17 results on '"ROBUST control"'
Search Results
2. Robust Consensus for Nonlinear Multiagent Systems with Uncertainty and Disturbance.
- Author
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Li, Zhiqiang, Xu, Chengjie, Liu, Chen, and Xu, Haichuan
- Subjects
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ROBUST control , *MULTIAGENT systems , *LYAPUNOV stability , *MATRICES (Mathematics) , *MATHEMATICAL optimization - Abstract
This paper investigates robust consensus for nonlinear multiagent systems with uncertainty and disturbance. The consensus evolution behavior is studied under general consensus protocol when each node is disturbed by the relative states between the node and its neighbors. At first, the robust consensus condition is obtained and the convergency analysis is given by using Lyapunov stability theory and matrix theory. Then, the practical consensus is investigated and the bound of the error states is presented. Finally, two numerical simulation examples are given to illustrate the proposed theoretical results. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
3. Robust Model of Discrete Competitive Facility Location Problem with Partially Proportional Rule
- Author
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Wuyang Yu
- Subjects
Scheme (programming language) ,Mathematical optimization ,021103 operations research ,Article Subject ,Computer science ,General Mathematics ,0211 other engineering and technologies ,General Engineering ,Sorting ,Robust optimization ,02 engineering and technology ,Engineering (General). Civil engineering (General) ,Facility location problem ,Simulated annealing ,QA1-939 ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,TA1-2040 ,Robust control ,Market share ,computer ,Mathematics ,computer.programming_language - Abstract
When consumers faced with the choice of competitive chain facilities that offer exclusive services, current rules cannot describe these customers’ behaviors very well. So we propose a partially proportional rule to represent this kind of customer behavior. In addition, the exact demands of customers in many real-world environments are often difficult to determine. This is contradicting to the assumption in most studies of the competitive facility location problem. For the competitive facility location problem with the partially proportional rule, we establish a robust optimization model to handle the uncertainty of customers’ demands. We propose two methods to solve the robust model by studying the properties of the counterpart problem. The first method MIP is presented by solving a mixed-integer optimization model of the counterpart problem directly. The second method SAS is given by embedding a sorting subalgorithm into the simulated annealing framework, in which the sorting subalgorithm can easily solve the subproblem. The effects of the budget and the robust control parameter to the location scheme are analyzed in a quasi-real example. The result shows that changes in the robust control parameter can affect the customer demands that were captured by the new entrants, thereby changing the optimal solution for facility location. In addition, there is a threshold of the robust control parameter for any given budget. Only when the robust control parameter is larger than this threshold, the market share captured by the new entering firm increases with the increases of this parameter. Finally, numerical experiments show the superiority of the algorithm SAS in large-scare competitive facility location problems.
- Published
- 2020
4. The Application of Time-Delay Dependent H∞ Control Model in Manufacturing Decision Optimization.
- Author
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Guo, Haifeng, Wang, Bo, Zhang, Jianhua, Chen, Song, and Qiu, Yi
- Subjects
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TIME delay systems , *MATHEMATICAL optimization , *PRODUCTION management (Manufacturing) , *ROBUST control , *MATHEMATICAL proofs , *MATHEMATICAL models - Abstract
This paper uses a time-delay dependent H∞ control model to analyze the effect of manufacturing decisions on the process of transmission from resources to capability. We establish a theoretical framework of manufacturing management process based on three terms: resource, manufacturing decision, and capability. Then we build a time-delay H∞ robust control model to analyze the robustness of manufacturing management. With the state feedback controller between manufacturing resources and decision, we find that there is an optimal decision to adjust the process of transmission from resources to capability under uncertain environment. Finally, we provide an example to prove the robustness of this model. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
5. Method for Determining the Maximum Allowable Capacity of Wind Farm Based on Box Set Robust Optimization.
- Author
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Guo, Lihui and Bai, Hao
- Subjects
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WIND power plants , *ROBUST control , *SET theory , *MATHEMATICAL optimization , *FLUCTUATIONS (Physics) , *DUALITY theory (Mathematics) - Abstract
With the increasing penetration of wind power, the randomness and volatility of wind power output would have a greater impact on safety and steady operation of power system. In allusion to the uncertainty of wind speed and load demand, this paper applied box set robust optimization theory in determining the maximum allowable installed capacity of wind farm, while constraints of node voltage and line capacity are considered. Optimized duality theory is used to simplify the model and convert uncertainty quantities in constraints into certainty quantities. Under the condition of multi wind farms, a bilevel optimization model to calculate penetration capacity is proposed. The result of IEEE 30-bus system shows that the robust optimization model proposed in the paper is correct and effective and indicates that the fluctuation range of wind speed and load and the importance degree of grid connection point of wind farm and load point have impact on the allowable capacity of wind farm. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
