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Thematische Bibliographien / Nonlinear control methods / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Nonlinear control methods“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 20. Februar 2023

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1

Conte, G., C. Moog, and A. Perdon. "Algebraic Methods for Nonlinear Control Systems." IEEE Transactions on Automatic Control 52, no. 12 (December 2007): 2395–96. http://dx.doi.org/10.1109/tac.2007.911476.

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2

CARMICHAEL, N., and M. D. QUINN. "Fixed-Point Methods in Nonlinear Control." IMA Journal of Mathematical Control and Information 5, no. 1 (1988): 41–67. http://dx.doi.org/10.1093/imamci/5.1.41.

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3

Han, Jing-Qing. "Nonlinear design methods for control systems." IFAC Proceedings Volumes 32, no. 2 (July 1999): 1531–36. http://dx.doi.org/10.1016/s1474-6670(17)56259-x.

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4

Hager, William W. "Multiplier Methods for Nonlinear Optimal Control." SIAM Journal on Numerical Analysis 27, no. 4 (August 1990): 1061–80. http://dx.doi.org/10.1137/0727063.

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5

Porubov, Alexey, and Boris Andrievsky. "Control methods for localization of nonlinear waves." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 375, no. 2088 (March 6, 2017): 20160212. http://dx.doi.org/10.1098/rsta.2016.0212.

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A general form of a distributed feedback control algorithm based on the speed–gradient method is developed. The goal of the control is to achieve nonlinear wave localization. It is shown by example of the sine-Gordon equation that the generation and further stable propagation of a localized wave solution of a single nonlinear partial differential equation may be obtained independently of the initial conditions. The developed algorithm is extended to coupled nonlinear partial differential equations to obtain consistent localized wave solutions at rather arbitrary initial conditions. This article is part of the themed issue ‘Horizons of cybernetical physics’.

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6

Almashaal, M. J., and A. R. Gaiduk. "METHODS COMPARISON OF NONLINEAR CONTROL SYSTEMS DESIGN." Mathematical Methods in Technologies and Technics, no. 4 (2021): 21–24. http://dx.doi.org/10.52348/2712-8873_mmtt_2021_4_21.

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7

Hedrick, J. Karl, and Swaminathan Gopalswamy. "Nonlinear flight control design via sliding methods." Journal of Guidance, Control, and Dynamics 13, no. 5 (September 1990): 850–58. http://dx.doi.org/10.2514/3.25411.

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8

Shakeel, Tanzeela, Jehangir Arshad, Mujtaba Hussain Jaffery, Ateeq Ur Rehman, Elsayed Tag Eldin, Nivin A. Ghamry, and Muhammad Shafiq. "A Comparative Study of Control Methods for X3D Quadrotor Feedback Trajectory Control." Applied Sciences 12, no. 18 (September 15, 2022): 9254. http://dx.doi.org/10.3390/app12189254.

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Unmanned aerial vehicles (UAVs), particularly quadrotor, have seen steady growth in use over the last several decades. The quadrotor is an under-actuated nonlinear system with few actuators in comparison to the degree of freedom (DOF); hence, stabilizing its attitude and positions is a significant challenge. Furthermore, the inclusion of nonlinear dynamic factors and uncertainties makes controlling its maneuverability more challenging. The purpose of this research is to design, implement, and evaluate the effectiveness of linear and nonlinear control methods for controlling an X3D quadrotor’s intended translation position and rotation angles while hovering. The dynamics of the X3D quadrotor model were implemented in Simulink. Two linear controllers, linear quadratic regulator (LQR) and proportional integral derivate (PID), and two nonlinear controllers, fuzzy controller (FC) and model reference adaptive PID Controller (MRAPC) employing the MIT rule, were devised and implemented for the response analysis. In the MATLAB Simulink Environment, the transient performance of nonlinear and linear controllers for an X3D quadrotor is examined in terms of settling time, rising time, peak time, delay time, and overshoot. Simulation results suggest that the LQR control approach is better because of its robustness and comparatively superior performance characteristics to other controllers, particularly nonlinear controllers, listed at the same operating point, as overshoot is 0.0% and other factors are minimal for the x3D quadrotor. In addition, the LQR controller is intuitive and simple to implement. In this research, all control approaches were verified to provide adequate feedback for quadrotor stability.

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Gaiduk, A. R., S. G. Kapustyan, and M. J. Almashaal. "Comparison of methods of nonlinear control systems design." Vestnik IGEU, no. 6 (December 28, 2021): 54–61. http://dx.doi.org/10.17588/2072-2672.2021.6.054-061.

