自平衡车控制方法研究及仿真分析
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摘要
针对自平衡车的自平衡阶段控制和转向阶段控制问题,提出了基于参数优化自抗扰技术和改进PID技术的自平衡车控制方法,首先基于拉格朗日公式建立自平衡车非线性模型及其控制策略,运用自适应进化算法进行模型参数的优化调整,调然后用过滤过程安排改进的PID控制策略进行模型转向控制,最后使用Matlab/Simulink进行自平衡车控制效果仿真分析。仿真实验结果表明,提出的控制策略能够快速调整平衡车姿态,且具有较好的控制平衡和转向精度以及较强的抗干扰能力。
To solve the problems of self-balancing control and steering control,a control method based on parameter optimization auto disturbance rejection and improved PID is proposed. In the proposed algorithm,a self-balancing vehicle nonlinear model and its control strategy are established based on Lagrangian formula. And then,the adaptive evolutionary algorithm is used to optimize the model parameters,and the improved PID control strategy based on filtering process arrangement is invoked for model steering controlthe. Finally,simulation analysis of control effects is carried out using Matlab/Simulink. The experimental results show that the proposed control strategy can quickly adjust the attitude of the balance car,and has better control accuracy and strong anti-interference ability.
To solve the problems of self-balancing control and steering control,a control method based on parameter optimization auto disturbance rejection and improved PID is proposed. In the proposed algorithm,a self-balancing vehicle nonlinear model and its control strategy are established based on Lagrangian formula. And then,the adaptive evolutionary algorithm is used to optimize the model parameters,and the improved PID control strategy based on filtering process arrangement is invoked for model steering controlthe. Finally,simulation analysis of control effects is carried out using Matlab/Simulink. The experimental results show that the proposed control strategy can quickly adjust the attitude of the balance car,and has better control accuracy and strong anti-interference ability.
引文
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[12]齐晓慧,李杰,韩帅涛.基于BP神经网络的自适应自抗扰控制及仿真[J].兵工学报,2013,34(6):776-782.(Qi Xiao-hui,Li Jie,Han Shuai-tao.Adaptive active disturbance rejection control and its simulation based on BP neural network[J].Acta Armamentarii,2013,34(6):776-782.)
[13]CHAN R P M,STOL K A,HALKYARD C Roger.Review of modelling and control of two-wheeled robots[J].Annual Reviews in Control,2013,37(1):89-103.
[14]ELSAYED S M,SARKERR A,ESSAM Daryl L.An improved selfadaptive differential evolution algorithm for optimization problems[J].IEEE Transactions on Industrial Informatics,2013,9(1):89-99.
[15]郭赣,朱熙.基于过渡过程安排的PID控制器设计[J].宇航学报,2012,33(7):930-935.(Guo Gan,Zhu Xi.Design of PID controllers based on arranging the transient process[J].Journal of Astronautics,2012,33(7):930-935.)
[2]CHOI Y J,RYOO J R,DOH T Y.Steering system in a self-balancing electric vehicle[J].Journal of Institute of Control Robotics and Systems,2014,20(9):942-949.
[3]MUHAMMAD M,BUYAMIN S,AHMAD M N,et al. Takagi-Sugeno fuzzy modeling of a two-wheeled inverted pendulum robot[J]. Journal of Intelligent and Fuzzy Systems,2013,25(3):535-546.
[4]Xu Jian-xin,Guo Zhao-qin,LEE Tong-heng.Design and implementation of a Takagi-Sugeno-type fuzzy logic controller on a two wheeled mobile robot[J].IEEE Transactions on Industrial Electronics,2013,60(12):5717-5728.
[5]MARTINEZ-SOTO R,RODRIGUEZ A,CASTILLO O.Gain optimization for inertia wheel pendulum stabilization using particle swarm optimization and genetic algorithms[J].International Journal of Innovative Computing,Information and Control,2012,8(6):4421-4430.
[6]AG ARWAL A K,KUMAR S.Balancing inverted pendulum by angle sensing using fuzzy logic supervised PID controller optimized by genetic algorithm[J].Sensors and Transducers,2011,133(10):74-82.
[7]Yang Chen-guang,Li Zhi-jun,Cui Rong-xin.Neural network based motion control of an underactuated wheeled inverted pendulum model[J].IEEE Transactions on Neural Networks and Learning Systems,2014,25(11):2004-2016.
[8]朱学峰,周文彬,陈华艳.二级倒立摆的TS型逐级模糊神经网络控制[J].华南理工大学学报:自然科学版,2005,33(2):43-47.(Zhu Xue-feng,Zhou Wen-bin,Chen Hua-yan.Control of the doublelink inverted pendulum by using Takagi-Sugeno model based on gradual fuzzy neural network[J].Journal of South China University of Technology:Natural Science Edition,2005,33(2):43-47.)
[9] FANG Jian.The research on the application of fuzzy immune PD algorithm in the two-wheeled and self-balancing robot system[J].International Journal of Control and Automation,2014,7(10):109-118.
[10]CHAN R P M,STOL K A,HALKYARD C Roger. Review of modelling and control of two-wheeled robots[J].Annual Reviews in Control,2013,37(1):89-103.
[11]李晓峰,崔云飞,高学山.小摆角两轮机器人动力学建模及控制器设计[J].北京理工大学学报,2014,34(10):1049-1053.(Li Xiao-feng,Cui Yun-fei,Gao Xue-shan.Dynamic modeling and controller design for a small-pendular two-wheeled robot[J].Transaction of Beijing Institute of Technology,2014,34(10):1049-1053.)
[12]齐晓慧,李杰,韩帅涛.基于BP神经网络的自适应自抗扰控制及仿真[J].兵工学报,2013,34(6):776-782.(Qi Xiao-hui,Li Jie,Han Shuai-tao.Adaptive active disturbance rejection control and its simulation based on BP neural network[J].Acta Armamentarii,2013,34(6):776-782.)
[13]CHAN R P M,STOL K A,HALKYARD C Roger.Review of modelling and control of two-wheeled robots[J].Annual Reviews in Control,2013,37(1):89-103.
[14]ELSAYED S M,SARKERR A,ESSAM Daryl L.An improved selfadaptive differential evolution algorithm for optimization problems[J].IEEE Transactions on Industrial Informatics,2013,9(1):89-99.
[15]郭赣,朱熙.基于过渡过程安排的PID控制器设计[J].宇航学报,2012,33(7):930-935.(Guo Gan,Zhu Xi.Design of PID controllers based on arranging the transient process[J].Journal of Astronautics,2012,33(7):930-935.)