极点配置模型参考自适应控制系统

本文提出了一种直接式模型参考自适应控制系统新结构,它包含一个具有期望极点配置的参考模型和使被控对象完全跟踪参考模型的自适应律。这种系统只需利用被控对象的输入输出信息,并且允许对象是非最小相位的。     

基于扰动补偿的无微分模型参考自适应控制系统设计

针对一类含有参数不确定性和未知非线性扰动的系统,本文提出一种基于扰动补偿的无微分模型参考自适应控制方法,实现系统输出对参考模型输出信号的高精度跟踪.首先,利用被控对象模型信息设计扰动估计器,对系统非线性扰动进行在线估计;其次,基于非线性扰动估计值设计参考模型和无微分参数更新律,构建无微分模型参考自适应控制器,建立基于扰动补偿和状态反馈的自适应控制律,以消除参数不确定性和非线性扰动对系统输出的影响,保证系统输出对参考模型输出的准确跟踪;然后,给出闭环系统误差信号收敛条件和控制器参数整定方法;最后,通过数值仿真验证所提方法的有效性和优越性.     

基于反步法的主从航天器相对姿态控制

对主从航天器的相对姿态控制问题,考虑从航天器系统不确定因素,提出了一种基于反步法的姿态控制方法,并引入自适应控制律.该方法首先根据主从航天器的相对位置信息,解算出从航天器观测轴指向主航天器以及从航天器跟踪主航天器轨道坐标系等两种任务的期望姿态;然后基于修正罗德里格参数(MI(P)描述的从航天器姿态误差动力学模型设计了姿态控制器以及针对航天器惯量的不确定性设计了自适应控制律;并基于Lyapunov方法从理论上证明了该方法能够实现全局渐近稳定的相对姿态控制.最后将该方法应用于某编队飞行任务,仿真结果表明此控制器能够实现其编队飞行控制,具有良好的控制性能.     

基于观测器的网络化多智能体预测控制

研究含模型不确定性的刚性航天器输入受限时的姿态跟踪控制设计问题.针对修改的罗德里格斯姿态参数描述的航天器姿态跟踪动力学模型,基于一种有界非线性连续函数和修改的罗德里格斯姿态参数自身有界性,设计鲁棒自适应状态反馈受限控制器,不确定参数的自适应更新律可保证在线估计参数的有界性.通过所提出的输入受限控制设计方法给出输入受限幅值、期望轨迹上界、模型不确定性上界与控制器增益之间的定量关系,并采用李亚普诺夫方法证明通过选择合适的控制器参数可以保证闭环系统角速度误差渐近收敛到零,且姿态跟踪误差收敛到原点小邻域,同时保证控制量始终在预先给定的受限范围内.仿真结果验证了所设计的控制器可在输入受限的情况下完成控制目标并有效抑制模型的不确定性影响.     

时变轨迹下工业过程鲁棒模糊预测控制

针对具有不确定性、外界未知干扰和跟踪轨迹变化的非线性工业过程,提出一种时变轨迹鲁棒模糊预测控制方法。该方法将非线性工业过程根据模糊规则表示为Takagi-Sugeno(T-S)模糊模型,并扩展输出跟踪误差到模糊模型中得到新型多自由度状态空间模型。针对扩展模型设计控制器,并给出基于线性矩阵不等式形式的稳定性充分条件,通过求解稳定性条件中参数计算对应子模型的控制律,并根据当前时刻的系统状态确定每个子模型和控制律的权值。同时,由于在实际生产过程中被控变量需要根据外界变化来实时调节,在设计控制器时充分考虑跟踪轨迹变化的影响。在啤酒发酵罐温度系统仿真研究结果表明设计的控制器能够满足控制需求,保证系统的稳定性和最优性能。     

基于T-S模糊模型非线性网络控制系统改进H∞跟踪控制

研究一类非线性网络控制系统改进H∞跟踪控制问题, 该类网络控制系统中非线性被控对象和被跟踪对象分别采用Takagi-Sugeno(T-S)模糊模型和线性稳定参考模型描述. 首先通过综合考虑网络中的数据传输时滞和数据丢包影响, 采用输入时滞法和并行分布补偿技术, 建立基于零阶保持器刷新时刻的系统状态跟踪误差模型. 然后利用改进的自由权矩阵方法, 并结合Lyapunov直接法给出系统满足H∞跟踪性能的充分条件以及模糊控制器的设计方法. 最后仿真实例表明本文方法的有效性和相比已有方法的优越性.     