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The issue of designing nonlinear control systems is a complex problem. A lot of methods are known that allow us to find a suitable control for a given nonlinear object that provides asymptotic stability of the nonlinear system equilibrium and an acceptable quality of the transient process. Many of these methods are difficult to apply in practice. Thus, comparing some of the methods in terms of simplicity of use is of great interest. Two analytical methods for the synthesis of nonlinear control systems are considered. They are the algebraic polynomial-matrix method that uses a quasilinear model, and the feedback linearization method that uses the Brunovsky model in combination with special feedbacks. A comparative analysis of the algebraic polynomial-matrix method and the feedback linearization method is carried out. It is found out that the algebraic polynomial-matrix method (APM) is much simpler than the feedback linearization method (FLM). A numerical example of designing a system that is synthesized by these methods is considered. It is found out that the system synthesized by the APM method has a region of attraction of the equilibrium position twice as large as the region of attraction of the system synthesized by the FLM method. It is reasonable to use the algebraic polynomial-matrix method with the quasilinear models in case of synthesis of control systems of objects with differentiable nonlinearities.

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10

Bartolini, G., E. Punta, and T. Zolezzi. "Simplex Methods for Nonlinear Uncertain Sliding-Mode Control." IEEE Transactions on Automatic Control 49, no. 6 (June 2004): 922–33. http://dx.doi.org/10.1109/tac.2004.829617.

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11

Tyatyushkin, A. I., and O. V. Morzhin. "Constructive methods of control optimization in nonlinear systems." Automation and Remote Control 70, no. 5 (May 2009): 772–86. http://dx.doi.org/10.1134/s0005117909050063.

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12

Betts,, JT, and I. Kolmanovsky,. "Practical Methods for Optimal Control using Nonlinear Programming." Applied Mechanics Reviews 55, no. 4 (July 1, 2002): B68. http://dx.doi.org/10.1115/1.1483351.

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13

Bell, D. "Algebraic and geometric methods in nonlinear control theory." Automatica 24, no. 4 (July 1988): 586–87. http://dx.doi.org/10.1016/0005-1098(88)90105-7.

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14

Rajkumar, V., and R. R. Mohler. "Nonlinear control methods for power systems: a comparison." IEEE Transactions on Control Systems Technology 3, no. 2 (June 1995): 231–37. http://dx.doi.org/10.1109/87.388132.

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15

Burstein, Gabriel. "Algebraic and geometric methods in nonlinear control theory." Acta Applicandae Mathematicae 11, no. 2 (February 1988): 177–91. http://dx.doi.org/10.1007/bf00047286.

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16

Kravaris, Costas, and Jeffrey C. Kantor. "Geometric methods for nonlinear process control. 1. Background." Industrial & Engineering Chemistry Research 29, no. 12 (December 1990): 2295–310. http://dx.doi.org/10.1021/ie00108a001.

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17

Belta, Calin, and Sadra Sadraddini. "Formal Methods for Control Synthesis: An Optimization Perspective." Annual Review of Control, Robotics, and Autonomous Systems 2, no. 1 (May 3, 2019): 115–40. http://dx.doi.org/10.1146/annurev-control-053018-023717.

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In control theory, complicated dynamics such as systems of (nonlinear) differential equations are controlled mostly to achieve stability. This fundamental property, which can be with respect to a desired operating point or a prescribed trajectory, is often linked with optimality, which requires minimizing a certain cost along the trajectories of a stable system. In formal verification (model checking), simple systems, such as finite-state transition graphs that model computer programs or digital circuits, are checked against rich specifications given as formulas of temporal logics. The formal synthesis problem, in which the goal is to synthesize or control a finite system from a temporal logic specification, has recently received increased interest. In this article, we review some recent results on the connection between optimal control and formal synthesis. Specifically, we focus on the following problem: Given a cost and a correctness temporal logic specification for a dynamical system, generate an optimal control strategy that satisfies the specification. We first provide a short overview of automata-based methods, in which the dynamics of the system are mapped to a finite abstraction that is then controlled using an automaton corresponding to the specification. We then provide a detailed overview of a class of methods that rely on mapping the specification and the dynamics to constraints of an optimization problem. We discuss advantages and limitations of these two types of approaches and suggest directions for future research.

