Relevant bibliographies by topics / Fuzzy interval optimal control problem

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Fuzzy interval optimal control problem'

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Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Fuzzy interval optimal control problem"

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Campos, José Renato, Edvaldo Assunção, Geraldo Nunes Silva, Weldon Alexander Lodwick, Marcelo Carvalho Minhoto Teixeira, and Gino Gustavo Maqui-Huamán. "Fuzzy interval optimal control problem." Fuzzy Sets and Systems 385 (April 2020): 169–81. http://dx.doi.org/10.1016/j.fss.2019.05.003.

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Abd El-Wahed Khalifa, Hamiden, Sultan S. Alodhaibi, and Pavan Kumar. "Solving Constrained Flow-Shop Scheduling Problem through Multistage Fuzzy Binding Approach with Fuzzy Due Dates." Advances in Fuzzy Systems 2021 (March 4, 2021): 1–8. http://dx.doi.org/10.1155/2021/6697060.

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This paper deals with constrained multistage machines flow-shop (FS) scheduling model in which processing times, job weights, and break-down machine time are characterized by fuzzy numbers that are piecewise as well as quadratic in nature. Avoiding to convert the model into its crisp, the closed interval approximation for the piecewise quadratic fuzzy numbers is incorporated. The suggested method leads a noncrossing optimal sequence to the considered problem and minimizes the total elapsed time under fuzziness. The proposed approach helps the decision maker to search for applicable solution related to real-world problems and minimizes the total fuzzy elapsed time. A numerical example is provided for the illustration of the suggested methodology.
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Lin, Jian, Qiang Zhang, and Fanyong Meng. "A Novel Algorithm for Group Decision Making Based on Continuous Optimal Aggregation Operator and Shapley Value." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 27, no. 06 (December 2019): 969–1002. http://dx.doi.org/10.1142/s0218488519500430.

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Interval linguistic preference relation is an effective tool for expressing experts’ preference in group decision making under uncertain linguistic environment. A new aggregation operator called continuous chi-square deviation based 2-tuple linguistic ordered weighted quasi-averaging (C-CDLOWQ) operator is proposed to transform the interval linguistic preference relations into precise linguistic preference relations. Some desirable properties and special cases of the C-CDLOWQ operator are analyzed in detail. To take the interactive phenomenon among experts into account, the Shapley weighting vector is presented to integrate the expected linguistic preference relations. The λ-fuzzy measure is employed to simplify the fuzzy measure on expert set. A CS-GDM algorithm is developed to group decision making with interval linguistic preference relations. The application in commercial investment problem is provided to illustrate the effectiveness of CS-GDM algorithm.
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LI, DENG-FENG, and YONG-CHUN WANG. "MATHEMATICAL PROGRAMMING APPROACH TO MULTIATTRIBUTE DECISION MAKING UNDER INTUITIONISTIC FUZZY ENVIRONMENTS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16, no. 04 (August 2008): 557–77. http://dx.doi.org/10.1142/s0218488508005418.

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There exists little investigation on multiattribute decision making under intuitionistic fuzzy environments although both crisp and fuzzy multiattribute decision making have achieved a great progress. In this paper, multiattribute decision making problems using intuitionistic fuzzy sets are investigated and the TOPSIS is further extended to develop one new methodology for solving such problems. In this methodology, an interval fractional programming model is constructed on the basis of the relative closeness coefficient using the TOPSIS. Comprehensive evaluation of each alternative, which may be described as an intuitionistic fuzzy set or interval number, is calculated using two auxiliary mathematical programming problems derived from the interval fractional programming model proposed in this paper. Optimal degrees of membership for alternatives are calculated to determine their ranking order using the concept of likelihood based on the ranking method of interval numbers. Implementation process of the method proposed in this paper is illustrated with a numerical example.
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Castillo, Oscar, Fevrier Valdez, Cinthia Peraza, Jin Hee Yoon, and Zong Woo Geem. "High-Speed Interval Type-2 Fuzzy Systems for Dynamic Parameter Adaptation in Harmony Search for Optimal Design of Fuzzy Controllers." Mathematics 9, no. 7 (April 1, 2021): 758. http://dx.doi.org/10.3390/math9070758.

