SIA OpenIR  > 机器人学研究室
水面移动机器人自主控制方法研究
其他题名Reserch on Autonomous Control Method for Unmanned Surface Vehicles
马玉龙1,2
导师韩建达
分类号TP242
关键词不确定性估计 主动建模 非线性控制 水面移动机器人(usv)
索取号TP242/M19/2013
页数121页
学位专业模式识别与智能系统
学位名称博士
2013-01-06
学位授予单位中国科学院沈阳自动化研究所
学位授予地点沈阳
作者部门机器人学研究室
摘要本文以水面移动机器人(USV)为背景,研究其在存在大量不确定因素及外界环境干扰条件下的状态及参数估计、跟踪控制及规划、机器人系统实验问题。本论文的具体内容安排如下:第1章,简单介绍了USV的研制与应用的情况;归纳了船舶系统在控制和规划两方面研究与进展,主要针对对跟踪控制和规划的现有方法进行了深入分析和综述,并指出了几种方法中存在的问题。第2章,由于模型是控制算法实现、控制器设计的基础,首先介绍了坐标系统和船舶的动力学模型,并此基础上得出了适合控制器设计的跟踪控制的数学模型。第3章,本章重点研究非线性系统在线估计,在简要介绍了用于非线性估计的UKF方法的基础上,提出了基于状态方差阵对角相似分解UKF(DSDUKF)和奇异值分解UKF(SVDUKF)的滤波器设计方法,放松了UKF算法对状态方差阵半正定性的要求,并在此基础上提出了加速度增强的UKF算法提高了算法精度和实时性。第4章,主要是跟踪控制设计,对二自由度模型在backstepping控制器设计方法上进行改进,增加了一个反馈项,使得系统的镇定收敛更快。针对三自由度跟踪模型,在考虑将来与规划合一的基础上,提出了速度和偏航角控制方案并进行了控制器设计。在此基础上提出了一种基于主动建模的鲁棒控制设计方法,对于包含难以准确建模动力学以及外来扰动等不确定性的系统,可以将其动力学模型合理简化,而将所有不确定因素以模型差的形式引入到系统中;将引入的模型差与系统原有状态组合成增广状态,构造出联合估计模型;再利用加速度增强UKF估计方法在线实时估计这个模型差;同时,将估计出的模型差反馈到控制量中,达到增强系统鲁棒性的目的。第5章,主要研究了在线动态规划算法。在研究规划方法发现,LP动态规划方法可以得到速度规划量,在前面第4章提出的控制策略基础上,将规划与控制合一设计,简化了设计步骤和控制器复杂度,仿真验证这种思想是可行有效的。 第6章, 介绍了水面机器人移动平台系统。在船载控制系统中,叙述了系统结构、硬件设备、系统集成等,接着着重阐述了平台系统的辨识及控制试验。在最后对全文作了总结。
其他摘要With the development of relative techniques, robots, especially various mobile robots, are becoming more and more applicable in the fields such as industry, national defense, public security, disaster rescue, and scientific exploration. However, most of the mobile robots still have to be controlled by human operator, namely, by the tele-operation, which restrains the extensive applications of mobile robots. Therefore, autonomous robot, which has environment adaptability and usually needs to work in dynamic environments with unpredictable disturbances and noises, has been becoming a focus to researchers. These ‘external’ uncertainties will further induce internal variations into the robot dynamics. Both the internal and external uncertainties will degrade the control performance and need compensation techniques to autonomously handling. As one of the key technologies of future unmanned systems environmental adaptability, the self-control technology has been widespread concern. Based on the Unmanned Surface Vehicle ( USV ), the research is foucs on state and parameter estimation of robot with internal uncertainty and external environmental disturbance, problem of tracking control and problem of converging design with control and planning as well. The main contents of this thesis are organized as follows: In chapter 1, the development and application of USV are introduced, and the research and progress of ship control and planning are also summarized, which mainly analyze and review existing methods of tracking control, and point out some problems existing in the methods. Therefore, the motivations and research topics are explained with respect to the existing problems in current methods. In chapter 2, Model was the foundation of control algorithm and controller design, therefore the coordinate system and the ship dynamics model are first introduced, and based on that, the mathematics models for the tracking controller design are obtained. In Chapter 3, focuses is on the online estimation of nonlinear system. After a brief introduction of the nonlinear estimate method for UKF is described, two filter design methods, diagonal similar decomposition (DSDUKF) and singular value decomposition UKF (SVDUKF) which are based on decomposition of state variance matrix, are put forward to relaxe positive semidefinite requirements of UKF algorithm to state variance. Furthermore, an acceleration enhanced UKF algorithm is proposed to improve the real time and precision of the algorithm. In Chapter 4, controller design is mainly introduced. First, a feedback item is added to Li ChangXi’s controller to improve the performance, namely system converge faster, for two degrees of freedom model. Second, to integrate the planning and control for future, a speed and yaw Angle control scheme was put forward and a controller is designed according to three degrees of freedom model. Based on this, an active model based control scheme is proposed. The complicated dynamics are reasonable simplified, the ignored elements as well as all the unpredictable uncertainties are summarized into a variable named model error. A reference model, which includes this model error as an extended state, is presented and a joint UKF estimator based on acceleration is proposed to actively estimate the model error. The estimated results are further fed back into controller for model error rejection. In chapter 5, the planning althgrams are described. In method research, LP dynamic planning method can get planning speed, and this can combine planning and control design, which simplify the design steps and controller complexity. The simulation validation of this thought is feasible and effective. In Chapter 6, the USV platform system is introduced in detail. In the control system of USV, the system structure, hardware equipment, system integration are described respectively, and the identification and experiment of the USV system are following. At last, the conclusion of the dissertation is following.
语种中文
产权排序1
文献类型学位论文
条目标识符http://ir.sia.cn/handle/173321/10785
专题机器人学研究室
作者单位1.中国科学院沈阳自动化研究所
2.中国科学院大学
推荐引用方式
GB/T 7714
马玉龙. 水面移动机器人自主控制方法研究[D]. 沈阳. 中国科学院沈阳自动化研究所,2013.
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