6. A Multiobjective Genetic Algorithm Based on a Discrete Selection Procedure.
- Author
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Long, Qiang, Wu, Changzhi, Wang, Xiangyu, Jiang, Lin, and Li, Jueyou
- Subjects
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GENETIC algorithms , *MATHEMATICAL optimization , *PARETO analysis , *ROBUST control , *SEARCH algorithms - Abstract
Multiobjective genetic algorithm (MOGA) is a direct search method for multiobjective optimization problems. It is based on the process of the genetic algorithm; the population-based property of the genetic algorithm is well applied in MOGAs. Comparing with the traditional multiobjective algorithm whose aim is to find a single Pareto solution, the MOGA intends to identify numbers of Pareto solutions. During the process of solving multiobjective optimization problems using genetic algorithm, one needs to consider the elitism and diversity of solutions. But, normally, there are some trade-offs between the elitism and diversity. For some multiobjective problems, elitism and diversity are conflicting with each other. Therefore, solutions obtained by applying MOGAs have to be balanced with respect to elitism and diversity. In this paper, we propose metrics to numerically measure the elitism and diversity of solutions, and the optimum order method is applied to identify these solutions with better elitism and diversity metrics. We test the proposed method by some well-known benchmarks and compare its numerical performance with other MOGAs; the result shows that the proposed method is efficient and robust. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
7. Robust Fault Detection for a Class of Uncertain Nonlinear Systems Based on Multiobjective Optimization.
- Author
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Yan, Bingyong, Wang, Huifeng, and Wang, Huazhong
- Subjects
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ROBUST control , *SET theory , *NONLINEAR systems , *MATHEMATICAL optimization , *ALGORITHMS , *MATRICES (Mathematics) - Abstract
A robust fault detection scheme for a class of nonlinear systems with uncertainty is proposed. The proposed approach utilizes robust control theory and parameter optimization algorithm to design the gain matrix of fault tracking approximator (FTA) for fault detection. The gain matrix of FTA is designed to minimize the effects of system uncertainty on residual signals while maximizing the effects of system faults on residual signals. The design of the gain matrix of FTA takes into account the robustness of residual signals to system uncertainty and sensitivity of residual signals to system faults simultaneously, which leads to a multiobjective optimization problem. Then, the detectability of system faults is rigorously analyzed by investigating the threshold of residual signals. Finally, simulation results are provided to show the validity and applicability of the proposed approach. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
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8. Short-Term Wind Speed Hybrid Forecasting Model Based on Bias Correcting Study and Its Application.
- Author
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Niu, Mingfei, Sun, Shaolong, Wu, Jie, and Zhang, Yuanlei
- Subjects
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WIND speed measurement , *BIAS correction (Topology) , *WIND power , *MATHEMATICAL optimization , *ROBUST control - Abstract
The accuracy of wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. In particular, reliable short-term wind speed forecasting can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, due to the strong stochastic nature and dynamic uncertainty of wind speed, the forecasting of wind speed data using different patterns is difficult. This paper proposes a novel combination bias correcting forecasting method, which includes the combination forecasting method and forecasting bias correcting model. The forecasting result shows that the combination bias correcting forecasting method can more accurately forecast the trend of wind speed and has a good robustness. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
9. Finding Robust Assailant Using Optimization Functions (FiRAO-PG) in Wireless Sensor Network.
- Author
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Shukla, Piyush Kumar, Goyal, Sachin, Wadhvani, Rajesh, Rizvi, M. A., Sharma, Poonam, and Tantubay, Neeraj
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ROBUST control , *MATHEMATICAL optimization , *MATHEMATICAL functions , *WIRELESS sensor networks , *DATA transmission systems , *COMPUTER algorithms - Abstract
Wireless sensor network consists of hundreds or thousands of low cost, low power, and self-organizing tiny sensor nodes that are deployed within the sensor network. Sensor network is susceptible to physical attacks due to deprived power and restricted resource capability and is exposed to external environment for transmitting and receiving data. Node capture attack is one of the most menacing attack in the wireless sensor network and may be physically captured by an adversary for extracting confidential information regarding cryptographic keys, node’s unique id, and so forth, from its memory to eliminate the confidentiality and integrity of the wireless links. Node capture attack suffers from severe security breach and tremendous network cost. We propose an empirically designed multiple objectives node capture attack algorithm based on optimization functions as an effective solution against the attacking efficiency of node capture attack. Finding robust assailant optimization-particle swarm optimization and genetic algorithm (FiRAO-PG) consists of multiple objectives: maximum node participation, maximum key participation, and minimum resource expenditure to find optimal nodes using PSO and GA. It will leverage a comprehensive tool to destroy maximum portion of the network realizing cost-effectiveness and higher attacking efficiency. The simulation results manifest that FiRAO-PG can provide higher fraction of compromised traffic than matrix algorithm (MA) so the attacking efficiency of FiRAO-PG is higher. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
