自行车机器人非线性系统的控制及实现
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摘要
自行车机器人是近年来机器人学术界提出的一种全新的智能运输(或交通)工具的概念,由于自行车机器人本身是一个欠驱动的具有侧向不稳定的非完整系统,其两轮纵向布置,与地面无滑动接触,同时自行车动力学模型具有对称性特征,其动力学特性较为复杂,因此,自行车机器人的自稳定控制相当困难。国内针对自行车机器人的动力学建模、控制算法及系统辨识的研究(相对)很少,而自行车机器人的动力学建模与控制等问题长期以来都是机器人学领域的研究热点。
本文以自行车机器人的动力学建模、控制和系统辨识问题作为研究对象,利用拉格朗日方法建立了转动车把调节方式下的自行车机器人的动力学模型,并对所建的模型进行滑模控制,同时,利用采集的车体倾角和车把转角数据,进行了基于ARX模型的线性系统辨识和ANFIS自适应模糊神经网络的非线性系统辨识的仿真研究。设计了自行车机器人实物样机,并完成了自行车机器人的控制实验。文中的主要内容和成果如下：
1、采用拉格朗日方法建立了依靠调节车把保证自行车机器人平衡的SISO非线性动力学模型。使用近似线性化的方法将非线性模型进行了线性化,设计了基于LQR的线性系统控制器,使自行车机器人在小范围内的变化可以得到有效的控制。
2、分析了SISO非线性动力学模型的特征,并提出了基于串级滑模、分层滑模以及稳定滑模控制器对自行车机器人模型的控制方法,从而能够更加有效的实现自行车机器人的稳定控制。
3、基于ARX模型的线性系统辨识以及ANFIS模型的非线性系统辨识工作原理,利用采集车体稳定运动时车体倾角与车把转角的数据,进行了自行车机器人的系统辨识。基于T-S模型的ANFIS网络能够很好地逼近实际自行车机器人的非线性系统,并且辨识精度高,优于ARX模型的线型辨识方法。通过辨识得到的输入和输出之间的模糊推理结论,为今后进行自行车机器人系统的有效控制提供了一定的参考价值。
4、基于ARM9嵌入式系统、单片机、高精度的MTi传感器等硬件平台设计了自行车机器人的实验样机,完成了系统数据采集功能,以自行车机器人单输入单输出非线性系统为例,进行了相应控制算法的实验。
本文的研究从非线性、欠驱动的角度为自行车机器人提供了新的控制思路,利用系统辨识模型方法对自行车机器人进行建模,丰富了本实验室对自行车机器人的研究成果,为进一步研究奠定了基础。
The bicycle robot that is proposed by academics in the field of robot in recent years is a new conception of intelligent transport (or traffic) tool. Because it is an under-actuated non-integrated system with lateral instability, it's two wheels are longitudinal and has non-sliding contact with the ground, meanwhile it's dynamics modeling possesses a kind of symmetrical feature, and its dynamic characteristics are complicated. Therefore, it is difficult to control the bicycle robot stable. In domestic the researches about bicycle dynamics modeling, control algorithm and system identification on the robot prototype platform are few, therefore the dynamic modeling and control should be the hot topic in robotics for long time.
In this paper, bicycle robot dynamics modeling, control and system identification problems are taken as the research objects. A dynamic model of adjusting bicycle handlebar control is presented, which is based on Lagrange method, and the algorithms of sliding mode control are designed, meanwhile, By using the test data of tilt angle and handlebar angle, linear and nonlinear identification have been done based on the ARX model and adaptive network-based fuzzy inference systems(ANFIS). A real bicycle robot is designed and experiments on control of bicycle robot are performed. The main contents can be summarized as follows:
1. A kind of steering control single-input single-output dynamic model for bicycle robot is presented, which is based on Lagrange method. It's nonlinear system is approximately linearized, and the designed controller of LQR is presented, which can effectively control bicycle robot when the tilt angle is in small value.
2. The characteristics of SISO nonlinear dynamic model are analyzed, and the cascade sliding-mode controller, hierarchical sliding-mode controller and stable sliding -mode controller are applied to the bicycle robot nonlinear system, which enable bicycle robot more stable.
3. Based on linear ARX model and non linear ANFIS model, The identifications of bicycle robot system are completed through the data of handlebar angle and those of inclination angle which are gathered when bicycle robot is stable. Simulation result by ANFIS based on T-S model could be very similar to the actual test data of nonlinear bicycle robot sysytem, and it's identification precision is higher than that of linear ARX model. The obtained conclusions of fuzzy inference between input and output by above identificaton methods can provide some reference value for effective control on bicycle robot system in future.
4. The bicycle robot for experiment has been designed on the platform of hardware including ARM9 embedded system, microcontroller, high-precision three-dimensional MTI sensor and so on, which has finished the sampling of systematical data. A series of experiments have been done based on the prototype, taking the bicycle robot SISO nonlinear system as example, on which the corresponding control experiment is carried.
