姿态检测及模糊PID算法的自平衡小车
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摘要
针对小车控制系统的非线性、多变量等复杂性以及高耦合的运动控制特点,设计了一款以STM32F103RCT6微处理器为主控制器,通过模糊PID控制算法等进行姿态检测使其保持平衡的自平衡避障小车。通过卡尔曼滤波优化算法对陀螺仪和加速度计数据进行融合,并以最优姿态角和小车速度为反馈量构成平衡和速度的双闭环控制,再利用模糊PID控制算法实现小车系统的自平衡控制。本系统相对于传统的滤波算法和PID算法,具有耗时短、稳定性强、功耗低等特点。实验证明,自平衡小车能够稳定地实现自平衡控制及避障功能。
Aiming at the complexity of vehicle control system,such as nonlinearity,multivariable and high coupling motion control characteristics,a self-balanced obstacle avoidance vehicle with STM32 F103 RCT6 microprocessor as the main controller is designed,which is balanced by attitude detection using fuzzy PID control algorithm.The data of gyroscope and accelerometer are fused by Kalman filter optimization algorithm,and the optimal attitude angle and car speed are used as feedback variables to form a double closed-loop control of balance and speed.Then the self-balancing control of car system is realized by using fuzzy PID control algorithm.Compared with the traditional filtering algorithm and PID algorithm,this system has the characteristics of short time-consuming,strong stability and low power consumption.The experiment results show that the self balancing vehicle can achieve self balancing control and achieve obstacle avoidance.
Aiming at the complexity of vehicle control system,such as nonlinearity,multivariable and high coupling motion control characteristics,a self-balanced obstacle avoidance vehicle with STM32 F103 RCT6 microprocessor as the main controller is designed,which is balanced by attitude detection using fuzzy PID control algorithm.The data of gyroscope and accelerometer are fused by Kalman filter optimization algorithm,and the optimal attitude angle and car speed are used as feedback variables to form a double closed-loop control of balance and speed.Then the self-balancing control of car system is realized by using fuzzy PID control algorithm.Compared with the traditional filtering algorithm and PID algorithm,this system has the characteristics of short time-consuming,strong stability and low power consumption.The experiment results show that the self balancing vehicle can achieve self balancing control and achieve obstacle avoidance.
引文
[1]杨凌霄,李晓阳.基于卡尔曼滤波的两轮自平衡车姿态检测方法[J].计算机仿真,2014,31(6):406-409.
[2]赵智.基于STM32的二轮自平衡电动车系统研制[D].武汉:华中师范大学,2013.
[3]王永虹,徐炜,郝立平.STM32系列ARM Cortex-M3微控制器原理与实践[M].北京:北京航空航天大学出版社,2008.
[4]张团善,何颖.卡尔曼滤波在两轮自平衡代步车姿态检测中的应用[J].单片机与嵌入式系统应用,2014(5):33-35.
[5]袁泽睿.两轮自平衡机器人控制算法的研究[D].哈尔滨:哈尔滨工业大学,2006.
[6]季鹏飞,朱燕.基于STM32的两轮自平衡小车控制系统设计[J].电子科技,2014,27(11):96-99.
[7]梁光胜,杜梦楠.基于互补滤波的两轮自平衡车姿态控制[J].测控技术,2015,34(5):72-74.
[2]赵智.基于STM32的二轮自平衡电动车系统研制[D].武汉:华中师范大学,2013.
[3]王永虹,徐炜,郝立平.STM32系列ARM Cortex-M3微控制器原理与实践[M].北京:北京航空航天大学出版社,2008.
[4]张团善,何颖.卡尔曼滤波在两轮自平衡代步车姿态检测中的应用[J].单片机与嵌入式系统应用,2014(5):33-35.
[5]袁泽睿.两轮自平衡机器人控制算法的研究[D].哈尔滨:哈尔滨工业大学,2006.
[6]季鹏飞,朱燕.基于STM32的两轮自平衡小车控制系统设计[J].电子科技,2014,27(11):96-99.
[7]梁光胜,杜梦楠.基于互补滤波的两轮自平衡车姿态控制[J].测控技术,2015,34(5):72-74.