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Optimal control Nodes Selection for Consensus in Multi-agent Systems

IFAC Proceedings Volumes 2014年第3期47卷 11697-11702页

作者： Wen Yang Xiaofan Wang Hongbo Shi Key Laboratory of Advanced Control and Optimization for Chemical Processes (East China University of Science and Technology) Ministry of Education Shanghai 200237 Department of Automation Shanghai Jiaotong University Shanghai 200240

In a resource limited multi-agent system, it is of practical importance to select a fraction of nodes (agents) to provide control inputs such that consensus can be achieved with optimized performance in terms of network cost and/or convergence speed. In this paper, we investigate the problem of how to select control nodes so as to minimize the network cost, where the control nodes are selected at the beginning and will be fixed all the time. This problem can be transformed to a combinatorial optimization problem, and further relaxed to a convex optimization problem with reweighted l 1 norm. We propose a suboptimal algorithm to solve the convex optimization problem. Finally, we offer several numerical examples to illustrate the efficiency of the proposed strategies, and investigate the relationship how the degrees of control nodes will influence network cost and convergence speed.

关键词： Node selection Convex optimization Multi-agent systems

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Multi-time Scale Hierarchical Predictive control for Energy Management of Microgrid System with Smart Users

Multi-time Scale Hierarchical Predictive Control for Energy ...

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第26届中国控制与决策会议

作者： Jun Xu Yuanyuan Zou Yugang Niu Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China Univ.of Science and Technology Key Laboratory of System Control and Information Processing Ministry of Education

In this paper, a multi-time scale hierarchal model predictive control strategy is proposed to optimize energy management problem of a microgrid with multiple smart users. According to the power flow among different energy modules, a hierarchical system model and a multi-time scale hierarchal energy optimization management problem are established. The centralized controller in the upper layer is to optimize the charge/discharge time and energy of storage devices, controllable supply power adjustment and dispatch of the aggregators. The optimization problem in the lower layer is to meet users' demands in real time. Meanwhile, in order to improve the disturbances caused by the randomness of renewable energy and variant loads, a multi-time scale optimization scheme is applied. At the slow scale, the upper optimization problem is solved, and the optimal energy scheduling in the long-term can be achieved. At the fast scale, the energy balance between supply and demand of smart users can be realized in the short-term. Finally, simulation results illustrate the effectiveness of proposed method.

关键词： Smart user,Microgrid,Energy management,Multi-time scale time-hierarchal,Model predictive control

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A new improved Alopex-based evolutionary algorithm and its application to parameter estimation

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Journal of Central South University 2013年第1期20卷 123-133页

作者：桑志祥李绍军董跃华 Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education (East China University of Science and Technology)

In this work, focusing on the demerit of AEA (Alopex-based evolutionary algorithm) algorithm, an improved AEA algorithm (AEA-C) which was fused AEA with clonal selection algorithm was proposed. Considering the irrationality of the method that generated candidate solutions at each iteration of AEA, clonal selection algorithm could be applied to improve the method. The performance of the proposed new algorithm was studied by using 22 benchmark functions and was compared with original AEA given the same conditions. The experimental results show that the AEA-C clearly outperforms the original AEA for almost all the 22 benchmark functions with 10, 30, 50 dimensions in success rates, solution quality and stability. Furthermore, AEA-C was applied to estimate 6 kinetics parameters of the fermentation dynamics models. The standard deviation of the objective function calculated by the AEA-C is 41.46 and is far less than that of other literatures' results, and the fitting curves obtained by AEA-C are more in line with the actual fermentation process curves.

关键词： Alopex evolutionary algorithm Alopex-based evolutionary algorithm clone selection parameter estimation

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Welding procedure expert system based on intelligent inference

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Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University 2014年 48卷 101-104页

作者： Wang, Xue-Wu Li, Yi-Xuan Yang, Shang-Yong Ding, Dong-Yan Gu, Xing-Sheng Key Laboratory of Advanced Control and Optimation for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai200237 China

Intelligent welding procedure expert system was developed to improve welding procedure selection for welding process in this paper. The expert system was designed based on Client/Server (C/S) mode, and the main functions include system management, knowledge base management, intelligent inference, remote access, etc. BP model and DEPSO based BP model were established to realize parameter estimation. Reasonable parameters can be derived based on the intelligent inference model and input welding condition. Obtained welding procedure can be presented as welding procedure card and multimedia display of welding process. Besides it, the output function makes the data share more convenient. The system is beneficial to welding parameters selection and welding engineer training. ©, 2014, Shanghai Jiao Tong University. All right reserved.

关键词： Neural networks

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Synchronization of heterogeneous dynamical networks via distributed impulsive control

Synchronization of heterogeneous dynamical networks via dist...

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Annual Conference of the IEEE Industrial Electronics Society

作者： Wangli He Qing-Long Han Feng Qian The Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Centre for Intelligent and Networked Systems Central Queensland University

ISBN: (纸本)9781479940318

This paper studies global synchronization between a heterogeneous dynamical network and a known target trajectory via distributed impulsive control. Synchronization with an error level, called quasi-synchronization, is analyzed by utilizing the time-varying Lyapunov function. Some sufficient quasi-synchronization conditions are presented and explicit expressions of error levels are derived. Furthermore, the effects of the pinning control matrix and the coupling strength are explored. Unlike the continuous pinning feedback control, it is shown that a large coupling strength will destroy synchronization in the present of impulsive controller, which is also verified by our simulations.

