离散区间二型模糊双线性时滞系统的稳定控制

本文研究了带有常时滞的离散时间二型模糊双线性系统的状态反馈控制问题.首先,考虑隶属函数的不确定性,将非线性系统建模为区间二型模糊双线性系统.其次,基于Lyapunov稳定性理论,提出了区间二型模糊状态反馈控制准则,以保证区间二型模糊双线性闭环系统的稳定性.控制器的设计可以通过序列线性规划矩阵方法求解得到.最后,通过数值...     

不确定离散模糊系统的鲁棒H2／H∞静态输出反馈控制

研究一类不确定离散模糊系统的鲁棒H2/H∞静态输出反馈控制问题.提出一种Lyapunov矩阵与系统矩阵解耦的离散模糊系统混合H2/H∞性能准则;基于该性能准则,以矩阵不等式的形式,给出了鲁棒H2/H∞模糊静态输出反馈控制器存在的充分条件,并给出了最优化模糊控制器设计的迭代线性矩阵不等式(ILMI)算法.     

基于熵权法和多层次分析法的水环境模糊综合评价研究

结合我国水环境的现状和特点构建了水环境承载能力指标评价体系,运用熵权法确定各评价指标的客观权重,将其与多层次分析法的主观权重进行合成,根据该权值对水环境进行模糊综合评价,评价结果表明,模型比单一的模糊综合评价法能够符合水环境的实际状况,从而为管理决策提供科学有效的依据.在综合评价的基础上,提出了改善水环境承载能力的建议.     

基于non-PDC算法的T-S模糊时滞系统的控制

针对一类T-S模糊模型描述的非线性时滞系统,提出了基于非并行分布补偿(non-PDC)算法的模糊状态反馈控制方法。为了减少保守性,选择一个新的二重积分不等式,用以处理求导LyapunovKrasovskii泛函所得的积分项。不同于并行分布补偿(PDC)算法,本文给出的基于non-PDC算法的模糊状态反馈控制器不要求与模糊模型分享共同的隶属函数或者相同的模糊规则数目,因此具有更大的设计灵活性。为了进一步减少non-PDC算法带来的保守性,考虑隶属函数的边界信息,同时引入适当的松弛变量。最后给出卡车拖车模型的仿真实例,证明了所提方法的有效性。     

模糊离散事件系统的状态反馈控制

在模糊离散事件系统中,首先定义了模糊谓词,给出了模糊谓词变换以及模糊谓词的控制不变和Σ_u-不变的定义,并证明了模糊谓词的控制不变和Σ_u-不变的等价性;其次,给出了模糊可控谓词的定义,并证明了对于预先给定的模糊谓词,存在一个状态反馈控制器使得闭环系统的可达模糊谓词等于该给定模糊谓词的充要条件是该模糊谓词是可控的。     

基于观测器的模糊时滞系统的指数稳定控制

研究一类模糊时滞系统的指数稳定和基于观测器的模糊控制问题.在系统状态未知的情况下,通过设计系统的模糊观测器利用矩阵不等式分析的方法给出了系统指数稳定条件和基于观测器的动态输出反馈控制器设计方案.仿真结果说明了所提方法的有效性.     

模糊输入时滞系统的输出反馈控制及稳定性分析

针对一类状态不可测的模糊输入时滞系统,应用平行分布补偿算法(PDC),设计了模糊观测器,提出了基于模糊观测器的输出反馈控制方法,给出了保证模糊时滞系统渐近稳定的新的充分条件.应用广义Lyapunov函数和线性矩阵不等式方法,证明了模糊输入时滞系统的渐近稳定性,同时给出了控制和观测增益矩阵的分离设计算法.仿真结果进一步验证了所提出的方法和条件的有效性.     

邯郸市水资源承载力评价与预测研究

选取水资源评价指标体系,运用复合模糊评价模型,对201.2年邯郸市水资源承载力(WRCC)进行了评价,得出WRCC为0.4145,表明邯郸市处于可承载的中游水平.接着通过灰色预测方法对邯郸市2020年的主要相关指标进行预测,再次利用复合模糊评价模型对水资源承载力进行综合评价,得出WRCC为0.5495,表明邯郸市的水资源还处于可承载的范畴,而且比2012年的水资源承载能力有所提高,分析结果为邯郸市水资源的合理利用及可持续发展提供一定理论依据.     

