d) the Hinfin theory-based robust neural game controller, and e) a standard RL-based robust neural controller, on two highly nonlinear robot arm control problems of i) a standard two-link rigid robot arm and ii) a 2-DOF SCARA robot manipulator. The proposed Markov game-adaptive fuzzy controller outperformed other controllers in terms of tracking errors and control torque requirements, over different desired trajectories. The results also demonstrate the viability of FISs for accelerating learning in Markov games and extending Markov game-based control to continuous state-action space problems.read more
Citations
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Journal ArticleDOI
Dynamic Learning From Adaptive Neural Control of Robot Manipulators With Prescribed Performance
TL;DR: A static neural learning control is proposed to improve the system performances without time-consuming online parameter adjustment process, and the proposed learning control can also guarantee the prescribed transient and steady-state tracking control performance.
Journal ArticleDOI
Fuzzy-Neural-Network Inherited Sliding-Mode Control for Robot Manipulator Including Actuator Dynamics
Rong-Jong Wai,Rajkumar Muthusamy +1 more
TL;DR: The design and analysis of an intelligent control system that inherits the robust properties of sliding-mode control (SMC) for an n-link robot manipulator, including actuator dynamics in order to achieve a high-precision position tracking with a firm robustness is presented.
Journal ArticleDOI
Backward Q-learning: The combination of Sarsa algorithm and Q-learning
TL;DR: The proposed RL algorithms can enhance learning speed and improve final performance, and the backward Q-learning based RL algorithm outperforms the well-known Q- learning and the Sarsa algorithm.
Journal ArticleDOI
Enhanced Adaptive Fuzzy Control With Optimal Approximation Error Convergence
Yongping Pan,Meng Joo Er +1 more
TL;DR: It is proved that the closed-loop system achieves partially asymptotic stability under a certain selection of control parameters under relaxed constraint conditions of the enhanced adaptive fuzzy control strategy.
Journal ArticleDOI
A Parameterized Nonlinear Programming Approach to Solve Matrix Games With Payoffs of I-Fuzzy Numbers
Deng-Feng Li,Jia-Cai Liu +1 more
TL;DR: A new methodology for solving matrix games with payoffs of Atanassov's intuitionistic fuzzy (I-fuzzy) numbers is developed and a difference-index-based ranking method is developed, which is proven to be a total order.
References
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Reinforcement Learning: An Introduction
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TL;DR: In this article, the authors present the first textbook that fully explains the neuro-dynamic programming/reinforcement learning methodology, which is a recent breakthrough in the practical application of neural networks and dynamic programming to complex problems of planning, optimal decision making, and intelligent control.
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TL;DR: This is the first textbook that fully explains the neuro-dynamic programming/reinforcement learning methodology, which is a recent breakthrough in the practical application of neural networks and dynamic programming to complex problems of planning, optimal decision making, and intelligent control.
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Markov games as a framework for multi-agent reinforcement learning
TL;DR: A Q-learning-like algorithm for finding optimal policies and its application to a simple two-player game in which the optimal policy is probabilistic is demonstrated.
Journal ArticleDOI
Fuzzy basis functions, universal approximation, and orthogonal least-squares learning
Li-Xin Wang,Jerry M. Mendel +1 more
TL;DR: Using the Stone-Weierstrass theorem, it is proved that linear combinations of the fuzzy basis functions are capable of uniformly approximating any real continuous function on a compact set to arbitrary accuracy.