关键词： Lyapunov methods distributed control interconnected systems matrix algebra time-varying systems trajectory control coupling strength distributed impulsive control global synchronization heterogeneous dynamical network pinning control matrix quasisynchronization target trajectory time-varying Lyapunov function Couplings Educational institutions Eigenvalues and eigenfunctions Symmetric matrices Synchronization Trajectory

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Adaptive neural control for pure-feedback nonlinear time-delay systems with unknown dead-zone: a Lyapunov-Razumikhin method

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控制理论与应用（英文版） 2013年第1期11卷 18-26页

作者： Zhaoxu YU Jianxu LUO Ji LIU Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

This paper addresses the problem of adaptive neural control for a class of uncertain pure-feedback nonlinear systems with multiple unknown state time-varying delays and unknown dead-zone. Based on a novel combination of the Razumikhin functional method, the backstepping technique and the neural network parameterization, an adaptive neural control scheme is developed for such systems. All closed-loop signals are shown to be semiglobally uniformly ultimately bounded, and the tracking error remains in a small neighborhood of the origin. Finally, a simulation example is given to demonstrate the effectiveness of the proposed control schemes.

关键词： Pure-feedback nonlinear systems Adaptive neural control Razumikhin functional Time-delay Deadzone

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chemical process dynamic optimization based on hybrid differential evolution algorithm integrated with Alopex

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Journal of Central South University 2013年第4期20卷 950-959页

作者：范勤勤吕照民颜学峰郭美锦 Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education (East China University of Science and Technology)

To solve dynamic optimization problem of chemical process (CPDOP), a hybrid differential evolution algorithm, which is integrated with Alopex and named as Alopex-DE, was proposed. In Alopex-DE, each original individual has its own symbiotic individual, which consists of control parameters. Differential evolution operator is applied for the original individuals to search the global optimization solution. Alopex algorithm is used to co-evolve the symbiotic individuals during the original individual evolution and enhance the fitness of the original individuals. Thus, control parameters are self-adaptively adjusted by Alopex to obtain the real-time optimum values for the original population. To illustrate the whole performance of Alopex-DE, several varietal DEs were applied to optimize 13 benchmark functions. The results show that the whole performance of Alopex-DE is the best. Further, Alopex-DE was applied to solve 4 typical CPDOPs, and the effect of the discrete time degree on the optimization solution was analyzed. The satisfactory result is obtained.

关键词： evolutionary computation dynamic optimization differential evolution algorithm Alopex algorithm self-adaptivity

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Unbiased Minimum-Variance Filtering for Systems with Randomly Multi-Step Sensor Delays

Unbiased Minimum-Variance Filtering for Systems with Randoml...

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Annual Conference of the IEEE Industrial Electronics Society

作者： Yilian Zhang Fuwen Yang Qing-Long Han Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Centre for Intelligent and Networked Systems Central Queensland University

ISBN: (纸本)9781479940318

In this paper, a linear unbiased minimum-variance filtering problem is considered for a class of systems with randomly multi-step sensor delays. A new mathematical model is established for the multi-step sensor delays. Different from the augmented method for dealing with delayed systems, a linear unbiased minimum-variance filter design method is proposed without augmenting the state vector, which effectively reduces the filter dimensions. A recursive algorithm for calculating the filter gain matrix is developed. The simulation results illustrate the effectiveness of the proposed method.

关键词： Networked control systems Filtering Recursive estimation Kalman filters Delay systems Stochastic processes Sensor system Communication networks

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H∞ controller design of event-triggered networked control systems

H∞ controller design of event-triggered networked control s...

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International Conference on Mechatronics and control (ICMC)

作者： Sheng Yan Huaicheng Yan Hongbo Shi Hao Zhang Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai China Department of Control Theory and and Engineering Tongji University Shanghai China

ISBN: (纸本)9781479925391

This paper deals with H ∞ controller design problem for event-triggered networked control systems (NCSs), where the next task release time and finishing time are predicted based on the sampled states. A new model of NCSs that involves the network conditions, state and event-triggered communication strategy is proposed. Based on this model, some novel criteria for the asymptotic stability analysis and H ∞ state feedback controller design of the event-triggered NCSs with time-varying delay are established to guarantee a prescribed H ∞ disturbance rejection attenuation level. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method.

关键词： Delays State feedback Networked control systems Actuators Stability analysis control design

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Adaptive strategy for time window length in SSVEP-based brain-computer interface

Adaptive strategy for time window length in SSVEP-based brai...

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International Conference on Mechatronics and control (ICMC)

作者： Yu Zhang Hehe Ma Jing Jin Xingyu Wang Key Laboratory for Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai China Shanghai Electric Group Co Ltd Shanghai Shanghai CN

ISBN: (纸本)9781479925391

Most of the existed methods for steady-state visual evoked potential (SSVEP) recognition require a specified time window length (TWL) to estimate the dominant frequency components in EEG signals. Typically, the TWL is manually predetermined and then fixed during running of the SSVEP-based brain-computer interface (BCI), which may not give the optimal information transfer rate (ITR). This study proposes an adaptive strategy and integrates it into multivariate synchronization index for SSVEP recognition with the automatically determined TWL. The optimal TWL is adaptively selected according to the estimated synchronization index, which provides the relatively higher ITR for the SSVEP recognition than a fixed TWL does. Experimental results evaluated on nine healthy subjects demonstrated effectiveness of the proposed adaptive strategy for the TWL in SSVEP-based BCI.

关键词： Electroencephalography Synchronization Accuracy Brain-computer interfaces Indexes Correlation Frequency synchronization

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