一种水下机器人运动的过程神经元控制

过程神经网络是传统神经网络的拓展, 增加了一个对于时间的聚合算子, 从而更好地模拟了生物神经元的信息处理机制. 这是由于水下机器人运动控制系统的输入、输出均是随时间连续变化的过程量. 结合S函数和预先规划思想, 建立水下机器人过程神经元的运动控制模型. 仿真试验证明,该新型控制模型, 对于水下机器人的运动非线性控制器具有设计简单、响应速度快、超调小、鲁棒性好等优点.     

A backstepping controller for path‐tracking of an underactuated autonomous airship

In this paper we propose a nonlinear control approach for the path‐tracking of an autonomous underactuated airship. A backstepping controller is designed from the airship nonlinear dynamic model including wind disturbances, and further enhanced to consider actuators saturation. Control implementation issues related to airship underactuation are also addressed, namely control allocation and an attitude reference shaping to obtain a faster error correction with smoother input requests. The results obtained demonstrate the capacity of an underactuated unmanned airship to execute a realistic mission including vertical take‐off and landing, stabilization and path‐tracking, in the presence of wind disturbances, with a single robust control law. Copyright © 2008 John Wiley & Sons, Ltd.     

Nonlinear vehicle side-slip estimation with friction adaptation

A nonlinear observer for estimation of the longitudinal velocity, lateral velocity, and yaw rate of a vehicle, designed for the purpose of vehicle side-slip estimation, is modified and extended in order to work for different road surface conditions. The observer relies on a road-tire friction model and is therefore sensitive to changes in the adhesion characteristics of the road surface. The friction model is parametrized with a single friction parameter, and an update law is designed. The adaptive observer is proven to be uniformly globally asymptotically stable and uniformly locally exponentially stable under a persistency-of-excitation condition and a set of technical assumptions, using results related to Matrosov's theorem. The observer is tested on recorded data from two test vehicles and shows good results on a range of road surfaces.     

可变速智能车辆车道变换控制研究

提出了一种能够兼顾横向控制和纵向控制的可变速自动换道控制方法。首先建立了可变速的车辆运动方程,并采用基于动态目标位置概念的控制机制,以模糊逻辑为控制策略,以T-S模糊模型为控制结构,以自适应神经网络为隶属度函数的参数调整手段,设计出一种兼顾智能车辆横向控制和纵向控制的运动控制器。仿真结果表明,提出的控制方法是可行的、有效的,并且较为理想地模拟实际交通环境中车辆换道的行为特性。     

基于柔性多状态开关和动态重构的配电网灵活运行方法

未来电动汽车(electric vehicle, EV)以及光伏(photovoltaic, PV)在配电网中的接入比例逐渐增高,这不仅带来了复杂的不确定性问题,而且导致了配电网净负荷时空分布的不均衡,从而产生了弃光、失负荷以及潮流分布不均匀的问题。基于此,考虑电动汽车充电负荷以及光伏的不确定性,提出了一种基于柔性多状态开关(flexible multi-state switch, FMS)和动态重构的含高比例电动汽车-光伏配电网灵活运行方法。首先,基于FMS灵活功率调控和网络动态重构,构建了支撑高比例电动汽车-光伏接入的配电网灵活运行框架。其次,利用蒙特卡洛随机模拟法对各类电动汽车充电负荷以及光伏出力进行不确定性建模,建立了基于场景集以及场景缩减法的随机规划模型。然后,建立了以弃光、失负荷、FMS损耗、网损成本最小和负荷均衡度最优为目标的配电网灵活运行模型。最后,在158节点系统上通过算例验证了所提方法的有效性。     

Adaptive fault-tolerant control for a class of flexible air-breathing hypersonic vehicles