一类控制输入饱和受限的不确定系统滑模控制

本文研究控制输入饱和受限情况下不确定系统的滑模控制问题,其中,被控对象同时存在状态矩阵不确定性和控制增益矩阵不确定性.设计了一种积分型切换面和一个具有特殊结构的滑模控制律,可以在参数不确定和控制受限影响下保证系统状态轨迹有限时间内到达指定的切换面,利用等价控制律方法给出了滑模动态渐近稳定的充分条件.数值仿真例子验证了本文算法的有效性.     

基于整形器的两轴柔性航天器自适应姿态控制

针对非线性柔性航天器大角度姿态机动问题,为了抑制在高速运动中产生的振动,提出了一种整形参考轨迹和自适应滑模姿态跟踪控制方法.跟踪过程中所使用的期望姿态角是由最优任意时延(Optimal Arbitrary Time-delay,OAT)输入整形器整形所得参考指令作用于参考模型得到,利用该期望姿态角能够得到更好地跟踪和抑制振动效果.针对非线性柔性航天器参数的未知时变特性和欠驱动特性,设计的自适应滑模控制器不仅能够完成期望的姿态角跟踪,同时还能够有效抑制姿态机动过程中的振动,并应用于单轴非线性柔性航天器的大角度姿态控制系统,并进行了仿真验证.结果表明,所提出的方法是可行而有效的.     

太阳帆航天器地球逃逸轨道解析最优控制律

针对太阳帆航天器地球逃逸轨道控制问题,给出一种新的解析最优控制律.该控制律可使航天器在逃逸过程中轨道能量变化速率最大,从而保证逃逸时间最短.考虑到地球逃逸轨道形状,引入改进春分点轨道根数对航天器运动学方程进行描述,并给出了地球逃逸轨道最优控制律的推导过程.仿真分析表明,该控制律计算速度较快,而且可以根据航天器状态实时计算姿态控制角,因此比较适用于未来太阳帆航天器在轨自主控制系统.     

机械臂轨迹跟踪滑模控制中的抖振抑制法研究

研究机械臂轨迹跟踪优化控制问题,针对机械臂轨迹跟踪滑模控制中存在的抖振问题,采用趋近律的方法来进行有效改善。用传统滑模控制趋近律的基础上,将饱和函数引入到了趋近律的设计中,提出一种改进的趋近律,并根据改进的趋近律设计了相应的滑模控制策略。将新的滑模控制策略应用到机械臂的轨迹跟踪控制中,对系统控制效果进行了验证。仿真结果表明,控制策略不仅有效地抑制了机械臂滑模控制中的抖振问题,而且保证了机械臂系统对期望轨迹的快速跟踪性,具有更好的趋近特性和收敛特性。     

Group path formation

When people make choices within a group, they are frequently influenced by the choices made by others. We have experimentally explored the general phenomenon of group behavior where an early action facilitates subsequent actions. Our concrete instantiation of this problem is group path formation where people travel between destinations with the travel cost for moving onto a location inversely related to the frequency with which others have visited the location. We compare the resulting paths to optimal solutions [minimal Steiner trees (MSTs)] and the "Active Walker" model of pedestrian motion from biophysics. There were systematic deviations from beeline pathways in the direction of MST. These deviations showed asymmetries (people took different paths from A to B than they did from B to A) and varied as a function of the topology of the destinations, the duration of travel, and the absolute scale of the world. The Active Walker model accounted for many of these results, in addition to correctly predicting the approximate spatial distribution of steps.     

Multi-robot coalition formation

As the community strives towards autonomous multi-robot systems, there is a need for these systems to autonomously form coalitions to complete assigned missions. Numerous coalition formation algorithms have been proposed in the software agent literature. Algorithms exist that form agent coalitions in both super additive and non-super additive environments. The algorithmic techniques vary from negotiation-based protocols in multi-agent system (MAS) environments to those based on computation in distributed problem solving (DPS) environments. Coalition formation behaviors have also been discussed in relation to game theory. Despite the plethora of MAS coalition formation literature, to the best of our knowledge none of the proposed algorithms have been demonstrated with an actual multi-robot system. There exists a discrepancy between the multi-agent algorithms and their applicability to the multi-robot domain. This paper aims to bridge that discrepancy by unearthing the issues that arise while attempting to tailor these algorithms to the multi-robot domain. A well-known multi-agent coalition formation algorithm has been studied in order to identify the necessary modifications to facilitate its application to the multi-robot domain. This paper reports multi-robot coalition formation results based upon simulation and actual robot experiments. A multi-agent coalition formation algorithm has been demonstrated on an actual robot system.     