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Fuzzy systems have become a good solution to the problem of fixed parameters in metaheuristic algorithms, proving their efficiency when performing dynamic parameter adaptations using type-1 and type-2 fuzzy logic. However, the computational cost of type-2 fuzzy systems when using the continuous enhanced Karnik–Mendel (CKM) algorithm for type-reduction, when applied to control and optimization, is too high. Therefore, it is proposed to use an approximation to the CKM algorithm in the type-2 fuzzy system for adjusting the pitch adjustment rate (PArate) parameter in the original harmony search algorithm (HS). The main contribution of this article is to verify that the implementation of the proposed methodology achieves results that are equivalent to the interval type-2 fuzzy system with the CKM algorithm, but in less computing time and also allowing an efficient dynamic parameter adaptation. It is noteworthy that this method is relatively new in the area of metaheuristics algorithms so there is a current interest to work with this methodology. The proposed method was used in optimizing the antecedents and consequents for an interval type-2 fuzzy controller of direct current motor. Experimental results without noise and then with uniform random noise numbers (Gaussian noise) in the controller were obtained to verify that the implementation is efficient when compared to conventional and other existing methods.
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Khooban, Mohammad Hassan, Alireza Alfi, and Davood Nazari Maryam Abadi. "Teaching–learning-based optimal interval type-2 fuzzy PID controller design: a nonholonomic wheeled mobile robots." Robotica 31, no. 7 (April 19, 2013): 1059–71. http://dx.doi.org/10.1017/s0263574713000283.

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SUMMARYThis paper introduces an optimal interval type-2 fuzzy proportional–integral–derivative (PID) controller to achieve the best trajectory tracking for nonholonomic wheeled mobile robots (WMRs). In the core of the proposed method, a novel population-based optimization algorithm, called teaching–learning-based optimization (TLBO), is employed for evolving the parameters of the controller as well as the parameters of the input and output membership functions. Two PID controllers are designed for each of two wheels separately whereas each controller has two inputs and one output that are logically connected by nine rules. The controller can handle the problem of integrated kinematic and dynamic tracking in the presence of uncertainties. Simulation results demonstrate the superiority of the proposed control scheme.
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Sama, Hanumantha Rao, Vasanta Kumar Vemuri, and Venkata Siva Nageswara Hari Prasad Boppana. "Optimal Control Policy for a Two-Phase M/M/1 Unreliable Gated Queue under N-Policy with a Fuzzy Environment." Ingénierie des systèmes d information 26, no. 4 (August 31, 2021): 357–64. http://dx.doi.org/10.18280/isi.260403.

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The two-phase service models analyzed by several authors considered only the probabilistic nature of the queue parameters with fixed cost elements. But the queue parameters and cost elements will be in general are of both possibilistic and probabilistic in nature. Analyzing the performance of the queueing systems with fuzzy environment facilitates to investigate for the possibilistic interval estimates to the performance measures of a queueing system rather than point estimates. In this work, it is proposed to construct membership function of the fuzzy cost function to obtain confidence estimates for some performance measures of a controllable two-phase service single server Markovian gated queue with server startups and breakdowns under N-policy in which the queue parameters viz. arrival rate, startup rate, batch service rate, individual service rate, repair rate and cost elements are all defined as fuzzy numbers. Based on Zadeh’s extension principle and the α-cuts, a set of parametric nonlinear programming problems are developed to find the upper and lower bounds of the minimum total expected cost per unit time at the possibility level α. As the analytical solutions of the nonlinear programming problems developed for the proposed model are tedious, considering the system parameters and cost elements as trapezoidal fuzzy numbers, numerical results for the lower and upper bounds of the optimal threshold N* and the minimum total expected cost per unit time are computed using the nonlinear programming solver available in MATLAB.
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Khan, Indadul, Sova Pal, and Manas Kumar Maiti. "A Hybrid PSO-GA Algorithm for Traveling Salesman Problems in Different Environments." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 27, no. 05 (October 2019): 693–717. http://dx.doi.org/10.1142/s0218488519500314.