超音速飞行器机翼颤振的时滞反馈控制

采用时滞反馈主动控制方法对超音速飞行器机翼颤振进行控制,以提高飞行器机翼系统的颤振临界速度.首先根据二元机翼的力学模型,制定时滞反馈控制策略并建立时滞反馈控制系统的数学模型;分别对无控、零时滞反馈控制和有时滞反馈控制系统进行稳定性分析,获得时滞反馈控制系统的颤振稳定性边界.利用MATLAB/SIMULINK进行时域数值模拟,验证理论稳定性分析结果的正确性.结果表明:通过调节时滞量,可有效提高飞行器机翼的颤振临界速度,且控制策略简单,效果较好.     

带有界扰动的一类大型互联非线性系统的鲁棒分散输出反馈设计

本文对带有界扰动的一类大型互联非线性系统进行了分散输出反馈控制设计,通过子系统状态的线性变换,得到分散输出反馈控制律.当输出反馈控制律作用于该系统时,系统的状态能够收敛到原点的一个小邻域内.     

An adaptive robust controller for time delay maglev transportation systems

For engineering systems, uncertainties and time delays are two important issues that must be considered in control design. Uncertainties are often encountered in various dynamical systems due to modeling errors, measurement noises, linearization and approximations. Time delays have always been among the most difficult problems encountered in process control. In practical applications of feedback control, time delay arises frequently and can severely degrade closed-loop system performance and in some cases, drives the system to instability. Therefore, stability analysis and controller synthesis for uncertain nonlinear time-delay systems are important both in theory and in practice and many analytical techniques have been developed using delay-dependent Lyapunov function. In the past decade the magnetic and levitation (maglev) transportation system as a new system with high functionality has been the focus of numerous studies. However, maglev transportation systems are highly nonlinear and thus designing controller for those are challenging. The main topic of this paper is to design an adaptive robust controller for maglev transportation systems with time-delay, parametric uncertainties and external disturbances. In this paper, an adaptive robust control (ARC) is designed for this purpose. It should be noted that the adaptive gain is derived from Lyapunov–Krasovskii synthesis method, therefore asymptotic stability is guaranteed.     

Superiority–inferiority modeling coupled minimax-regret analysis for energy management systems

In this study, a superiority–inferiority-based minimax-regret analysis (SI-MRA) model is developed for supporting the energy management systems (EMS) planning under uncertainty. In SI-MRA model, techniques of fuzzy mathematical programming (FMP) with the superiority and inferiority measures and minimax regret analysis (MMR) are incorporated within a general framework. The SI-MRA improves upon conventional FMP methods by directly reflecting the relationships among fuzzy coefficients in both the objective function and constraints with a high computational efficiency. It can not only address uncertainties expressed as fuzzy sets in both of the objective function and system constraints but also can adopt a list of scenarios to reflect the uncertainties of random variables without making assumptions on their possibilistic distributions. The developed SI-MRA model is applied to a case study of long-term EMS planning, where fuzziness and randomness exist in the costs for electricity generation and demand. A number of scenarios associated with various alternatives and outcomes under different electricity demand levels are examined. The results can help decision makers identify an optimal strategy of planning electricity generation and capacity expansion based on a minimax regret level under uncertainty.     

State feedback for uncertain dynamical systems

We consider a linear dynamical system in which the system and input matrices, as well as the input, are uncertain. We present a control system consisting of a linear control to stabilize the nominal system, a nonlinear control to cope with the uncertainties, and an insensitive observer for the state estimation. Practical stability is guaranteed for uncertainties with known bounds. Furthermore, the control system is designed to achieve insensitivity against parameter variations. The theoretical results are illustrated by an application to the suspension control of a maglev vehicle.     

高速公路交通事件影响范围的模糊预测

高速公路交通事件的影响范围预测主要包括两个方面:排队长度预测和延误预测.在对排队长度和延误进行实时预测时,需精确预测出事件发生期间的交通流量和通行能力、事件持续时间及车辆到达事件点的时间等参数,而事实上,用于确定这些参数的信息是非常复杂的,得到的这些参数值是模糊的,但是其变化范围是可以精确确定的.充分考虑上述参数的模糊特性,及事件发生期间的交通流量和通行能力的模糊关系,运用α截集表示了车辆到达离去曲线,建立了交通事件排队长度模糊预测模型和延误模糊预测模型.利用2001年6月16日发生在美国210-E高速公路的交通事件验证了该模型的可行性,并分析了事件发生期间交通流量和通行能力及事件持续时间的模糊度变化对排队长度和延误预测结果的影响.     