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18

Dai, Honghua, and Xiaokui Yue. "Preface: Nonlinear Computational and Control Methods in Aerospace Engineering." Computer Modeling in Engineering & Sciences 122, no. 1 (2020): 1–4. http://dx.doi.org/10.32604/cmes.2020.09126.

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19

Fahroo, Fariba, and I. Michael Ross. "Pseudospectral Methods for Infinite-Horizon Nonlinear Optimal Control Problems." Journal of Guidance, Control, and Dynamics 31, no. 4 (July 2008): 927–36. http://dx.doi.org/10.2514/1.33117.

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20

Kurdila, Andrew J., Thomas W. Strganac, John L. Junkins, Jeonghwan Ko, and Maruthi R. Akella. "Nonlinear Control Methods for High-Energy Limit-Cycle Oscillations." Journal of Guidance, Control, and Dynamics 24, no. 1 (January 2001): 185–92. http://dx.doi.org/10.2514/2.4700.

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21

Bushuev, A. B., V. V. Grigoriev, and V. A. Petrov. "Positive Nonlinear Systems Synthesis Based on Optimal Control Methods." Mekhatronika, Avtomatizatsiya, Upravlenie 20, no. 2 (February 13, 2019): 67–71. http://dx.doi.org/10.17587/mau.20.67-71.

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This paper considers a nonlinear positive control system of autonomous intellectual agent’s motions. Single or multiple intellectual agents are capable of independent decision-making to achieve an odor source or radioactive source. Various biologically inspired behaviour-based approaches, such as chemotaxis, the moth inspired casting algorithm, flocking behavior and foraging, population development or species interaction are used for creation of control system of an intellectual agent. The agent is a tracking system, for example, unmanned aerial vehicle or a multicopter. The intellectual agent searches hazardous pollutants in dangerous environments. The task of a flying robot is to find the source of invisible pollutions. The aim of this work is to synthesize the control law, that provides a predetermined degree of exponential stability in a closed-loop positive system based on Lotka—Volterra equations. The methods of optimal control theory are used in the synthesis of the system. Asymptotic stability is achieved by solving the Riccati equation. The stability of the control system is an important criterion of quality to be ensured. Therefore, the intellectual agent is able to control the movements to the right and to the left, reaching the source. With the model proposed we provide the simulation and experimental results, which correspond to the quality metrics required. In this work, the control system has a given degree of exponential stability and the transient response, which confirms the possibility of using the model proposed in terrestrial mobile robots, unmanned aerial vehicles, autonomous underwater vehicles and other robots in searching a large area.

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22

Silva, Geraldo Nunes, Maria do Rosário de Pinho, Marco Antonio Teixeira, Marcelo Messias, and Elbert Macau. "Nonlinear Systems: Asymptotic Methods, Stability, Chaos, Control, and Optimization." Mathematical Problems in Engineering 2011 (2011): 1–4. http://dx.doi.org/10.1155/2011/498751.

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23

Anikonov, Yu E., Yu V. Krivtsov, and M. V. Neshchadim. "Constructive methods in the nonlinear problems of control theory." Journal of Applied and Industrial Mathematics 5, no. 2 (April 2011): 165–79. http://dx.doi.org/10.1134/s1990478911020037.

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24

Kazantzis, Nikolaos, and Costas Kravaris. "Time-discretization of nonlinear control systems via Taylor methods." Computers & Chemical Engineering 23, no. 6 (June 1999): 763–84. http://dx.doi.org/10.1016/s0098-1354(99)00007-1.

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25

Fradkov, A. L., and A. Yu Pogromsky. "Methods of Nonlinear and Adaptive Control of Chaotic Systems." IFAC Proceedings Volumes 29, no. 1 (June 1996): 2066–71. http://dx.doi.org/10.1016/s1474-6670(17)57976-8.

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26

Kravaris, Costas, and Jeffrey C. Kantor. "Geometric methods for nonlinear process control. 2. Controller synthesis." Industrial & Engineering Chemistry Research 29, no. 12 (December 1990): 2310–23. http://dx.doi.org/10.1021/ie00108a002.

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27

Chaudhary, Naveed Ishtiaq, Zeshan Aslam khan, Syed Zubair, Muhammad Asif Zahoor Raja, and Nebojsa Dedovic. "Normalized fractional adaptive methods for nonlinear control autoregressive systems." Applied Mathematical Modelling 66 (February 2019): 457–71. http://dx.doi.org/10.1016/j.apm.2018.09.028.