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In this study particle swarm optimization (PSO) is modified and hybridised with genetic algorithm (GA) using one’s output as the other's input to solve Traveling Salesman Problem(TSP). Here multiple velocity update rules are introduced to modify the PSO and at the time of the movement of a solution, one rule is selected depending on its performances using roulette wheel selection process. Each velocity update rule and the corresponding solution update rule are defined using swap sequence (SS) and swap operation (SO). K-Opt operation is applied in a regular interval of iterations for the movement of any stagnant solution. GA is applied on the final output swarm of the PSO to search the optimal path of the large size TSPs. Roulette wheel selection process, multi-point cyclic crossover and the K-opt operation for the mutation are used in the GA phase. The algorithm is tested in crisp environment using different size benchmark test problems available in the TSPLIB. In the crisp environment the algorithm gives approximately 100% success rate for the test problems up to considerably large sizes. Efficiency of the algorithm is tested with some other existing algorithms in the literature using Friedman test. Some approaches are incorporated with this algorithm for finding solutions of the TSPs in imprecise (fuzzy/rough) environment. Imprecise problems are generated from the crisp problems randomly, solved and obtained results are discussed. It is observed that the performance of the proposed algorithm is better compared to the some other algorithms in the existing literature with respect to the accuracy and the consistency for the symmetric TSPs as well as the Asymmetric TSPs.
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Campos, J. R., E. Assunção, G. N. Silva, W. A. Lodwick, and M. C. M. Teixeira. "Discrete-time interval optimal control problem." International Journal of Control 92, no. 8 (December 8, 2017): 1778–84. http://dx.doi.org/10.1080/00207179.2017.1410575.

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Ji, Linna, Fengbao Yang, and Xiaoming Guo. "Image Fusion Algorithm Selection Based on Fusion Validity Distribution Combination of Difference Features." Electronics 10, no. 15 (July 21, 2021): 1752. http://dx.doi.org/10.3390/electronics10151752.

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Aiming at addressing the problem whereby existing image fusion models cannot reflect the demand of diverse attributes (e.g., type or amplitude) of difference features on algorithms, leading to poor or invalid fusion effect, this paper puts forward the construction and combination of difference features fusion validity distribution based on intuition-possible sets to deal with the selection of algorithms with better fusion effect in dual mode infrared images. Firstly, the distances of the amplitudes of difference features between fused images and source images are calculated. The distances can be divided into three levels according to the fusion result of each algorithm, which are regarded as intuition-possible sets of fusion validity of difference features, and a novel construction method of fusion validity distribution based on intuition-possible sets is proposed. Secondly, in view of multiple amplitude intervals of each difference feature, this paper proposes a distribution combination method based on intuition-possible set ordering. Difference feature score results are aggregated by a fuzzy operator. Joint drop shadows of difference feature score results are obtained. Finally, the experimental results indicate that our proposed method can achieve optimal selection of algorithms that has relatively better effect on the fusion of difference features according to the varied feature amplitudes.
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Dissertations / Theses on the topic "Fuzzy interval optimal control problem"

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Campos, José Renato. "Problemas de controle ótimo intervalar e intervalar fuzzy /." Ilha Solteira, 2018. http://hdl.handle.net/11449/157499.