A car-following collision prevention control device based on the cascaded fuzzy inference system

A car-following collision prevention control device based on the cascaded fuzzy inference system (CFIS), consisting of a velocity fuzzy controller and an acceleration fuzzy controller, to nonlinearly control car acceleration or deceleration rate is proposed. The distance and speed relative to the car in front are measured using spread spectrum radar and applied to the collision prevention control device. The output acceleration or deceleration rate obtained from the CFIS car-following collision prevention system is based on the characteristics of the vehicle. The simulation results demonstrate that the presented CFIS control device can solve the oscillation problems for final relative distance between the lead vehicle (LV) and following vehicle (FV) and relative speed. When the LV applies the brake suddenly or a stationary obstacle appears in front of vehicle moving at high speed on the roadway, the CFIS control device can safely avoid a collision. The CFIS car-following collision prevention control device proposed in this paper can provide a safe, reasonable and comfortable drive.     

基于T-S模型的Lurie混沌系统的模糊控制

针对Lurie混沌控制系统,进行了T-S模糊建模和模糊控制器设计,从而实现了Lurie混沌系统的稳定.在用T-S模糊模型精确重构Lurie系统结构的基础上,利用反馈同步思想,基于并行分布补偿(PDC)技术,得到了简单且易实现的控制器.仿真结果验证了该控制方法的有效性.     

Adaptive fuzzy output feedback control for a single-link flexible robot manipulator driven DC motor via backstepping

In this paper, an adaptive fuzzy output feedback approach is proposed for a single-link robotic manipulator coupled to a brushed direct current (DC) motor with a nonrigid joint. The controller is designed to compensate for the nonlinear dynamics associated with the mechanical subsystem and the electrical subsystems while only requiring the measurements of link position. Using fuzzy logic systems to approximate the unknown nonlinearities, an adaptive fuzzy filter observer is designed to estimate the immeasurable states. By combining the adaptive backstepping and dynamic surface control (DSC) techniques, an adaptive fuzzy output feedback control approach is developed. Stability proof of the overall closed-loop system is given via the Lyapunov direct method. Three key advantages of our scheme are as follows: (i) the proposed adaptive fuzzy control approach does not require that all the states of the system be measured directly, (ii) the proposed control approach can solve the control problem of robotic manipulators with unknown nonlinear uncertainties, and (iii) the problem of “explosion of complexity” existing in the conventional backstepping control methods is avoided. The detailed simulation results are provided to demonstrate the effectiveness of the proposed controller.     

Robust Output Tracking Control of an Uncertain Linear System via a Modified Optimal Linear-Quadratic Method

This study investigates the robust output tracking problem for a class of uncertain linear systems. The uncertainties are assumed to be time invariant and to satisfy the matching conditions. According to the selected nominal parameters, an optimal solution with a prescribed degree of stability is determined. Then, an auxiliary input via the use of an adapting factor, connected to the nominal optimal control, is introduced to guarantee the robustness and prescribed degree of stability for the output tracking control of the uncertain linear systems. This method is very simple and effective and can reject bounded uncertainties imposed on the states. A maglev vehicle model example is given to show its effectiveness.     

Using greedy clustering method to solve capacitated location-routing problem with fuzzy demands

In this paper, the capacitated location-routing problem with fuzzy demands (CLRP-FD) is considered. In CLRP-FD, facility location problem (FLP) and vehicle routing problem (VRP) are observed simultaneously. Indeed, the vehicles and the depots have a predefined capacity to serve the customers that have fuzzy demands. To model this problem, a fuzzy chance constrained programming model of that is designed based upon the fuzzy credibility theory. To solve this problem, a greedy clustering method (GCM) including the stochastic simulation is proposed. To obtain the best value of the dispatcher preference index of the model and to analyze its influence on the final solution, numerical experiments are carried out. Finally, to show the performance of the greedy clustering method, associated results are compared with the lower bound of the solutions.     

Tracking control of chaotic spinning disks via nonlinear dynamic output feedback with input constraints

In many control engineering applications, it is impossible or expensive to measure all the states of the dynamical system and only the system output is available for controller design. In this study, a new dynamic output feedback control algorithm is proposed to stabilize the unstable periodic orbit of chaotic spinning disks with incomplete state information. The proposed control structure is based on the T‐S fuzzy systems. This investigation also introduces a new design procedure to satisfy a constraint on the T‐S fuzzy dynamic output feedback control signal. This procedure is independent of the exact value of initial states. Finally, computer simulations are accomplished to illustrate the performance of the proposed control algorithm. © 2015 Wiley Periodicals, Inc. Complexity 21: 148–159, 2016