In this paper, a novel adaptive fault-tolerant control (FTC) scheme is proposed for a class of flexible air-breathing hypersonic vehicles with unknown inertial and aerodynamic parameters and even input constraints. The fault model under consideration covers the case that all actuators suffer from unknown time-varying faults. In the controller design and stability analysis, we introduce new Lyapunov functions, some differentiable auxiliary functions, a bound estimation approach, and a Nussbaum function, which help us successfully circumvent the obstacle caused by the faults, input constraints, and flexible modes. In addition to higher reliability, the proposed scheme is able to ensure that all closed-loop signals are globally uniformly bounded and to steer the tracking errors of altitude and velocity into predefined arbitrarily small residual sets. As a result, the tracking accuracy can be designated in advance. Simulation results illustrate the effectiveness of the proposed scheme.     

纯电动汽车用内置式永磁同步电机弱磁控制策略综述

针对纯电动汽车对内置式永磁同步电机（IPMSM）要求有较宽的调速区间,结合内置式永磁同步电机的结构特点,并在内置式永磁同步电机的数学模型基础上,介绍了其弱磁扩速原理。综述了永磁同步电机（PMSM）弱磁控制策略的研究和发展现状,根据每个弱磁控制策略的特点进行分类和归纳,并进行分析。最后对纯电动汽车用永磁同步电机的弱磁控制算法进行展望。     

Sliding mode learning control for T-S fuzzy system and an application to hypersonic flight vehicle

Sliding mode-based learning control is presented for T-S fuzzy system. A T-S fuzzy model with both uncertainties and unmodeled dynamics is proposed firstly, in which the information of uncertainties and unmodeled dynamics are assumed to be unknown. Then, according to a given reference model, state-tracking error system is built. Respecting facts, the input matrices of the built T-S fuzzy model are different from each other. An extended state observer is built for estimating the unknown uncertainties and unmodeled dynamics, and a corresponding sliding surface is proposed. A learning controller is then presented for the closed loop system. Moreover, a numerical simulation result on hypersonic flight vehicles is considered to testify the controller's availability.     

Power supply to electric vehicle charging stations in India:Justification of a framework for a dynamic and adaptive electricity tariff policy

Given the steady growth in the use of electric vehicles in India, and the need to locate charging stations in various places to fulfill the corresponding demand, this paper focuses on an applicable Electricity Tariff Policy Framework in the context of power supply to Electric Vehicle Charging Stations(EVCSs). The framework is conceptualized by a theoretical foundation, an operational/functional foundation, and a transactional foundation. The theoretical foundation incorporates justice risk across the Energy Lifecycle stages by classifying the relevant international, regional, and national issues within the legal layers of Energy Law while examining the core ideas of the 'Energy Trilemma'. The paper identifies two basic dimensions, (a) the target sets of end-users, and (b) EVCSs settings and the applicable tariff schemes, as the foundation of the framework. The operational/functional foundation consists of techno-economic and social criteria, and the transactional foundation involves the consideration of different target sets of end-users. The proposed framework will benefit policymakers in general and those, especially in India. Adaptation and use of the framework in other countries should consider their basic economic, geographic and political structures.     

基于强化学习的电动汽车换电站实时调度策略优化

随着电动汽车的应用推广,换电站的调度优化逐渐成为研究热点。传统的基于换电需求预测值的调度策略在实际应用中面临着难以适应动态干扰因素、预测误差累积等问题。为了解决这些问题,提出了一种基于带基线的蒙特卡罗策略梯度法的换电站实时调度策略,用于优化换电站的充放电策略以及响应电池数量。提出了带基线的蒙特卡罗策略梯度强化学习,并为换电站实时调度问题选取合适的状态空间和动作空间;设计了奖励函数对智能体进行离线训练,从电池状态数据、分时电价和排队电动汽车数量中学习得到最优策略网络;在离线训练好的模型基础上进行实时调度策略测试。基于换电站的服务可用率和经济效益验证了所提调度策略的有效性和经济性,算例结果表明所提策略能对电网负荷起到一定的削峰填谷作用。