轮式移动机器人轨迹跟踪的最优控制

深入分析了轮式移动机器人的运动状态,建立了WMR路径偏差系统的非线性数学模型。应用小偏差线性化理论,将该多输入多输出非线性系统简化成一个单输入单输出线性系统。然后基于线性二次型调节器理论进行了系统最优控制器的设计,并针对该理论中加权矩阵Q与R难以确定的问题,从控制效果出发,采用自适应遗传算法对其进行了优化。实现了移动机器人对预定轨迹的满意鲁棒跟踪,同时满足了实时性要求。实验结果证明了该方法的正确性与实用性。

奇异摄动控制系统:理论与应用

系统地回顾了近年来奇异摄动控制技术的发展 ,主要包括线性奇异摄动系统的稳定性分析与镇定、最优控制、H∞ 控制 ,非线性奇异摄动系统的镇定、优化控制和基于积分流形的几何方法 ,以及奇异摄动技术在实际工业 ,例如机器人领域、航天技术领域和工程工业、制造业等中的成功应用 .并指出了这一领域进一步研究的方向

泵车布料机构运动学分析及轨迹规划控制建模与仿真

给出了以混凝土泵车各臂油缸长度为参变量的布料机构浇筑过程的轨迹规划计算方法。在解决布料机构运动学分析的逆问题时 ,采用了基于多峰值并行搜索的遗传算法来求解最优控制优化目标函数 ,并对施工过程进行了仿真

自适应LQR方法在载人潜水器动力定位中的应用研究

根据我国正在研制开发的某深海载人潜水器的特性及其对载人潜水器动力定位控制的要求,采用最优控制方法LQR与递推辨识系统参数相结合的方法———自适应LQR方法进行控制。仿真结果表明这种方法具有良好的控制效果。

一类载人潜水器的改进LQG控制研究

针对一类载人潜水器(MSV,MannedSubmersibleVehicle)在动力定位中多自由度之间存在的强耦合、非线性,以及系统参数的时变特性,文章采用带遗忘因子的递推最小二乘法和平方根法对系统参数进行辨识,然后在状态空间进行多输入多输出(MIMO)线性系统的最优控制研究。仿真结果表明,该两种改进LQG控制方法对于外界扰动以及系统的参数时变具有良好的控制效果,控制精度得到提高,为实际载人潜水器控制系统的多自由度动力定位控制提供了坚实的依据。

基于SQP算法的7000m载人潜水器有约束非线性控制分配研究

以7000m载人潜水器为研究对象,分析了潜水器的推进系统,并给出了6自由度推力转换模型,重点讨论了载人潜水器控制分配的优化问题.结合7000m载人潜水器的推进器布置和推进器特点,设计了优化准则代价函数,采用序列二次规划(sequential quadratic programm ing,SQP)算法求解了载人潜水器的非线性控制分配问题,通过半物理仿真平台实验验证了本文提出的控制分配算法的正确性和有效性.yh

混凝土泵车浇筑过程机器人化探讨

对混凝土泵车布料机构的运动姿态调整采取了最优控制 ,解决了泵车机器人化的运动分析的臂解问题 ,论述了泵车动态分析中应解决和注意的问题 ,给出了泵车控制自动化的程序流程图和控制系统图 ,从而为提高泵车施工过程的自动化和泵车的机器人化提出了新的思路

多移动机器人实时最优运动规划

研究多移动机器人的实时运动规划问题，提出了运动规划问题的体系结构，并将最优控制与智能决策相结合，建立实时专家系统，在其支持下，使机器人在时间—能量最优情况下完成规划策略。仿真结果表明该方法具有很强的实时性。

基于规则的移动机器人实时运动规划

研究移动机器人在动态环境中的导航与避障问题。为提高规划的实时性,提出了基于规则的规划方法,将多移动障碍环境机器人的运动规划分解为相对简单的单移动障碍运动规划,利用最优控制来实现单障碍的最优避障,并用智能搜索方法解决了移动机器人在多移动障碍环境中的实时运动规划问题。仿真实例表明了该方法的有效性。