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Orientador: Edvaldo Assunção
Resumo: Neste trabalho estudamos problemas de controle ótimo intervalar e intervalar fuzzy. Em particular, propomos problemas de controle ótimo via teoria de incerteza generalizada e teoria dos conjuntos fuzzy. Dentre os vários tipos de incerteza generalizada utilizamos apenas a intervalar. Embora as abordagens do processo de solução dos problemas de controle ótimo intervalar e intervalar fuzzy sejam similares, as premissas iniciais para o uso e identificação de aplicação delas em problemas práticos são distintas assim como é distinto o processo de tomada de decisão. Assim, propomos inicialmente o problema de controle ótimo intervalar em tempo discreto. A primeira proposta de solução para o problema de controle ótimo intervalar em tempo discreto é construída usando a aritmética intervalar restrita de níveis simples juntamente com a técnica de programação dinâmica. As respostas do problema de controle ótimo intervalar contêm as possibilidades de soluções viáveis, e para implementar uma solução viável para o usuário final usamos a solução que minimiza o arrependimento máximo nos exemplos numéricos. A segunda proposta de solução para o problema de controle ótimo intervalar em tempo discreto é realizada com a aritmética intervalar restrita uma vez que essa aritmética intervalar é mais geral do que a aritmética intervalar restrita de níveis simples pois não considera os intervalos envolvidos nas operações variando de forma dependente. Exemplos numéricos também foram construídos e ilustram... (Resumo completo, clicar acesso eletrônico abaixo)
Abstract: In this work we study the interval optimal control problem and fuzzy interval optimal control problem. In particular, we propose optimal control problems via theory of generalized uncertainty and fuzzy set theory. Among the various types of generalized uncertainty we use only the interval uncertainty. Although the approaches to solve the interval optimal control problem and fuzzy interval optimal control problem are similar, the input data for problems with generalized uncertainty and flexibility are distinct as is distinct the decision-making process. Thus, we initially propose the discrete-time interval optimal control problem. The first solution method to solve the discrete-time interval optimal control problem is constructed using single-level constrained interval arithmetic coupled with a dynamic programming technique. The optimal interval solution contains the real-valued optimal solutions, and to implement a feasible solution to the user we use the minimax regret criterion in numerical examples. The second solution method to solve the discrete-time interval optimal control problem is done with the constrained interval arithmetic since this interval arithmetic is more general than the single-level constrained interval arithmetic because it does not have its intervals varying of dependent form in interval operations. Numerical examples have also been constructed and illustrate the method of solution. Finally, we study the discrete-time fuzzy interval optimal control prob... (Complete abstract click electronic access below)
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Book chapters on the topic "Fuzzy interval optimal control problem"

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Li, Hongyi, Ligang Wu, Hak-Keung Lam, and Yabin Gao. "Optimal Control of Interval Type-2 Fuzzy-Model-Based Systems." In Analysis and Synthesis for Interval Type-2 Fuzzy-Model-Based Systems, 155–75. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0593-0_10.

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Deng, Yanfei. "An Optimal Control Model for Biogas Investment Problem Under Fuzzy Environment." In Advances in Intelligent Systems and Computing, 171–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-47241-5_13.

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Figueroa-García, Juan Carlos, and Germán Hernandez. "Computing Optimal Solutions of a Linear Programming Problem with Interval Type-2 Fuzzy Constraints." In Lecture Notes in Computer Science, 567–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28942-2_51.

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El Hassan, Zerrik, and EL Kabouss Abella. "Regional Optimal Control Problem of a Heat Equation with Bilinear Bounded Boundary Controls." In Recent Advances in Intuitionistic Fuzzy Logic Systems and Mathematics, 131–42. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53929-0_10.

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Mitsuishi, Takashi, and Yasunari Shidama. "Fuzzy Number as Input for Approximate Reasoning and Applied to Optimal Control Problem." In Artificial Intelligence and Soft Computing, 144–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13208-7_19.

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Singh, Satvir, J. S. Saini, and Arun Khosla. "A PSO-Based Framework for Designing Fuzzy Systems from Noisy Data Set." In Machine Learning Algorithms for Problem Solving in Computational Applications, 210–28. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1833-6.ch013.

