Vertical Balance Control Of Hexapod Robot Based On Adaptive Algorithm

The history of robot can be traced back to the 1930 s.Its design concept has always been to serve human beings,in order to achieve the tasks that human beings are difficult to complete.In the 1970 s,robot technology began to develop rapidly.The reason is that more and more disciplines participate in and various fields cross.Therefore,the development level of robot technology is more and more representative of a country's scientific and technological strength.In modern times,due to the improvement of robot technology,the tasks that can be completed are becoming more and more complex,and people's expectations for it are also rising.One of the most important indexes of modern robots is the adaptability to unstructured terrain,which is directly reflected in the shape design of robots.Hexapod shape is a form with high adaptability to unstructured terrain.This paper focuses on the control of upright balance process,which is a specific behavior of a hexapod robot(eccentric wheel foot type),with a specific robot as the reference object.First of all,this paper briefly describes the mechanical structure of the hexapod robot as the research object,and uses Lagrange equation method to model the vertical balance system of the hexapod robot.In the modeling process,the necessary simplification of the research object model is carried out on the premise that it will not cause serious impact on the results,so as to facilitate the research.Secondly,an adaptive control algorithm is designed for the upright balance dynamic model of hexapod robot,and the control scheme is verified by Simulink.Four groups of experiments are carried out,and the results show that the control scheme is effective.The design of the controller is PID control with secant signal compensation,and on-line adjustment of the control parameters according to the second derivative of the error.Finally,the hexapod robot is modeled on the robot simulation experiment platform v-rep,and the client program is written for upright training experiment.The results verify the correctness of the dynamic model and the feasibility of the upright training scheme.