考虑多重不确定性的含需求响应及电碳交易的虚拟电厂优化调度策略

为解决当前对虚拟电厂系统中多重不确定性源的表征考虑不全面且处理方法单一的问题,提出一种基于蒙特卡罗与曼哈顿概率距离的场景生成与削减法来对风光出力、电价进行建模,并利用区间法对碳价、需求响应进行建模;以虚拟电厂运行成本最小化为目标,综合考虑电碳交易及激励型需求响应机制,建立基于区间线性规划的虚拟电厂优化调度模型,采用强区间线性规划法中的两阶段分解算法对模型进行求解,针对不同不确定性源的特点,采用2种方法处理虚拟电厂系统中的多重不确定性。算例结果表明,该模型能够通过指标变化趋势有效地反映多重不确定性因素对系统调度结果的影响,在保证虚拟电厂系统安全运行的基础上提高经济性,区间形式的表示为决策者留出了一定的现实选择空间。     

基于曲面二次标准型逼近的最近电压稳定临界点求取方法

针对切平面不能准确描述电压稳定极限曲面极限点邻域性质的缺陷,文中提出一种考虑负荷增长方向不确定性的基于曲面二次标准型逼近的两层规划模型,求解最近电压稳定临界点。该方法首先计算初始化负荷增长方向上的电压稳定临界点,并利用Weingarten映射获得该点处极限曲面的主曲率,进而获得极限曲面的二次标准型逼近,最后采用基于信赖域思想的二次规划法迭代搜索最近的电压稳定临界点。所提模型可同时考虑鞍节点分岔和极限诱导分岔两种临界点。对多个IEEE系统及中国东部某实际大区域电网等系统的分析证明了该方法的有效性。分析表明,所提方法具有收敛速度快、鲁棒性好,全局寻优能力强的优点,可快速确定电力系统的电压薄弱区域,并可直接用于大系统分析。     

多机器人的积分滑模编队控制

以一组非完整约束两轮机器人为研究对象,提出了具有非匹配不确定性的移动机器人系统的积分滑模编队控制。在单个机器人运动学模型的基础上,考虑机器人自身参数变化、打滑和侧移等不确定性的影响,采用领航-跟随机制,建立了编队系统的动力学模型。该动力学模型含有非匹配不确定性,无法应用积分滑模控制的不变性抑制。在合理的假设下,从理论上证明了具有非匹配不确定的编队系统在滑模阶段具有局部渐近稳定性;证明了提出的积分滑模编队控制律能够保证滑模的可达性条件。最后以三个机器人组成仿真实验平台,验证了在非匹配不确定性的了积分滑模编队控制方法的有效性及可行性。     

基于不确定理论的输电网规划

电力系统中存在着一系列不确定因素,而智能电网的发展给电力系统带来了更多且不确定程度更高的不确定因素。传统的规划方法不适应新形势下的输电网规划,为使规划方案能适应不确定因素带来的影响,不确定理论被引入输电网规划中,取得了一系列成果。分别从随机理论、模糊集理论、可信性理论等方面,对国内外不确定因素建模方法的研究成果和基于不确定理论的输电网规划方法的研究成果进行了综述。从规划方案不确定性评价指标的快速计算、不确定因素的处理等方面,展望了未来基于不确定理论的输电网规划方法研究中需要关注的问题。     

一种欠驱动船舶编队滑模鲁棒控制方法

为实现模型参数不确定和外界风浪流干扰情况下的欠驱动水面船舶编队控制,提出一种跟随者仅利用领航船的位置及航向信息,而不利用领航船的速度、角速度和动力学信息的编队控制方法.根据领航船的位置及航向角信息设计虚拟船,使其轨迹与参考船在有限时间内重合;基于虚拟船策略建立领导-跟随模式下的船舶编队控制模型;将反步法与滑模变结构方法相结合,设计了一种可以有效处理模型参数摄动和外界环境干扰问题的鲁棒控制器,使得跟随船沿着虚拟船的轨迹运动,从而与领航者保持期望的队形,达到编队控制的目的.通过对由3只“Northern Clipper”实船所组成的船舶编队进行编队控制仿真实验,结果验证了所提方法的正确性和有效性.     