最优控制具有非完整性的两轮驱动机器人小车

研究了移动机器人反馈控制问题．这里所考虑的机器人是一个两轮驱动的具有非完整性的移动机器人小车．考虑了笛卡儿空间中轨线跟踪问题的扩展．且表明只要参考小车保持运动，在虚设的参考小车位形周围的小车位形的稳定成为可能．提出了最优控制律并给出了仿真结果。

On-line algorithms for single machine scheduling with family setup times

The paper considers an on-line single machine scheduling problem where the goal is to minimize the makespan. The jobs are partitioned into families and a setup is performed every time the machine starts processing a batch of jobs of the same family. The scheduler is aware of the number of families and knows the setup time of each family, although information about a job only becomes available when that job is released. We give a lower bound on the competitive ratio of any on-line algorithm. Moreover, for the case of two families, we provide an algorithm with a competitive ratio that achieves this lower bound. As the number of families increases, the lower bound approaches 2, and we give a simple algorithm with a competitive ratio of 2.

State Estimation for Anaerobic Digesters using the ADM1

The optimization of full-scale biogas plant operation is of great importance to make biomass a competitive source of renewable energy. The implementation of innovative control and optimization algorithms, such as Nonlinear Model Predictive Control, requires an online estimation of operating states of biogas plants. This state estimation allows for optimal control and operating decisions according to the actual state of a plant. In this paper such a state estimator is developed using a calibrated simulation model of a full-scale biogas plant, which is based on the Anaerobic Digestion Model No.1. The use of advanced pattern recognition methods shows that model states can be predicted from basic online measurements such as biogas production, CH4 and CO2 content in the biogas, pH value and substrate feed volume of known substrates. The machine learning methods used are trained and evaluated using synthetic data created with the biogas plant model simulating over a wide range of possible plant operating regions. Results show that the operating state vector of the modelled anaerobic digestion process can be predicted with an overall accuracy of about 90%. This facilitates the application of state-based optimization and control algorithms on full-scale biogas plants and therefore fosters the production of eco-friendly energy from biomass.

Multi-Objective Reactive Power Support from Wind farms for Network Performance Enhancement

This paper examines the ability of the doubly fed induction generator (DFIG) to deliver multiple reactive power objectives during variable wind conditions. The reactive power requirement is decomposed based on various control objectives (e.g. power factor control, voltage control, loss minimisation, and flicker mitigation) defined around different time frames (i.e. seconds, minutes, and hourly), and the control reference is generated by aggregating the individual reactive power requirement for each control strategy. A novel coordinated controller is implemented for the rotor-side converter and the grid-side converter considering their capability curves and illustrating that it can effectively utilise the aggregated DFIG reactive power capability for system performance enhancement. The performance of the multi-objective strategy is examined for a range of wind and network conditions, and it is shown that for the majority of the scenarios, more than 92% of the main control objective can be achieved while introducing the integrated flicker control scheme with the main reactive power control scheme. Therefore, optimal control coordination across the different control strategies can maximise the availability of ancillary services from DFIG-based wind farms without additional dynamic reactive power devices being installed in power networks.

Hybrid optomechanics for Quantum Technologies

We review the physics of hybrid optomechanical systems consisting of a mechanical oscillator interacting with both a radiation mode and an additional matterlike system. We concentrate on the cases embodied by either a single or a multi-atom system (a Bose-Einstein condensate, in particular) and discuss a wide range of physical effects, from passive mechanical cooling to the set-up of multipartite entanglement, from optomechanical nonlocality to the achievement of non-classical states of a single mechanical mode. The reviewed material showcases the viability of hybridised cavity optomechanical systems as basic building blocks for quantum communication networks and quantum state-engineering devices, possibly empowered by the use of quantum and optimal control techniques. The results that we discuss are instrumental to the promotion of hybrid optomechanical devices as promising experimental platforms for the study of nonclassicality at the genuine mesoscopic level.