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28

Papageorgiou, Nikolaos S., Vicenţiu D. Rădulescu, and Dušan D. Repovš. "Relaxation methods for optimal control problems." Bulletin of Mathematical Sciences 10, no. 01 (February 25, 2020): 2050004. http://dx.doi.org/10.1142/s1664360720500046.

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We consider a nonlinear optimal control problem with dynamics described by a differential inclusion involving a maximal monotone map [Formula: see text]. We do not assume that [Formula: see text], incorporating in this way systems with unilateral constraints in our framework. We present two relaxation methods. The first one is an outgrowth of the reduction method from the existence theory, while the second method uses Young measures. We show that the two relaxation methods are equivalent and admissible.

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29

Blundell, Richard, Dennis Kristensen, and Rosa L. Matzkin. "Control Functions and Simultaneous Equations Methods." American Economic Review 103, no. 3 (May 1, 2013): 563–69. http://dx.doi.org/10.1257/aer.103.3.563.

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The control function approach is a convenient method of estimation in simultaneous equation systems. This requires that the system can be expressed in triangular form with variables satisfying a conditional mean independence restriction. Linear simultaneous models with additive errors can always be expressed in this form. However, in nonlinear nonadditive simultaneous systems, conditional independence requires a strong additional restriction known as control function separability. We argue that nonadditive models are a key characteristic of simultaneous models of economic behavior with unobserved heterogeneity. We review alternative “system” approaches and document the biases that occur when the control function approach is used inappropriately.

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30

Vasilyev, G. S., O. R. Kuzichkin, I. A. Kurilov, and D. I. Surzhik. "Development of methods to model UAVS nonlinear automatic control systems." Revista de la Universidad del Zulia 11, no. 30 (July 2, 2020): 137–47. http://dx.doi.org/10.46925//rdluz.30.10.

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When modeling Automatic Control Systems (ACS) of an unmanned aerial vehicle (UAV), it is often necessary to take into account the nonlinearity of an aircraft's reaction when the controls drift, as well as the strong influence of various destabilizing factors that make the system go out of linear mode. When known analytical and numerical methods are used to analyze dynamic systems, it is problematic to obtain general solutions that are valid for the variable parameters of the system under study and, at the same time, provide the required error value. A method has been developed to model dynamic processes in automatic non-linear UAV control systems based on linear approximation by parts and crosslinking of partial solutions with consideration of the initial conditions. An example of using the technique to model the transition characteristics of an ACS UAV with a single non-linear link is considered. Based on the analysis of errors in the calculation of the transition process, the effectiveness of the proposed approach is shown.

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31

Hałas, Krzysztof, Eugeniusz Krysiak, Tomasz Hałas, and Sławomir Stępień. "Numerical Solution of SDRE Control Problem – Comparison of the Selected Methods." Foundations of Computing and Decision Sciences 45, no. 2 (June 1, 2020): 79–95. http://dx.doi.org/10.2478/fcds-2020-0006.

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AbstractMethods for solving non-linear control systems are still being developed. For many industrial devices and systems, quick and accurate regulators are investigated and required. The most effective and promising for nonlinear systems control is a State-Dependent Riccati Equation method (SDRE). In SDRE, the problem consists of finding the suboptimal solution for a given objective function considering nonlinear constraints. For this purpose, SDRE methods need improvement.In this paper, various numerical methods for solving the SDRE problem, i.e. algebraic Riccati equation, are discussed and tested. The time of computation and computational effort is presented and compared considering selected nonlinear control plants.

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32

Singh, Sonal, and Shubhi Purwar. "Enhanced Composite Nonlinear Control Technique using Adaptive Control for Nonlinear Delayed Systems." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 13, no. 3 (May 18, 2020): 396–404. http://dx.doi.org/10.2174/2213111607666181226151059.

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Background and Introduction: The proposed control law is designed to provide fast reference tracking with minimal overshoot and to minimize the effect of unknown nonlinearities and external disturbances. Methods: In this work, an enhanced composite nonlinear feedback technique using adaptive control is developed for a nonlinear delayed system subjected to input saturation and exogenous disturbances. It ensures that the plant response is not affected by adverse effect of actuator saturation, unknown time delay and unknown nonlinearities/ disturbances. The analysis of stability is done by Lyapunov-Krasovskii functional that guarantees asymptotical stability. Results: The proposed control law is validated by its implementation on exothermic chemical reactor. MATLAB figures are provided to compare the results. Conclusion: The simulation results of the proposed controller are compared with the conventional composite nonlinear feedback control which illustrates the efficiency of the proposed controller.

