Aiming at the inaccurately modeling and some uncertain existing in servo system seriously affected the control quality and the instability problem, sliding mode control algorithm with improved reaching law is proposed in this paper. The improved reaching law is used to weaken the chattering problem existing in the sliding mode control. Also the kalman filter is used to inhibit the interference, which makes the servo system have strong anti-interference ability and the ability of weakening the chattering problem existing in the sliding mode control. The method of sliding mode control is the basis of a number of patents and patents pending. The simulation results show that the algorithm can effectively inhibit the external disturbance and noise existing in the system, and make the system have strong anti-interference ability. At the same time, the chattering also is obviously inhibited, and the method makes the system stability and control quality been further improved.
The sliding mode control algorithm based on grey prediction theory is proposed in this study, aiming at the uncertainties in the servo system of fruit harvesting robot and the external disturbances that may affect the control quality of conventional sliding mode control algorithm. The proposed algorithm uses the grey theory ability to unknown information data to establish the grey model to the uncertainty and real-time compensate the unmodeled dynamics and the interference signal of system. Meanwhile, an improved reaching law direction is proposed to resist chattering and improve control accuracy. The simulation results show that the proposed sliding mode control algorithm effectively predicts and compensates the unmodeled dynamics and disturbances signal in the DC motor servo system of the fruit harvesting robot and improves the control precision of controller which provides the theoretical basis for the industrial application based on the grey prediction theory of sliding mode control algorithm.
In this paper, a kind of infinite, local finite tree T, named a controlled tree, is introduced. Some strong limit properties, such as the strong law of large numbers and the asymptotic equipartition property, for nonhomogeneous Markov chains indexed by T, are established. The outcomes are the generalizations of some well-known results.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.