10. The Evolutionary Algorithm to Find Robust Pareto-Optimal Solutions over Time.
- Author
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Chen, Meirong, Guo, Yinan, Liu, Haiyuan, and Wang, Chun
- Subjects
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EVOLUTIONARY algorithms , *ROBUST control , *MATHEMATICAL optimization , *DYNAMICAL systems , *GLOBAL environmental change , *PROBLEM solving - Abstract
In dynamic multiobjective optimization problems, the environmental parameters change over time, which makes the true pareto fronts shifted. So far, most works of research on dynamic multiobjective optimization methods have concentrated on detecting the changed environment and triggering the population based optimization methods so as to track the moving pareto fronts over time. Yet, in many real-world applications, it is not necessary to find the optimal nondominant solutions in each dynamic environment. To solve this weakness, a novel method called robust pareto-optimal solution over time is proposed. It is in fact to replace the optimal pareto front at each time-varying moment with the series of robust pareto-optimal solutions. This means that each robust solution can fit for more than one time-varying moment. Two metrics, including the average survival time and average robust generational distance, are present to measure the robustness of the robust pareto solution set. Another contribution is to construct the algorithm framework searching for robust pareto-optimal solutions over time based on the survival time. Experimental results indicate that this definition is a more practical and time-saving method of addressing dynamic multiobjective optimization problems changing over time. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
11. Parallel and Cooperative Particle Swarm Optimizer for Multimodal Problems.
- Author
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Zhang, Geng and Li, Yangmin
- Subjects
- *
PARTICLE swarm optimization , *PROBLEM solving , *MATHEMATICAL optimization , *MATHEMATICAL functions , *ORTHOGONAL functions , *ROBUST control - Abstract
Although the original particle swarm optimizer (PSO) method and its related variant methods show some effectiveness for solving optimization problems, it may easily get trapped into local optimum especially when solving complex multimodal problems. Aiming to solve this issue, this paper puts forward a novel method called parallel and cooperative particle swarm optimizer (PCPSO). In case that the interacting of the elements in D-dimensional function vector X=[x1,x2,…,xd,…,xD] is independent, cooperative particle swarm optimizer (CPSO) is used. Based on this, the PCPSO is presented to solve real problems. Since the dimension cannot be split into several lower dimensional search spaces in real problems because of the interacting of the elements, PCPSO exploits the cooperation of two parallel CPSO algorithms by orthogonal experimental design (OED) learning. Firstly, the CPSO algorithm is used to generate two locally optimal vectors separately; then the OED is used to learn the merits of these two vectors and creates a better combination of them to generate further search. Experimental studies on a set of test functions show that PCPSO exhibits better robustness and converges much closer to the global optimum than several other peer algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
12. Robust Optimisation Approach for Vehicle Routing Problems with Uncertainty.
- Author
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Sun, Liang and Wang, Bing
- Subjects
- *
UNCERTAINTY (Information theory) , *ROBUST control , *MATHEMATICAL optimization , *VEHICLE routing problem , *TRANSPORTATION costs , *PROBLEM solving - Abstract
We formulated a solution procedure for vehicle routing problems with uncertainty (VRPU for short) with regard to future demand and transportation cost. Unlike E-SDROA (expectation semideviation robust optimisation approach) for solving the proposed problem, the formulation focuses on robust optimisation considering situations possibly related to bidding and capital budgets. Besides, numerical experiments showed significant increments in the robustness of the solutions without much loss in solution quality. The differences and similarities of the robust optimisation model and existing robust optimisation approaches were also compared. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
13. Observer-Based Robust Fault Detection Filter Design and Optimization for Networked Control Systems.
- Author
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Jiang, Weilai, Dong, Chaoyang, Niu, Erzhuo, and Wang, Qing
- Subjects
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ROBUST control , *FAULT tolerance (Engineering) , *FILTERS & filtration design & construction , *MATHEMATICAL optimization , *MARKOVIAN jump linear systems , *LINEAR matrix inequalities - Abstract
The problem of robust fault detection filter (FDF) design and optimization is investigated for a class of networked control systems (NCSs) with random delays. The NCSs are modeled as Markovian jump systems (MJSs) by assuming that the random delays obey a Markov chain. Based on the model, an observer-based residual generator is constructed and the corresponding fault detection problem is formulated as an H∞ filtering problem by which the error between the residual signal and the fault is made as small as possible. A sufficient condition for the existence of the desired FDF is derived in terms of linear matrix inequalities (LMIs). Furthermore, to improve the performance of the robust fault detection systems, a time domain optimization approach is proposed. The solution of the optimization problem is given in the form of Moore-Penrose inverse of matrix. A numerical example is provided to illustrate the effectiveness and potential of the proposed approach. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
14. Reliability-Based Robust Design Optimization of Structures Considering Uncertainty in Design Variables.
- Author
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Wang, Shujuan, Li, Qiuyang, and Savage, Gordon J.