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33

Ivan, Cosmin, and Mihai Catalin Arva. "Nonlinear Time Series Analysis in Unstable Periodic Orbits Identification-Control Methods of Nonlinear Systems." Electronics 11, no. 6 (March 18, 2022): 947. http://dx.doi.org/10.3390/electronics11060947.

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The main purpose of this paper is to present a solution to the well-known problems generated by classical control methods through the analysis of nonlinear time series. Among the problems analyzed, for which an explanation has been sought for a long time, we list the significant reduction in control power and the identification of unstable periodic orbits (UPOs) in chaotic time series. To accurately identify the type of behavior of complex systems, a new solution is presented that involves a method of two-dimensional representation specific to the graphical point of view, and in particular the recurrence plot (RP). An example of the issue studied is presented by applying the recurrence graph to identify the UPO in a chaotic attractor. To identify a certain type of behavior in the numerical data of chaotic systems, nonlinear time series will be used, as a novelty element, to locate unstable periodic orbits. Another area of use for the theories presented above, following the application of these methods, is related to the control of chaotic dynamical systems by using RP in control techniques. Thus, the authors’ contributions are outlined by using the recurrence graph, which is used to identify the UPO from a chaotic attractor, in the control techniques that modify a system variable. These control techniques are part of the closed loop or feedback strategies that describe control as a function of the current state of the UPO stabilization system. To exemplify the advantages of the methods presented above, the use of the recurrence graph in the control of a buck converter through the application of a phase difference signal was analyzed. The study on the command of a direct current motor using a buck converter shows, through a final concrete application, the advantages of using these analysis methods in controlling dynamic systems.

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34

Gross, Kerianne H., Matthew A. Clark, Jonathan A. Hoffman, Eric D. Swenson, and Aaron W. Fifarek. "Run-Time Assurance and Formal Methods Analysis Nonlinear System Applied to Nonlinear System Control." Journal of Aerospace Information Systems 14, no. 4 (April 2017): 232–46. http://dx.doi.org/10.2514/1.i010471.

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35

M. Rouyan, Nurhana, Renuganth Varatharajoo, Samira Eshghi, Ermira Junita Abdullah, and Shinji Suzuki. "Aircraft pitch control tracking with sliding mode control." International Journal of Engineering & Technology 7, no. 4.13 (October 9, 2018): 62. http://dx.doi.org/10.14419/ijet.v7i4.13.21330.

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Sliding mode control (SMC) is one of the robust and nonlinear control methods. An aircraft flying at high angles of attack is considered nonlinear due to flow separations, which cause aerodynamic characteristics in the region to be nonlinear. This paper presents the comparative assessment for the flight control based on linear SMC and integral SMC implemented on the nonlinear longitudinal model of a fighter aircraft. The controller objective is to track the pitch angle and the pitch rate throughout the high angles of attack envelope. Numerical treatments are carried out on selected conditions and the controller performances are studied based on their transient responses. Obtained results show that both SMCs are applicable for high angles of attack.

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CHEN, GUANRONG, JORGE L. MOIOLA, and HUA O. WANG. "BIFURCATION CONTROL: THEORIES, METHODS, AND APPLICATIONS." International Journal of Bifurcation and Chaos 10, no. 03 (March 2000): 511–48. http://dx.doi.org/10.1142/s0218127400000360.

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Bifurcation control deals with modification of bifurcation characteristics of a parameterized nonlinear system by a designed control input. Typical bifurcation control objectives include delaying the onset of an inherent bifurcation, stabilizing a bifurcated solution or branch, changing the parameter value of an existing bifurcation point, modifying the shape or type of a bifurcation chain, introducing a new bifurcation at a preferable parameter value, monitoring the multiplicity, amplitude, and/or frequency of some limit cycles emerging from bifurcation, optimizing the system performance near a bifurcation point, or a combination of some of these objectives. This article offers an overview of this emerging, challenging, stimulating, and yet promising field of research, putting the main subject of bifurcation control into perspective.

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Guang Geng and G. M. Geary. "The control of input-constrained nonlinear processes using numerical generalized predictive control methods." IEEE Transactions on Industrial Electronics 45, no. 3 (June 1998): 496–501. http://dx.doi.org/10.1109/41.679008.

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38

Byrd, Richard H., Jorge Nocedal, and Richard A. Waltz. "Steering exact penalty methods for nonlinear programming." Optimization Methods and Software 23, no. 2 (April 2008): 197–213. http://dx.doi.org/10.1080/10556780701394169.

