可重构模块机器人自适应模糊分散控制方法研究
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摘要
可重构模块机器人由一组具有标准连接接口的连杆模块和关节模块构成,它能够根据特定环境和任务的要求,快速地分离和重构成不同构形的机器人。可重构机器人系统具有时变、强耦合和非线性的动力学特征,其动力学控制是一个富有挑战性的研究问题,因此研究可重构模块机器人动力学控制方法具有重要的理论意义。
本文首先利用指数积公式给出了可重构模块机器人的运动学方程,并利用刚体的动力学方程几何公式得出系统的动力学模型;其次研究了可重构模块机器人的动力学控制问题,采用分散控制方法,针对子系统动力学模型研究了间接自适应模糊分散控制和直接自适应模糊分散控制两种方法,并通过Lyapunov稳定性理论分别对这两种控制方法进行了理论分析和证明;最后对两种控制方法进行了仿真验证,结果表明这两种控制方法都使系统达到了良好的动态性能,有效地实现了系统的轨迹跟踪控制。
With the rapid development of robot technology, robot is widely applied in many fields of remote, unstructured, and hazardous environments such as space and undersea exploration, mining, and construction. Fixed configuration robots have been uniquely designed to perform a specific task in the limited environments. In some situations, using robots with different parameters for a variety of task is possible when the task requirements are specified in advance. However, in many unstructured and less predictable environments, such as a nuclear waste retrieval site, aboard a space station, or a lunar base construction site, it is very difficult or impossible to design a single robotic system that can meet a wide range of task requirements. In these circumstances, it might be advantageous to deploy a reconfigurable and modular robot system which can be reconfigured itself into robots with different parameters which are individually well suited to the diverse task requirements. A reconfigurable modular robotic system consists of various link and joint modules with standardized connecting interfaces that can be easily separated and reassembled into many different configurations according to the changing task and changing environment. Such a robotic system has several advantages over conventional fixed-parameter design. First of all, it is very flexible and adaptable to different tasks requirements and working environments. The standardized design is easy for maintenance, modification, and transportation, and economical for manufacturing. Furthermore, it allows the re-use of the same part for different purposes, and thus reduces the total inventory of modules and the cost of redesign and repair. Scholars in the world have deeply studied the reconfigurable and modular robots, but focus on its automatic generation in kinematics and dynamics, kinematics calibration and distributed control. There are many challenging, just underway and marginal aspects, for example, inverse kinematics solution, uncertainty control, dynamic control, fault detection & diagnosis and tolerant control. These problems still need scholars pay much attention and effort. Hence, it is very necessary and significant to study further on reconfigurable and modular robot.
In this paper, the domestic and foreign researching status are analyzed, main research contents of reconfigurable and modular robots are summarized, the kinematics and dynamics function of reconfigurable and modular robot are researched, and as the key points the control schemes indirect and direct adapative fuzzy decentralized control are studied. The important content of this paper are as follows:
First, the kinematics function of reconfigurable and modular robot is set up by exponential product method. Base on rigid body Newton-Euler iterative algorithm, a iterative Newton-Euler function of the system is got by iterative of generalized velocity generalized acceleration and inverted iterative of generalized force. The dynamics function of reconfigurable and modular robot is set up.
Second, the indirect adaptive fuzzy decentralized control scheme is proposed for the dynamic control for trajectory tracking problem of reconfigurable and modular robots. The dynamics of reconfigurable and modular robots are firstly represented as a set of interconnected subsystems. Based on the sub-system dynamic function of reconfigurable and modular robot we have set up, indirect adaptive fuzzy decentralized controller is designed. Suppose subsystem dynamic model parameters are known, the ideal controller for ith subsystem is designed based on feedback linearization approach. The fuzzy logic systems are employed to approximate to two unknown functions in ideal controller using universal approximation property of fuzzy systems, and subsystem dynamic fuzzy modeling is implemented. The effect of interconnection and fuzzy approximation error are removed by fuzzy logic systems. Adaptive laws are designed to adjust parameters of fuzzy systems. All adaptive algorithms in the subsystem controller are derived from the sense of Lyapunov stability analysis, so that stability of the closed-loop system can be guaranteed. Simulation examples are presented to obtain a good tracking performance and demonstrate the effectiveness of the proposed decentralized controller. In this way fuzzy logic systems are employed to approximate to unknown function of plant model to implement fuzzy modeling for nonlinear uncertain systems. The control structure is slightly more complicated.