- Subjects
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ROBUST control , *MATHEMATICAL optimization , *UNCERTAINTY , *SENSITIVITY analysis , *COST analysis - Abstract
This paper investigates the structural design optimization to cover both the reliability and robustness under uncertainty in design variables. The main objective is to improve the efficiency of the optimization process. To address this problem, a hybrid reliability-based robust design optimization (RRDO) method is proposed. Prior to the design optimization, the Sobol sensitivity analysis is used for selecting key design variables and providing response variance as well, resulting in significantly reduced computational complexity. The single-loop algorithm is employed to guarantee the structural reliability, allowing fast optimization process. In the case of robust design, the weighting factor balances the response performance and variance with respect to the uncertainty in design variables. The main contribution of this paper is that the proposed method applies the RRDO strategy with the usage of global approximation and the Sobol sensitivity analysis, leading to the reduced computational cost. A structural example is given to illustrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
15. A Problem-Reduction Evolutionary Algorithm for Solving the Capacitated Vehicle Routing Problem.
- Author
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Liu, Wanfeng and Li, Xia
- Subjects
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EVOLUTIONARY algorithms , *VEHICLE routing problem , *MATHEMATICAL optimization , *ALGORITHMS , *STOCHASTIC convergence , *ROBUST control - Abstract
Assessment of the components of a solution helps provide useful information for an optimization problem. This paper presents a new population-based problem-reduction evolutionary algorithm (PREA) based on the solution components assessment. An individual solution is regarded as being constructed by basic elements, and the concept of acceptability is introduced to evaluate them. The PREA consists of a searching phase and an evaluation phase. The acceptability of basic elements is calculated in the evaluation phase and passed to the searching phase. In the searching phase, for each individual solution, the original optimization problem is reduced to a new smaller-size problem. With the evolution of the algorithm, the number of common basic elements in the population increases until all individual solutions are exactly the same which is supposed to be the near-optimal solution of the optimization problem. The new algorithm is applied to a large variety of capacitated vehicle routing problems (CVRP) with customers up to nearly 500. Experimental results show that the proposed algorithm has the advantages of fast convergence and robustness in solution quality over the comparative algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
16. Game Approach for H∞ Robust Control Strategy to Follow the Production in the Singularly Perturbed Bilinear Dynamic Input-Output Systems
- Author
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Cheng-ke Zhang, Huai-nian Zhu, Qiyou Liu, and Ning Bin
- Subjects
Input/output ,Mathematical optimization ,National economy ,Control theory ,General Mathematics ,Differential game ,General Engineering ,Bilinear interpolation ,Production (economics) ,Robust control ,Social production ,Saddle ,Mathematics - Abstract
For simulating and analyzing the input and output problem of national economy more accurately, this paper considers the fast and slow production processes during the course of social production development, takes stochastic economic risks into consideration, and constructs a H∞ robust control model to follow the production in the singularly perturbed dynamic input-output systems. Further introducing ideas of noncooperative differential game theory, the H∞ robust control model is transformed into a saddle-point equilibrium game model, and a new method for solving dynamic input-output problem by using saddle-point equilibrium strategies is obtained. A numerical result is presented in the end to illustrate the effectiveness of the method.
- Published
- 2017
17. Robust Stabilization andH∞Control for Uncertain Neural Networks with Mixed Time Delays
- Author
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Bin Wen, Li Liang, and Hui Li
- Subjects
Lyapunov stability ,Mathematical optimization ,Adaptive control ,Artificial neural network ,Control theory ,General Mathematics ,General Engineering ,Stability (learning theory) ,Linear matrix inequality ,Convex combination ,Robust control ,Mathematics - Abstract
This paper is concerned with the problem of robust stabilization andH∞control for a class of uncertain neural networks. For the robust stabilization problem, sufficient conditions are derived based on the quadratic convex combination property together with Lyapunov stability theory. The feedback controller we design ensures the robust stability of uncertain neural networks with mixed time delays. We further design a robustH∞controller which guarantees the robust stability of the uncertain neural networks with a givenH∞performance level. The delay-dependent criteria are derived in terms of LMI (linear matrix inequality). Finally, numerical examples are provided to show the effectiveness of the obtained results.
- Published
- 2015
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