Study On The Two Wheeled Self Balanced Robot Control System

Two self-balancing robot as an absolute instability of wheeled mobile robot, the mathematical model has many complex features. Because of its equation of state also has a non-linear, multi-variable characteristics, strong coupling, time-varying parameter uncertainty, which makes it an ideal platform to verify the various control algorithms, has important theoretical significance. It sports the same time flexible, simple structure, low cost, suitable for working in confined spaces and dangerous, such as search and rescue narrow space or get crowded office environment applications, but also as a special transport, special vehicles, special sports equipment Disabled protection system, used in many areas of modern society and the economy, has a broad application prospects. Currently, the two-wheeled self-balancing robot control algorithms in terms of almost all of the two-wheeled robot movement Balance at the beginning of the control algorithms are used PID control strategy for its calm, after further study other control strategies, including the kinds of deformation PID control strategy. You can see that the two-wheeled self-balancing robot almost all around PID control strategy to expand, due to the core issue of two-wheeled robot is its control of balance problems, so two-wheeled robot system for anti-jamming capability requirements are very strict. To this end, by analyzing the movement of two-wheeled self-balancing robot, has been self-balancing two-wheeled robot mathematical model and by analyzing the mathematical model, using simple and clever way to their decoupling to complex dynamics model decomposed into two independent subsystems, namely sports balance subsystem and heading control subsystem. As a result of the measurement system consisting of multi-inertial sensors to measure the movement of two-wheeled robot posture, in order to reduce the two-wheeled robot produced during measurement error and drift,we use kalman filtering algorithm for measurement data fusion, and after simulation verify the validity of kalman filtering algorithm. By studying the model reference adaptive control theory, design a model reference adaptive control algorithm to achieve balance control system, through pole placement method to control the issue of the course were calm. Ultimate realization of two self-balancing robot is self-balancing control, yaw control and speed tracking control. Finally, adaptive controller using MATLAB simulation, and the simulation results and the simulation results are compared conventional controller, adaptive controller is verified on the advantages of immunity and adjust the time. And simulation results show simulation results proved the effectiveness and superiority of the adaptive controller.