多机器人编队离散模型及队形控制稳定性分析

针对包含绕心运动情况下的多机器人编队进行离散建模,并利用该模型解决保持队形期望前端始终朝着编队前进方向的控制问题.以控制多机器人编队收敛到期望的队形并镇定到预设运动规律上为目标,定义了一类通信拓扑图,基于该类图提出了一种分布式协同控制算法.给出了该控制算法下编队系统渐进稳定的充分必要条件及反馈控制参数的收敛域.证明了在该充分必要条件下可实现编队收敛到期望的队形和预设运动规律上的目标.仿真实验表明,在该算法控制下多机器人编队较好地收敛到期望队形并按预设规律运动,且过程中始终保持队形期望前端朝着编队前进方向,进而验证了该算法的有效性和正确性.     

Concept formation in design

This paper presents a computationally tractable view on where simple design concepts come from by proposing a paradigm for the formation of design concepts based on the emergence of patterns in the representation of designs. It is suggested that these design patterns form the basis of concepts. These design patterns once learned are then added to the repertoire of known patterns so that they do not need to be learned again. This approach uses the notion called the `loosely-wired brain'. This paper elaborates on this idea primarily through implemented examples drawn from the genetic engineering of evolutionary systems, and the qualitative representation of shapes and their multiple representations.     

对队形控制的思考

首先指出多机器人队形控制研究要解决的主要问题,并较为完整地阐述了当前国内外相关研究的状况．然后总结了队形控制的各种研究方法,并指出当前队形控制研究中存在的一些问题以及在实际设计队形控制系统时应考虑的问题．最后指出了今后的研究方向．     

The formation of protostars

A review is given of the physical and numerical problems which arise in the theory of star formation. The evolution of protostars based on numerical calculations is qualitatively described. From the discussion of the basic physics conclusions are drawn regarding the demands on the numerical methods. Some new ideas are outlined concerning the treatment of non-stationary radiating spherical shock fronts with the aid of an implicit difference scheme.     

行军启发的多机器人紧密队形保持策略

多机器人协同编队是多机器人研究的关键技术之一,是通过控制队伍中的机器人,使其按照预定的队形进行前进。本文基于领航者-追随者结构的编队控制方法,从军队急行军中得到启发,设计了同列依次替补和末排向内收拢原则,提出多机器人紧密队形保持策略。并引入能耗和队形紧密度两个编队性能评价指标,验证了多机器人紧密队形保持策略的优越性。仿真实验证明了多机器人紧密队形保持策略的有效性。     

An algorithm of molasse formation

An algorithm is described for the simulation of the geological processes involved in the formation and evolution of a molasse basin. Variables include continuous subsidence, source-area denudation, formation of cyclothems, and fluviatile meandering     

Computational modeling of city formation

Although a burst of recent research in economics has examined how industries form, a majority of it considers highly simplified models. In this paper, we use computational modeling techniques to expand from traditional, simple, analytically tractable economic models to more complex two dimensional landscapes. Using the basic theories developed in earlier research, we examine what factors cause cities to emerge, including: transportation costs, the percentage of workers in a population, and the elasticity of substitution. These three factors should cause workers and firms to agglomerate, causing cities to emerge out of a scattered population.     

Non-additive multi-objective robot coalition formation

Manifold increase in the complexity of robotic tasks has mandated the use of robotic teams called coalitions that collaborate to perform complex tasks. In this scenario, the problem of allocating tasks to teams of robots (also known as the coalition formation problem) assumes significance. So far, solutions to this NP-hard problem have focused on optimizing a single utility function such as resource utilization or the number of tasks completed. We have modeled the multi-robot coalition formation problem as a multi-objective optimization problem with conflicting objectives. This paper extends our recent work in multi-objective approaches to robot coalition formation, and proposes the application of the Pareto Archived Evolution Strategy (PAES) algorithm to the coalition formation problem, resulting in more efficient solutions. Simulations were carried out to demonstrate the relative diversity in the solution sets generated by PAES as compared to previously studied methods. Experiments also demonstrate the relative scalability of PAES. Finally, three different selection strategies were implemented to choose solutions from the Pareto optimal set. Impact of the selection strategies on the final coalitions formed has been shown using Player/Stage.