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Eigenvector Method for the Hamiltonian Approach 哈密顿方法的特征向量法
IF 1.8 4区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS Pub Date : 2018-10-03 DOI: 10.1201/B15929-10
Z. Gajic, M. Lim, D. Skataric, W. Su, V. Kecman
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引用次数: 0
Hamiltonian Method for Steady State Optimal Control and Filtering 稳态最优控制与滤波的哈密顿方法
IF 1.8 4区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS Pub Date : 2018-10-03 DOI: 10.1201/b15929-12
Z. Gajic, M. Lim, D. Skataric, W. Su, V. Kecman
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引用次数: 0
Quasi-Weakly Coupled Linear Control Systems 准弱耦合线性控制系统
IF 1.8 4区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS Pub Date : 2018-10-03 DOI: 10.1201/B15929-3
Z. Gajic, M. Lim, D. Skataric, W. Su, V. Kecman
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引用次数: 0
Decoupling Transformation, Lyapunov Equation, and Boundary Value Problem 解耦变换,李雅普诺夫方程,边值问题
IF 1.8 4区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS Pub Date : 2018-10-03 DOI: 10.1201/B15929-5
Z. Gajic, M. Lim, D. Skataric, W. Su, V. Kecman
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引用次数: 0
Finite Time Optimal Control via Hamiltonian Method 基于hamilton方法的有限时间最优控制
IF 1.8 4区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS Pub Date : 2018-10-03 DOI: 10.1201/B15929-8
Z. Gajic, M. Lim, D. Skataric, W. Su, V. Kecman
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引用次数: 0
Nash Differential Games 纳什微分对策
IF 1.8 4区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS Pub Date : 2018-10-03 DOI: 10.1201/b15929-9
Z. Gajic, M. Lim, D. Skataric, W. Su, V. Kecman
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引用次数: 0
Stochastic Linear Weakly Coupled Systems 随机线性弱耦合系统
IF 1.8 4区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS Pub Date : 2018-10-03 DOI: 10.1201/B15929-6
Z. Gajic, M. Lim, D. Skataric, W. Su, V. Kecman
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引用次数: 0
L2‐L∞ filtering for a class of stochastic nonlinear systems with aperiodic sampling 一类非周期采样随机非线性系统的L2‐L∞滤波
IF 1.8 4区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS Pub Date : 2018-01-01 DOI: 10.1002/oca.2401
B. Zhu, M. Suo, Zhiping Zhang, Min Li, Shunli Li
This work investigates the problem of L2‐L∞ filtering for a class of stochastic nonlinear systems with nonuniform sampling. The sampled‐data filter developed in this paper is an impulsive differential system whose states change abruptly at every sampling instant. The resulting filtering error system is modeled as a stochastic nonlinear impulsive differential system. The goal is to propose a method for designing a target filter that ensures the stochastic asymptotic stability of the filtering error system and guarantees a prescribed L2‐L∞ performance. Based on a time‐varying Lyapunov functional, by virtue of a convex combination technique, a design method to achieve such a filter is formulated in the form of solving a set of linear matrix inequalities. The effectiveness of the proposed filtering strategy is shown via a numerical example of a stochastic Chua's circuit system.
本文研究了一类非均匀采样随机非线性系统的L2‐L∞滤波问题。本文所开发的采样数据滤波器是一个脉冲微分系统,其状态在每个采样时刻都发生突变。所得到的滤波误差系统被建模为随机非线性脉冲微分系统。目的是提出一种设计目标滤波器的方法,以确保滤波误差系统的随机渐近稳定性并保证规定的L2‐L∞性能。基于时变Lyapunov泛函,利用凸组合技术,以求解一组线性矩阵不等式的形式阐述了实现这种滤波器的设计方法。通过一个随机蔡氏电路系统的数值算例证明了所提滤波策略的有效性。
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引用次数: 3
Switched optimization control of power allocation of hybrid energy storage systems 混合储能系统功率分配的切换优化控制
IF 1.8 4区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS Pub Date : 2018-01-01 DOI: 10.1002/oca.2336
Xiulan Song, Limin Meng, Lei Wang
In this paper, we present a switched optimization control method for power allocation of hybrid energy storage systems (HESSs) subject to constraints on the state of charge and power split. By the energy conservation principle, a continuous‐time switching model is established to describe changes of charge quantities of the HESS during its charging‐or‐discharging process. Then an analytic switched state feedback law with some free parameters is constructed by the concept of common control Lyapunov functions, which is used to allocate the power of storage units during the charging‐or‐discharging process. To cope with the constraints and performance functions formulating the power allocation requirements of storage units, the receding horizon control principle is used to compute the parameters of the analytic switched control law by online solving a constrained optimization problem. The results on asymptotical stability and common section region (0.5, ∞) of the switched optimization controller are established in the presence of constraints by using the properties of common control Lyapunov functions. By comparing to linear‐quadratic regulator control of the HESS, an example is used to illustrate the effectiveness and performance of the switched optimization controller presented here.
本文提出了一种受充电状态和功率分配约束的混合储能系统功率分配的切换优化控制方法。根据能量守恒原理,建立了连续时间开关模型来描述HESS在充放电过程中电荷量的变化。然后,利用公共控制李雅普诺夫函数的概念,构造了具有一定自由参数的解析开关状态反馈律,用于在充放电过程中分配存储单元的功率。针对存储单元功率分配要求的约束条件和性能函数,采用后退水平控制原理,通过在线求解约束优化问题,计算解析切换控制律的参数。利用公共控制Lyapunov函数的性质,在存在约束的情况下,建立了切换优化控制器的渐近稳定性和公共截面区域(0.5,∞)。通过与HESS的线性二次型调节器控制的比较,实例说明了所提出的切换优化控制器的有效性和性能。
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引用次数: 0
A decentralized receding horizon optimal approach to formation control of networked mobile robots 网络化移动机器人编队控制的分散后退地平线最优方法
IF 1.8 4区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS Pub Date : 2017-01-01 DOI: 10.1002/oca.2333
Mohammad Hosseinzadeh Yamchi, R. Mahboobi Esfanjani
This paper presents a receding horizon optimal controller with guaranteed stability for multirobot formation, taking into account collision and obstacle avoidance. The proposed scheme is based on synchronous decentralized strategy wherein all the vehicles that are connected via a packed‐delaying network solve a finite horizon–constrained optimal control problem to obtain their own control action at each sampling instant. First, each robot is modeled by a single integrator dynamics; then, by defining a control law for each robot and considering the effect of communication delay, the closed‐loop dynamics is described as a delay differential equation with tunable parameters. Afterwards, a novel finite‐horizon optimal control setup is established to obtain these adjustable gains such that a desirable formation is achieved. The efficiency and applicability of the suggested scheme are demonstrated by simulation results.
针对多机器人编队问题,提出了一种考虑碰撞和避障的保证稳定的后退地平线最优控制器。该方案基于同步分散策略,其中所有通过打包延迟网络连接的车辆在每个采样时刻求解有限水平约束最优控制问题,以获得各自的控制动作。首先,每个机器人由单个积分器动力学建模;然后,通过定义每个机器人的控制律,并考虑通信延迟的影响,将闭环动力学描述为具有可调参数的延迟微分方程。然后,建立了一种新的有限水平最优控制设置,以获得这些可调增益,从而实现理想的地层。仿真结果验证了该方案的有效性和适用性。
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引用次数: 0
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Optimal Control Applications & Methods
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