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39

Lacroix, Benoit, Zhao Heng Liu, and Patrice Seers. "A Comparison of Two Control Methods for Vehicle Stability Control by Direct Yaw Moment." Applied Mechanics and Materials 120 (October 2011): 203–17. http://dx.doi.org/10.4028/www.scientific.net/amm.120.203.

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This paper proposes a comparison study of two vehicle stability control methods by direct yaw-moment control (DYC): a PID and a sliding controller. For the purpose of this study, control systems are based solely on vehicle side-slip angle state feedback and the lateral dynamics of the 2 DOF vehicle model are used to establish the desired response. Close-loop dynamics of the PID controller are determined with the pole placement method, and an anti-windup strategy is adopted to respond to the tire’s nonlinear characteristics. The comparison study was performed by computer simulations with a 14 DOF nonlinear vehicle model validated with experimental data. The controllers are evaluated for typical severe manoeuvres on low friction road surfaces. It is found that despite their fundamental differences, the control methods provide comparable performances for the cases studied.

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40

Galperin, A. "Optimal Iterative Methods for Nonlinear Equations." Numerical Functional Analysis and Optimization 30, no. 5-6 (June 30, 2009): 499–522. http://dx.doi.org/10.1080/01630560902987956.

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41

Lupu, C., C. Petrescu, and C. Dimon. "MULTI-MODEL AND INVERSE MODEL CONTROL METHODS FOR NONLINEAR SYSTEMS." IFAC Proceedings Volumes 40, no. 18 (September 2007): 253–58. http://dx.doi.org/10.3182/20070927-4-ro-3905.00043.

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42

Kim, Wonhee, Donghoon Shin, Youngwoo Lee, and Chung Choo Chung. "Survey of Nonlinear Control Methods to Permanent Magnet Stepping Motors." Journal of Institute of Control, Robotics and Systems 20, no. 3 (March 1, 2014): 323–32. http://dx.doi.org/10.5302/j.icros.2014.14.9019.

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43

Aydi, Amira, Mohamed Djemel, and Mohamed Chtourou. "Two fuzzy internal model control methods for nonlinear uncertain systems." International Journal of Intelligent Computing and Cybernetics 10, no. 2 (June 12, 2017): 223–40. http://dx.doi.org/10.1108/ijicc-07-2016-0026.

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Purpose The purpose of this paper is to use the internal model control to deal with nonlinear stable systems affected by parametric uncertainties. Design/methodology/approach The dynamics of a considered system are approximated by a Takagi-Sugeno fuzzy model. The parameters of the fuzzy rules premises are determined manually. However, the parameters of the fuzzy rules conclusions are updated using the descent gradient method under inequality constraints in order to ensure the stability of each local model. In fact, without making these constraints the training algorithm can procure one or several unstable local models even if the desired accuracy in the training step is achieved. The considered robust control approach is the internal model. It is synthesized based on the Takagi-Sugeno fuzzy model. Two control strategies are considered. The first one is based on the parallel distribution compensation principle. It consists in associating an internal model control for each local model. However, for the second strategy, the control law is computed based on the global Takagi-Sugeno fuzzy model. Findings According to the simulation results, the stability of all local models is obtained and the proposed fuzzy internal model control approaches ensure robustness against parametric uncertainties. Originality/value This paper introduces a method for the identification of fuzzy model parameters ensuring the stability of all local models. Using the resulting fuzzy model, two fuzzy internal model control designs are presented.

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44

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Lu, Zuliang. "Adaptive mixed finite element methods for nonlinear optimal control problems." Lobachevskii Journal of Mathematics 32, no. 1 (January 2011): 1–15. http://dx.doi.org/10.1134/s1995080211010124.

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Goto, Norihiro, and Hiroyasu Kawable. "Direct optimization methods applied to a nonlinear optimal control problem." Mathematics and Computers in Simulation 51, no. 6 (February 2000): 557–77. http://dx.doi.org/10.1016/s0378-4754(99)00145-7.

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Schäfer, Andreas, Peter Kühl, Moritz Diehl, Johannes Schlöder, and Hans Georg Bock. "Fast reduced multiple shooting methods for nonlinear model predictive control." Chemical Engineering and Processing: Process Intensification 46, no. 11 (November 2007): 1200–1214. http://dx.doi.org/10.1016/j.cep.2006.06.024.

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