A two-wheeled self-balance robot self-adaptive sliding mode changing structure control method and system

Abstract

The invention discloses a two-wheeled self-balance robot self-adaptive sliding mode changing structure control method and system. According to a classical mechanics analytic method and based on a Lagrange algorithm based on energy analysis, molding of a kinetic equation of a two-wheeled self-balance robot of is realized, a sliding mode changing structure controller is designed according to the kinetic equation. The sliding mode changing structure controller comprises a speed sliding mold changing structure controller and an angle sliding mold changing structure controller; the speed sliding mold changing structure controller and the angle sliding mold changing structure controller give each other feedbacks; a feedback equation is that theta r=beta V; and self-adaptive controlling is carried out on the system on the basis of a function approximation mode. Through adoption of the technical scheme of the present invention, the modeling process is enabled to be more simplified and comprehensive; the robustness and the respond speed of the system are raised; simultaneously, since a mutual feedback relation exists between the speed and the angle of the system, when an inclination angle of the system is overlarge, the system will automatically decelerate; while the speed is reduced, the system will return to the balance position; in a condition of facing different road surface conditions, the system can adapt to external environment and large scope load changes, thereby guaranteeing the safety and stability of the system.

Description

technical field [0001] The invention relates to the field of robot control, in particular to an adaptive sliding mode variable structure control method and system for a two-wheel self-balancing robot. Background technique [0002] In recent years, with the deepening of mobile robot research and wider application fields, the environment and tasks it faces are becoming more and more complex. Robots often encounter some relatively narrow workplaces with many large corners. How to perform tasks flexibly and quickly in such a relatively complex environment has become a problem that people are quite concerned about. The concept of two-wheeled self-balancing robot is proposed under such background. Two-wheel self-balancing robot technology is a comprehensive technology across multiple disciplines. Its system model is a rather complex nonlinear and unstable dynamic model, and the two-wheel self-balancing robot system has a special structure and strong ability to adapt to terrain ch...

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Problems solved by technology

[0003] To simulate the two-wheeled self-balancing robot system, it is first necessary to know the mathematical model of the system, and then it is possible to simulate the system. Most of the modeling methods of the two-wheeled self-balancing robot in the prior art adopt the system modeling method One of the classical mechanical analysis method or the Lagrange method based on energy analysis, the consequences of using the classical mechanical analysis method alone for modeling is that the mechanical analysis process is too complicated; while the Lagrange method based on energy analysis alone ignores the energy in the system Changes

At the same time, the control algorithms of two-wheeled self-balancing robots in the prior art are mostly PID control algorithms, LQR control algorithms, optimal control algorithms, fuzzy control algorithms, etc. It is difficult to achieve a satisfactory control effect, the robustness is not good enough, the response speed is not fast enough, the system is unstable in the face of large disturbances, and when the external road conditions change, it cannot adapt to a more complex external environment and a wide range of loads It cannot automatically detect whether the load is added or not; the data processing method is not intelligent enough; the speed control method only depends on the change of the inclination angle, which is too simple; the chattering of the system is very large

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