Third, the direct adaptive fuzzy decentralized control scheme is proposed for the sub-system dynamic function of reconfigurable and modular robots. There is no need of fuzzy modeling for nonlinear uncertain systems, fuzzy logic system acts as a controller. For sub-system dynamic function of reconfigurable and modular robot we have set up, suppose subsystem dynamic model parameters are known, the ideal controller for ith subsystem is designed based on feedback linearization approach. The fuzzy logic systems are employed to approximate to ideal controller using universal approximation property of fuzzy systems. The effect of interconnection and fuzzy approximation error are removed by fuzzy logic systems. Adaptive laws are designed to adjust parameters of fuzzy systems. All adaptive algorithms in the subsystem controller are derived from the sense of Lyapunov stability analysis, so that stability of the closed-loop system can be guaranteed. Because this method has no needs of fuzzy modeling, and directly approximate to ideal controller. A fuzzy system is less used than the preceding method. The control structure is more simplified than indirect adaptive fuzzy decentralized control scheme. The result of simulation shows that this method not only makes system to obtain a good tracking performance, but also greatly decrease the computing time than indirect adaptive fuzzy decentralized control.
At last, all the work is concluded in the summary, and combining what I have learned, prospect for some questions is laid out.
本文首先利用指数积公式给出了可重构模块机器人的运动学方程,并利用刚体的动力学方程几何公式得出系统的动力学模型;其次研究了可重构模块机器人的动力学控制问题,采用分散控制方法,针对子系统动力学模型研究了间接自适应模糊分散控制和直接自适应模糊分散控制两种方法,并通过Lyapunov稳定性理论分别对这两种控制方法进行了理论分析和证明;最后对两种控制方法进行了仿真验证,结果表明这两种控制方法都使系统达到了良好的动态性能,有效地实现了系统的轨迹跟踪控制。
With the rapid development of robot technology, robot is widely applied in many fields of remote, unstructured, and hazardous environments such as space and undersea exploration, mining, and construction. Fixed configuration robots have been uniquely designed to perform a specific task in the limited environments. In some situations, using robots with different parameters for a variety of task is possible when the task requirements are specified in advance. However, in many unstructured and less predictable environments, such as a nuclear waste retrieval site, aboard a space station, or a lunar base construction site, it is very difficult or impossible to design a single robotic system that can meet a wide range of task requirements. In these circumstances, it might be advantageous to deploy a reconfigurable and modular robot system which can be reconfigured itself into robots with different parameters which are individually well suited to the diverse task requirements. A reconfigurable modular robotic system consists of various link and joint modules with standardized connecting interfaces that can be easily separated and reassembled into many different configurations according to the changing task and changing environment. Such a robotic system has several advantages over conventional fixed-parameter design. First of all, it is very flexible and adaptable to different tasks requirements and working environments. The standardized design is easy for maintenance, modification, and transportation, and economical for manufacturing. Furthermore, it allows the re-use of the same part for different purposes, and thus reduces the total inventory of modules and the cost of redesign and repair. Scholars in the world have deeply studied the reconfigurable and modular robots, but focus on its automatic generation in kinematics and dynamics, kinematics calibration and distributed control. There are many challenging, just underway and marginal aspects, for example, inverse kinematics solution, uncertainty control, dynamic control, fault detection & diagnosis and tolerant control. These problems still need scholars pay much attention and effort. Hence, it is very necessary and significant to study further on reconfigurable and modular robot.
In this paper, the domestic and foreign researching status are analyzed, main research contents of reconfigurable and modular robots are summarized, the kinematics and dynamics function of reconfigurable and modular robot are researched, and as the key points the control schemes indirect and direct adapative fuzzy decentralized control are studied. The important content of this paper are as follows:
First, the kinematics function of reconfigurable and modular robot is set up by exponential product method. Base on rigid body Newton-Euler iterative algorithm, a iterative Newton-Euler function of the system is got by iterative of generalized velocity generalized acceleration and inverted iterative of generalized force. The dynamics function of reconfigurable and modular robot is set up.
Second, the indirect adaptive fuzzy decentralized control scheme is proposed for the dynamic control for trajectory tracking problem of reconfigurable and modular robots. The dynamics of reconfigurable and modular robots are firstly represented as a set of interconnected subsystems. Based on the sub-system dynamic function of reconfigurable and modular robot we have set up, indirect adaptive fuzzy decentralized controller is designed. Suppose subsystem dynamic model parameters are known, the ideal controller for ith subsystem is designed based on feedback linearization approach. The fuzzy logic systems are employed to approximate to two unknown functions in ideal controller using universal approximation property of fuzzy systems, and subsystem dynamic fuzzy modeling is implemented. The effect of interconnection and fuzzy approximation error are removed by fuzzy logic systems. Adaptive laws are designed to adjust parameters of fuzzy systems. All adaptive algorithms in the subsystem controller are derived from the sense of Lyapunov stability analysis, so that stability of the closed-loop system can be guaranteed. Simulation examples are presented to obtain a good tracking performance and demonstrate the effectiveness of the proposed decentralized controller. In this way fuzzy logic systems are employed to approximate to unknown function of plant model to implement fuzzy modeling for nonlinear uncertain systems. The control structure is slightly more complicated.