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In most of Fuzzy Logic System (FLS) designs, human reasoning is encoded into programs to make decisions and/or control systems. Designing an optimal FLS is equivalent to an optimization problem, in which efforts are made to locate a point in fitness search-space where the performance is better than that of other locations. The number of parameters to be tuned in designing an FLS is quite large. Also, fitness search space is highly non-linear, deceptive, non-differentiable, and multi-modal in nature. Noisy data, from which to construct the FLS, may make the design problem even more difficult. This chapter presents a framework to design Type-1 (T1) and Interval Type-2 (IT2) FLSs (Liang and Mendel, 2000c, Mendel, 2001, 2007, Mendel et al., 2006) using Particle Swarm Optimization (PSO) (Eberhart and Kennedy, 1995, Kennedy and Eberhart, 1995). This framework includes the use of PSO based Nature Inspired (NI) Toolbox discussed in the chapter titled, “Nature-Inspired Toolbox to Design and Optimize Systems.”
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"Optimal Quadratic Cost Problem over an Infinite Time Interval: Algebraic Riccati Equation." In Control Theory for Partial Differential Equations, 121–77. Cambridge University Press, 2000. http://dx.doi.org/10.1017/cbo9781107340848.004.

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"Optimal Quadratic Cost Problem Over a Preassigned Finite Time Interval: Differential Riccati Equation." In Control Theory for Partial Differential Equations, 11–120. Cambridge University Press, 2000. http://dx.doi.org/10.1017/cbo9781107340848.003.

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Li, Minghuang, and Fusheng Yu. "Semidefinite Programming-Based Method for Implementing Linear Fitting to Interval-Valued Data." In Contemporary Theory and Pragmatic Approaches in Fuzzy Computing Utilization, 172–87. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-1870-1.ch012.

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Building a linear fitting model for a given interval-valued data set is challenging since the minimization of the residue function leads to a huge combinatorial problem. To overcome such a difficulty, this article proposes a new semidefinite programming-based method for implementing linear fitting to interval-valued data. First, the fitting model is cast to a problem of quadratically constrained quadratic programming (QCQP), and then two formulae are derived to develop the lower bound on the optimal value of the nonconvex QCQP by semidefinite relaxation and Lagrangian relaxation. In many cases, this method can solve the fitting problem by giving the exact optimal solution. Even though the lower bound is not the optimal value, it is still a good approximation of the global optimal solution. Experimental studies on different fitting problems of different scales demonstrate the good performance and stability of our method. Furthermore, the proposed method performs very well in solving relatively large-scale interval-fitting problems.
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Wong, K., H. W. J. Lee, and Chi Kin Chan. "Optimal Feedback Production for a Supply Chain." In Successful Strategies in Supply Chain Management, 50–66. IGI Global, 2005. http://dx.doi.org/10.4018/978-1-59140-303-6.ch003.

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In this chapter, we modeled the dynamics of a supply chain considered by several authors. An infinite-horizon, time-delayed, optimal control problem was thus obtained. By approximating the time interval [0, ¥] by [0, Tf ], we obtained an approximated problem (P(Tf )) which could be easily solved by the control parameterization method. Moreover, we could show that the objective function of the approximated problem converged to that of the original problem as Tf ® ¥. Several examples have been solved to illustrate the efficiency of our method. In these examples, some important results relating the production rate to demand rate have been developed.
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Conference papers on the topic "Fuzzy interval optimal control problem"

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Dmitruk, Andrei, and Ivan Samylovskiy. "Optimal Synthesis in the Goddard Problem on a Constrained Time Interval." In 2018 17th European Control Conference (ECC). IEEE, 2018. http://dx.doi.org/10.23919/ecc.2018.8550227.

