Introduction

2009 International Conference on Information Technology and Computer Science

2009

This paper constructs an operant conditioning learning system based on fuzzy and probabilistic automata, which used for on-line self-learning of fuzzy rules. The learning system can learn its rules on line by interaction with environment, and achieve the best rule consequent. The probability cans grantee the global superiority of learning mechanism. The fuzzy inference can improve the robustness and rapidity and of learning. Furthermore, we adopt two fuzzy control structure in order to avoid rule explosion problem that mean the rules will increase in exponent which induced by the more number of input variable, which can predigest difficulty of design. We apply our model to inverted pendulum selfbalance control and the simulation indicate: the designed operant conditioning learning system can realize the selflearning of fuzzy rules, and it especially has outstanding superiority for dealing with lack of prior knowledge.

Section: Introductionmentioning

confidence: 99%

Self-Balance Control of Inverted Pendulum Based on Fuzzy Skinner Operant Conditioning

2009 International Conference on Information Technology and Computer Science

2009

“…This paper constructs an operant conditioning probabilistic automaton (OCPA) bionic autonomous learning system based on the Skinner OC and probabilistic automaton [16], which can be used to control the balance of a two-wheeled flexible robot. The OCPA system has two stages: in the first stage, an operant action is selected stochastically from a set of operant actions; this action is then used as the input control signal for the control system; in the second stage, after the action has been applied, the OCPA learning system gathers information about the orientation of the system and uses this information for optimization until it finally achieves control.…”

Section: Introductionmentioning

confidence: 99%

Bionic autonomous learning control of a two-wheeled self-balancing flexible robot

J. Control Theory Appl.

2011

This paper presents an OCPA (operant conditioning probabilistic automaton) bionic autonomous learning system based on Skinner's operant conditioning theory for solving the balance control problem of a two-wheeled flexible robot. The OCPA learning system consists of two stages: in the first stage, an operant action is selected stochastically from a set of operant actions and then used as the input of the control system; in the second stage, the learning system gathers the orientation information of the system and uses it for optimization until achieves control target. At the same time, the size of the operant action set can be automatically reduced during the learning process for avoiding little probability event. Theory analysis is made for the designed OCPA learning system in the paper, which theoretically proves the convergence of operant conditioning learning mechanism in OCPA learning system, namely the operant action entropy will converge to minimum with the learning process. And then OCPA learning system is applied to posture balanced control of two-wheeled flexible self-balanced robots. Robot does not have posutre balanced skill in initial state and the selecting probability of each operant in operant sets is equal. With the learning proceeding, the selected probabilities of optimal operant gradually tend to one and the operant action entropy gradually tends to minimum, and so robot gradually learned the posture balanced skill.

“…Therefore we will use probabilistic finite automata (PFA) to construct OC learning model. PFA [17,18] mainly studies the automata with stochastic factors and it is suitable to construct bionic learning system.…”

Section: Introductionmentioning

confidence: 99%

Self-organization stochastic fuzzy control based on OCPFA and applied on self-balanced robot

2010 8th World Congress on Intelligent Control and Automation

2010

This paper constructs a stochastic fuzzy controller to realize self-balancing control of two-wheeled robot. The rule of fuzzy controller is stochastic, which is automatically generated by an OCPFA learning system and optimized online. The OCPFA learning system is in fact a Probabilistic Finite Automata (PFA) which based on Skinner Operant Conditioning (Skinner OC), and it is composed by a bionic reorientation mechanism and an OC learning mechanism. The reorientation mechanism take for orientation function of robot posture balance as the goal-orientation, and response to the output control variable of fuzzy stochastic controller; Learning mechanism updated probability contribution of output control variable by using the response information form the environment and then choose the optimal control variable according to the new probability contribution. The OCPFA learning system gradually adapts to the environment changes by interacting with the dynamic environment based on which the learning system realize the self-organization of stochastic fuzzy control rules and show character of autonomous learning. In the same time the theoretical prove that the process of autonomous learning is convergent in sense of probability. The simulation indicate the stochastic fuzzy controller can successfully applied in twowheeled robot self-balancing without requiring the model of the robot and the robot can show control behavior of autonomous learning which similar to animal's OC learning behavior.