Third, the direct adaptive fuzzy decentralized control scheme is proposed for the sub-system dynamic function of reconfigurable and modular robots. There is no need of fuzzy modeling for nonlinear uncertain systems, fuzzy logic system acts as a controller. For sub-system dynamic function of reconfigurable and modular robot we have set up, suppose subsystem dynamic model parameters are known, the ideal controller for ith subsystem is designed based on feedback linearization approach. The fuzzy logic systems are employed to approximate to ideal controller using universal approximation property of fuzzy systems. The effect of interconnection and fuzzy approximation error are removed by fuzzy logic systems. Adaptive laws are designed to adjust parameters of fuzzy systems. All adaptive algorithms in the subsystem controller are derived from the sense of Lyapunov stability analysis, so that stability of the closed-loop system can be guaranteed. Because this method has no needs of fuzzy modeling, and directly approximate to ideal controller. A fuzzy system is less used than the preceding method. The control structure is more simplified than indirect adaptive fuzzy decentralized control scheme. The result of simulation shows that this method not only makes system to obtain a good tracking performance, but also greatly decrease the computing time than indirect adaptive fuzzy decentralized control.
At last, all the work is concluded in the summary, and combining what I have learned, prospect for some questions is laid out.
引文
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[2] Paredis C J J. An agent-based approach to the design of rapidly deployable fault tolerant manipulators [D]. Pittsburgh: Carnegie Mellon University, 1996.
[3] Chen I-Ming. Theory and applications of modular reconfigurable robotic systems[D]. Pasadena: California Institute of Technology, 1994.
[4] Chen I-Ming, Yang Guilin. Configuration independent kinematics for modular robots [C]// Proceedings of the IEEE International Conference on Robotics and Automation. New York: IEEE Press, 1996: 1440-1445.
[5] Yang Guilin. Kinematics, dynamics, calibration, and configuration optimization of modular reconfigurable robots[D]. Singapore: Nanyang Technological University, 1999.
[6] Yim M, Duff D G, Roufas K D. PolyBot: a modular reconfigurable robot[C]// Proceedings of the IEEE International Conference on Robotics and Automation. New York: IEEE Press, 2000: 514-520.
[7] Shimizu M, Ishiguro A, Kawakatsu T. Slimebot: a modular robot that exploits emergent phenomena[C]// Proceedings of the IEEE International Conference on Robotics and Automation. New York: IEEE Press, 2005: 2982-2987.
[8] Shen Weimin, Krivokon M, Chiu H, et al. Multimode locomotion via SuperBot robots[C]// Proceedings of the IEEE International Conference on Robotics and Automation. New York: IEEE Press, 2006: 2552-2557.
[9] Zhang Houxiang, González-Gómez J, Chen S Y, et al. A novel modular climbing caterpillar using low-frequency vibrating passive suckers[C]// Proceeding of IEEE/ASME International Conference on Advanced Intelligent Mechatronics, New York: IEEE Press, 2007.
[10] Yim M, Roufas K, Duff D, et al. Modular reconfigurable robots in space applications[J]. Autonomous Robots, 2003, 14 (2): 225-237.
[11] Yim M, Shen Weimin, Salemi B, et al. Modular self-reconfigurable robot systems: challenges and opportunities for the future[J]. IEEE Robotics & Automation Magazine, 2007, 14 (1): 43-52.
[12] DaidiéD, Barbey O, Guignard A, et al. The DoF-Box project: an educational kit for configurable robots[C]// Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics, New York: IEEE Press, 2007: 1-6.
[13] Lohmeier S, Buschmann T, Ulbrich H, et al. Modular joint design for performance enhanced humanoid robot LOLA[C]// Proceedings of the IEEE International Conference on Robotics and Automation. New York: IEEE Press, 2006: 88-93.
[14]郑浩峻,李铁民,王立平.可重构机器人树状拓扑结构[J].清华大学学报:自然科学版,2002,42 (11):1480-1483.
[15]李斌,吴镇炜,谈大龙,等.可重构模块机器人技术的探讨[J].信息与控制,2001,30 (7):684-688.
[16]刘明尧,张超平,谭跃钢.可重构模块化机器人构形设计[J].机械与电子,2005 (5): 53-55.
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