From the non-linear and underactuated point of view, the research provides a new idea for bicycle robot control. The modeling by methods of system identification enriches our laboratory researches on bicycle robot, and lay the foundation for further study.
本文以自行车机器人的动力学建模、控制和系统辨识问题作为研究对象,利用拉格朗日方法建立了转动车把调节方式下的自行车机器人的动力学模型,并对所建的模型进行滑模控制,同时,利用采集的车体倾角和车把转角数据,进行了基于ARX模型的线性系统辨识和ANFIS自适应模糊神经网络的非线性系统辨识的仿真研究。设计了自行车机器人实物样机,并完成了自行车机器人的控制实验。文中的主要内容和成果如下：
1、采用拉格朗日方法建立了依靠调节车把保证自行车机器人平衡的SISO非线性动力学模型。使用近似线性化的方法将非线性模型进行了线性化,设计了基于LQR的线性系统控制器,使自行车机器人在小范围内的变化可以得到有效的控制。
2、分析了SISO非线性动力学模型的特征,并提出了基于串级滑模、分层滑模以及稳定滑模控制器对自行车机器人模型的控制方法,从而能够更加有效的实现自行车机器人的稳定控制。
3、基于ARX模型的线性系统辨识以及ANFIS模型的非线性系统辨识工作原理,利用采集车体稳定运动时车体倾角与车把转角的数据,进行了自行车机器人的系统辨识。基于T-S模型的ANFIS网络能够很好地逼近实际自行车机器人的非线性系统,并且辨识精度高,优于ARX模型的线型辨识方法。通过辨识得到的输入和输出之间的模糊推理结论,为今后进行自行车机器人系统的有效控制提供了一定的参考价值。
4、基于ARM9嵌入式系统、单片机、高精度的MTi传感器等硬件平台设计了自行车机器人的实验样机,完成了系统数据采集功能,以自行车机器人单输入单输出非线性系统为例,进行了相应控制算法的实验。
本文的研究从非线性、欠驱动的角度为自行车机器人提供了新的控制思路,利用系统辨识模型方法对自行车机器人进行建模,丰富了本实验室对自行车机器人的研究成果,为进一步研究奠定了基础。
The bicycle robot that is proposed by academics in the field of robot in recent years is a new conception of intelligent transport (or traffic) tool. Because it is an under-actuated non-integrated system with lateral instability, it's two wheels are longitudinal and has non-sliding contact with the ground, meanwhile it's dynamics modeling possesses a kind of symmetrical feature, and its dynamic characteristics are complicated. Therefore, it is difficult to control the bicycle robot stable. In domestic the researches about bicycle dynamics modeling, control algorithm and system identification on the robot prototype platform are few, therefore the dynamic modeling and control should be the hot topic in robotics for long time.
In this paper, bicycle robot dynamics modeling, control and system identification problems are taken as the research objects. A dynamic model of adjusting bicycle handlebar control is presented, which is based on Lagrange method, and the algorithms of sliding mode control are designed, meanwhile, By using the test data of tilt angle and handlebar angle, linear and nonlinear identification have been done based on the ARX model and adaptive network-based fuzzy inference systems(ANFIS). A real bicycle robot is designed and experiments on control of bicycle robot are performed. The main contents can be summarized as follows:
1. A kind of steering control single-input single-output dynamic model for bicycle robot is presented, which is based on Lagrange method. It's nonlinear system is approximately linearized, and the designed controller of LQR is presented, which can effectively control bicycle robot when the tilt angle is in small value.
2. The characteristics of SISO nonlinear dynamic model are analyzed, and the cascade sliding-mode controller, hierarchical sliding-mode controller and stable sliding -mode controller are applied to the bicycle robot nonlinear system, which enable bicycle robot more stable.
3. Based on linear ARX model and non linear ANFIS model, The identifications of bicycle robot system are completed through the data of handlebar angle and those of inclination angle which are gathered when bicycle robot is stable. Simulation result by ANFIS based on T-S model could be very similar to the actual test data of nonlinear bicycle robot sysytem, and it's identification precision is higher than that of linear ARX model. The obtained conclusions of fuzzy inference between input and output by above identificaton methods can provide some reference value for effective control on bicycle robot system in future.
4. The bicycle robot for experiment has been designed on the platform of hardware including ARM9 embedded system, microcontroller, high-precision three-dimensional MTI sensor and so on, which has finished the sampling of systematical data. A series of experiments have been done based on the prototype, taking the bicycle robot SISO nonlinear system as example, on which the corresponding control experiment is carried.
From the non-linear and underactuated point of view, the research provides a new idea for bicycle robot control. The modeling by methods of system identification enriches our laboratory researches on bicycle robot, and lay the foundation for further study.
引文
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