Filtered adaptive constrained sampled-data control for uncertain multivariable nonlinear systems

A filtered adaptive constrained sampled-data controller for uncertain multivariable nonlinear systems in the presence of various constraints is synthesized in this paper. A piecewise constant adaptive law drives that estimation error dynamics to zero at each sampling time instant yields adaptive parameters. The filtered control scheme consists of two components. Based on an estimation/cancellation strategy, a disturbance rejection control law is designed to compensate the nonlinear uncertainties within the bandwidth of low-pass filters, whereas a constraint violation avoidance control law is designed to solve an online constrained optimization problem. Although a reduced sampling time helps to minimize the estimation error caused by the neglect of unknowns, the resulting aggressive signals put more restrictions on the control law. Greater sacrifice of tracking performance is required to satisfy the constraints. The constraints violation avoidance control law is in favor of a larger sampling time. Sufficient conditions are given to guarantee the stability of the closed-loop system with the sampled-data controller, where the input/output signals are held constant over the sampling period. Numerical examples are provided to validate the theoretical results, comparisons between the constrained sampled-data controller and unconstrained adaptive controller with the implementation of different sampling times are carried out.     

Adaptive robust control of a class of nonlinear systems in semi‐strict feedback form with non‐uniformly detectable unmeasured internal states

This paper proposes a novel control method for a special class of nonlinear systems in semi‐strict feedback form. The main characteristic of this class of systems is that the unmeasured internal states are non‐uniformly detectable, which means that no observer for these states can be designed to make the observation error exponentially converge to zero. In view of this, a projection‐based adaptive robust control law is developed in this paper for this kind of system. This method uses a projection‐type adaptation algorithm for the estimation of both the unknown parameters and the internal states. Robust feedback term is synthesized to make the system robust to uncertain nonlinearities and disturbances. Although the estimation error for both the unknown parameters and the internal states may not converge to zero, the tracking error of the closed‐loop system is proved to converge to zero asymptotically if the system has only parametric uncertainties. Furthermore, it is theoretically proved that all the signals are bounded, and the control algorithm is robust to bounded disturbances and uncertain nonlinearities with guaranteed output tracking transient performance and steady‐state accuracy in general. The class of system considered here has wide engineering applications, and a practical example—control of mechanical systems with dynamic friction—is used as a case study. Simulation results are obtained to demonstrate the applicability of the proposed control methodology. Copyright © 2010 John Wiley & Sons, Ltd.     

Adaptive backstepping repetitive learning control design for nonlinear discrete‐time systems with periodic uncertainties

This paper addresses a tracking problem for uncertain nonlinear discrete‐time systems in which the uncertainties, including parametric uncertainty and external disturbance, are periodic with known periodicity. Repetitive learning control (RLC) is an effective tool to deal with periodic unknown components. By using the backstepping procedures, an adaptive RLC law with periodic parameter estimation is designed. The overparameterization problem is overcome by postponing the parameter estimation to the last backstepping step, which could not be easily solved in robust adaptive control. It is shown that the proposed adaptive RLC law without overparameterization can guarantee the perfect tracking and boundedness of the states of the whole closed‐loop systems in presence of periodic uncertainties. In addition, the effectiveness of the developed controller is demonstrated by an implementation example on a single‐link flexible‐joint robot. Copyright © 2014 John Wiley & Sons, Ltd.     

最近电压稳定临界点的两层规划模型和信赖域算法

考虑了负荷变化的不确定性,提出负荷分布范围的棱锥状模型。建立了求解最近电压稳定临界点的非线性二层规划模型,提出一种求解该问题的信赖域方法。该方法先计算已知负荷增长方向上的电压稳定临界点,然后建立只有上、下界约束的近似模型,基于信赖域方法的思想迭代搜索最近电压稳定临界点,求取临界点时考虑了发电机无功出力的限制。算例分析表明,该算法收敛速度快、鲁棒性好,全局寻优能力强,并可找出系统电压稳定的薄弱区域和母线。     

不确定性环境下基于深度强化学习的综合能源系统动态调度

随着综合能源系统中间歇性能源和负荷不确定性的逐步增强,传统的调度方法局限于固定物理模型及参数设定,难以较好地动态响应源荷的随机波动。针对这一问题,提出了一种基于深度强化学习的综合能源系统动态调度方法。首先,以数据驱动方式构建面向综合能源系统的深度强化学习模型,通过智能体与综合能源系统的持续交互,自适应学习调度策略,降低对物理模型的依赖程度。其次,通过添加随机扰动的方式表征源荷不确定性变化特征,针对不确定性变化特征改进深度强化学习模型的状态空间、动作空间、奖励机制以及训练流程等关键环节,并经由近端策略优化算法优化求解,实现了综合能源系统的动态调度决策。最后,通过算例仿真验证了所提方法在不同时间尺度以及不确定性环境下的可行性和有效性。