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Liu, Gang, Jing-hua Han, Yu-bin Wu, and Mei-jiao Liu. "An Optimal Control Problem of Adaptive Fuzzy Controllers for Fuzzy Control Systems." In 2010 International Conference on Intelligent Computation Technology and Automation (ICICTA). IEEE, 2010. http://dx.doi.org/10.1109/icicta.2010.583.

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Leal, Ulcilea A. Severino, Geraldo N. Silva, and Weldon A. Lodwick. "Necessary condition for optimal control problem with interval-valued objective function." In XXXV CNMAC - Congresso Nacional de Matemática Aplicada e Computacional. SBMAC, 2015. http://dx.doi.org/10.5540/03.2015.003.01.0131.

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Leal, Ulcilea A. Severino, Geraldo N. Silva, and Weldon A. Lodwick. "Multi-objective optimization in optimal control problem with interval-valued objective function." In XXXV CNMAC - Congresso Nacional de Matemática Aplicada e Computacional. SBMAC, 2015. http://dx.doi.org/10.5540/03.2015.003.01.0130.

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Allawi, Ziyad T., and Turki Y. Abdalla. "An optimal defuzzification method for interval type-2 fuzzy logic control scheme." In 2015 Science and Information Conference (SAI). IEEE, 2015. http://dx.doi.org/10.1109/sai.2015.7237207.

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Razavi, Aliakbar, and Amirreza Kosari. "Fuzzy Optimal Control Approach in Low-Thrust Orbit Transfer Problem." In 2021 26th International Computer Conference, Computer Society of Iran (CSICC). IEEE, 2021. http://dx.doi.org/10.1109/csicc52343.2021.9420576.

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Lin, Feng-Tse. "Shortest Path Problem Based on Interval-Valued Fuzzy Numbers and Signed Distance Defuzzification Method." In 2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC). IEEE, 2009. http://dx.doi.org/10.1109/icicic.2009.331.

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Ghaemi, Mostafa, Mohammad-R. Akbarzadeh-T., and Mohsen Jalaeian-F. "Optimal design of adaptive interval type-2 fuzzy sliding mode control using Genetic algorithm." In 2011 2nd International Conference on Control, Instrumentation, and Automation (ICCIA). IEEE, 2011. http://dx.doi.org/10.1109/icciautom.2011.6356731.

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Simon, Andra´s, and George T. Flowers. "Magnetic Bearing Control Using Interval Type-2 Fuzzy Logic." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-82507.

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Magnetic bearings are an exciting and innovative technology that has seen considerable advances in recent years. Being unstable by nature, these systems require active control. Most often linear techniques are used very successfully. On the other hand, there are applications where linear methods have limited effectiveness. Fuzzy logic control performs very well in nonlinear control situations where the plant parameters are either partially or mostly unidentified. Its effectiveness for nonlinear systems also offers advantages to magnetic bearing systems. Type-2 fuzzy logic systems represent significant advances over traditional fuzzy logic systems in general. These fuzzy logic systems are capable to deal with uncertainties which can be found in almost every practical system. Uncertainties stem from several sources; noise present in the position input signals, the location and shape of fuzzy sets and the fuzzy rule-base describing the operation of the fuzzy controller, among others. Since a mathe-matical model of the controlled plant is often only a conveniently close approximation of the real process at hand, a major challenge lies in the application of the control methods to real plants. Type-2 fuzzy logic and fuzzy logic systems in general tackle the control problem at hand using human reasoning based on rules and expert knowledge of the plant described by human expressions. The current work consist of model development, controller design, simulation and experimental validation. The basic simulation model consist of a horizontal shaft supported by a radial magnetic bearing. The magnetic bearing is modeled as a nonlinear element. The controller designs are implemented and tested using a bench-top rotor rig equipped with a radial magnetic bearing. Some representative results are presented and discussed.
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Rogachev, N. G. "Fuzzy-Optimal Online Control of a Mobile Robot in the Obstacle Avoidance Problem." In 2020 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon). IEEE, 2020. http://dx.doi.org/10.1109/fareastcon50210.2020.9271297.

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