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 f...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.展开更多
This study mainly concerns a motion model and the main control algorithm of two-wheel self-balancing vehicle models.Details of the critical parameters fetching and output value of two-wheel self-balancing vehicle mode...This study mainly concerns a motion model and the main control algorithm of two-wheel self-balancing vehicle models.Details of the critical parameters fetching and output value of two-wheel self-balancing vehicle models are introduced,including those concerning balance control,speed control and direction control.An improved cascade coupling control scheme is proposed for two-wheel vehicles,based on a proportional-integral-derivative(PID)control algorithm.Moreover,a thorough comparison between a classic control system and the improved system is provided,and all aspects thereof are analyzed.It is determined that the control performance of the two-wheel self-balancing vehicle system based on the PID control algorithm is reliable,enabling the vehicle body to maintain balance while moving smoothly along a road at a fast average speed with better practical per-formance.展开更多
基金supported by the National Natural Science Foundation of China (No. 60774077)the National High Technology Development Plan(863) of China (No. 2007AA04Z226)+1 种基金the Beijing Municipal Education Commission Key Project (No. KZ200810005002)the Beijing Natural Science Foundation Project (No. 4102011)
文摘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.
文摘This study mainly concerns a motion model and the main control algorithm of two-wheel self-balancing vehicle models.Details of the critical parameters fetching and output value of two-wheel self-balancing vehicle models are introduced,including those concerning balance control,speed control and direction control.An improved cascade coupling control scheme is proposed for two-wheel vehicles,based on a proportional-integral-derivative(PID)control algorithm.Moreover,a thorough comparison between a classic control system and the improved system is provided,and all aspects thereof are analyzed.It is determined that the control performance of the two-wheel self-balancing vehicle system based on the PID control algorithm is reliable,enabling the vehicle body to maintain balance while moving smoothly along a road at a fast average speed with better practical per-formance.