A sliding mode adaptive control system for a two-wheeled self-balancing robot

Abstract

The invention discloses a sliding mode adaptive control system for a two-wheel self-balancing robot, which includes a sensor measurement module, a main control chip and a motor system, wherein the sensor measurement module is used to collect motion parameters of the self-balancing robot, and the motor system is used to drive two The wheel self-balancing robot moves; the main control chip is connected with the sensor measurement module and the motor system, and is used to control the motion of the motor system according to the motion parameters collected by the sensor measurement module; The output equation of the adaptive controller is: U=-(K+φ)X. Adopting the technical scheme of the present invention, it is possible to adapt to the external environment while minimizing the impact of various disturbances in the external environment on the two-wheeled self-balancing robot without losing robustness. At the same time, the present invention also uses machine learning to The running-in of long-term accumulated factors makes the two-wheeled self-balancing robot have optimal performance, thus ensuring safety and stability.

Description

technical field [0001] The invention relates to the control field of a two-wheel self-balancing robot, in particular to a sliding-mode self-adaptive control system for a two-wheel self-balancing robot. Background technique [0002] The two-wheeled self-balancing robot is a self-balancing robot that uses sensors to sense its own state, and then controls the motor rotation through a control algorithm to achieve self-balancing. In recent years, with the continuous improvement of the two-wheeled self-balancing robot technology and the continuous reduction in cost, it has gradually become a means of transportation accepted by more people, making the two-wheeled self-balancing robot begin to change from the experimental research stage to a popular means of transportation. The environment and tasks are becoming more and more complex. [0003] At present, there are various types of balancing robots on the market, most of which use PID control algorithm. This algorithm collects the ...

AI Technical Summary

Problems solved by technology

This algorithm is simple and practical, but it is not the ideal controller, because in a complex operating environment, the algorithm does not handle it very well in many cases. For example, when there is interference from the outside world, the method will cause the control to shake When the interference is particularly large, it will also cause the balance car to lose its balance; at the same time, it is unreasonable for the PID algorithm to use the three members of proportionality, integral and differential for linear combination. Both robustness and system stability cannot be considered at the same time. Improving robustness will reduce stability, and conversely improving stability will reduce robustness

That is to say, if the robustness of the balance car using the PID algorithm is increased, it has a strong ability to keep upright, but once the angle deviation is too large, it will easily lose control, resulting in dangerous results. If the stability If it is increased, its robustness will be reduced, which will lead to a decrease in the ability of the balance car to bear the load

All in all, the robustness of the PID algorithm 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 the complex external environment and large-scale load changes. make the system chatter very large

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