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Zeitschriftenartikel zum Thema „Optimized Control“

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Veröffentlicht am 9. November 2024

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1

Hatjimihail, A. T. „Genetic algorithms-based design and optimization of statistical quality-control procedures“. Clinical Chemistry 39, Nr. 9 (01.09.1993): 1972–78. http://dx.doi.org/10.1093/clinchem/39.9.1972.

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Abstract In general, one cannot use algebraic or enumerative methods to optimize a quality-control (QC) procedure for detecting the total allowable analytical error with a stated probability with the minimum probability for false rejection. Genetic algorithms (GAs) offer an alternative, as they do not require knowledge of the objective function to be optimized and can search through large parameter spaces quickly. To explore the application of GAs in statistical QC, I developed two interactive computer programs based on the deterministic crowding genetic algorithm. Given an analytical process, the program "Optimize" optimizes a user-defined QC procedure, whereas the program "Design" designs a novel optimized QC procedure. The programs search through the parameter space and find the optimal or near-optimal solution. The possible solutions of the optimization problem are evaluated with computer simulation.

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Mohammed, Reham H., Ahmed M. Ismaiel, Basem E. Elnaghi und Mohamed E. Dessouki. „African vulture optimizer algorithm based vector control induction motor drive system“. International Journal of Electrical and Computer Engineering (IJECE) 13, Nr. 3 (01.06.2023): 2396. http://dx.doi.org/10.11591/ijece.v13i3.pp2396-2408.

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<span lang="EN-US">This study describes a new optimization approach for three-phase induction motor speed drive to minimize the integral square error for speed controller and improve the dynamic speed performance. The new proposed algorithm, African vulture optimizer algorithm (AVOA) optimizes internal controller parameters of a fuzzy like proportional differential (PD) speed controller. The AVOA is notable for its ease of implementation, minimal number of design parameters, high convergence speed, and low computing burden. This study compares fuzzy-like PD speed controllers optimized with AVOA to adaptive fuzzy logic speed regulators, fuzzy-like PD optimized with genetic algorithm (GA), and proportional integral (PI) speed regulators optimized with AVOA to provide speed control for an induction motor drive system. The drive system is simulated using MATLAB/Simulink and laboratory prototype is implemented using DSP-DS1104 board. The results demonstrate that the suggested fuzzy-like PD speed controller optimized with AVOA, with a speed steady state error performance of 0.5% compared to the adaptive fuzzy logic speed regulator’s 0.7%, is the optimum alternative for speed controller. The results clarify the effectiveness of the controllers based on fuzzy like PD speed controller optimized with AVOA for each performance index as it provides lower overshoot, lowers rising time, and high dynamic response.</span>

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Danesfahani, Reza, und Ali Montazeri. „Optimized Automatic Frequency Control“. IEEJ Transactions on Electronics, Information and Systems 126, Nr. 8 (2006): 938–41. http://dx.doi.org/10.1541/ieejeiss.126.938.

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4

Sameh, Mariam A., Mostafa I. Marei, M. A. Badr und Mahmoud A. Attia. „An Optimized PV Control System Based on the Emperor Penguin Optimizer“. Energies 14, Nr. 3 (01.02.2021): 751. http://dx.doi.org/10.3390/en14030751.

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During the day, photovoltaic (PV) systems are exposed to different sunlight conditions in addition to partial shading (PS). Accordingly, maximum power point tracking (MPPT) techniques have become essential for PV systems to secure harvesting the maximum possible power from the PV modules. In this paper, optimized control is performed through the application of relatively newly developed optimization algorithms to PV systems under Partial Shading (PS) conditions. The initial value of the duty cycle of the boost converter is optimized for maximizing the amount of power extracted from the PV arrays. The emperor penguin optimizer (EPO) is proposed not only to optimize the initial setting of duty cycle but to tune the gains of controllers used for the boost converter and the grid-connected inverter of the PV system. In addition, the performance of the proposed system based on the EPO algorithm is compared with another newly developed optimization technique based on the cuttlefish algorithm (CFA). Moreover, particle swarm optimization (PSO) algorithm is used as a reference algorithm to compare results with both EPO and CFA. PSO is chosen since it is an old, well-tested, and effective algorithm. For the evaluation of performance of the proposed PV system using the proposed algorithms under different PS conditions, results are recorded and introduced.

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Isobe, Jiro. „Optimized PM Control with QCM“. JAPAN TAPPI JOURNAL 55, Nr. 9 (2001): 1241–44. http://dx.doi.org/10.2524/jtappij.55.1241.

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6

Fracasso, P. T., F. S. Barnes und A. H. R. Costa. „Optimized Control for Water Utilities“. Procedia Engineering 70 (2014): 678–87. http://dx.doi.org/10.1016/j.proeng.2014.02.074.

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7

Yang, Zeqing, Wei Cui, Wenbo Zhang, Zhaohua Wang, Bingyin Zhang, Yingshu Chen, Ning Hu, Xiaoyang Bi und Wei Hu. „A New Performance Optimization Method for Linear Motor Feeding System“. Actuators 12, Nr. 6 (06.06.2023): 233. http://dx.doi.org/10.3390/act12060233.

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The linear motor feeding system is a typical electromechanical coupling system. Conventional characteristic analyses of electromechanical coupling often overlook the influence of flexible deformation in critical components of the linear motor feeding system. Moreover, when employing genetic algorithms to optimize servo system PID control parameters, slow convergence, nonconvergence, or premature convergence problems may arise. To address these issues, this paper proposes a new performance optimization method for a linear motor feeding system. The method uses a combination of “multi-body theory + finite element” to accurately account for the flexible deformation of critical components of the feeding system, establishes a rigid–flexible electromechanical coupling model of the linear motor feeding system, and optimizes the PID parameters of the established model with an improved adaptive genetic algorithm. Simulation results demonstrate that, when utilizing an adaptive genetic algorithm to optimize the rigid–flexible electromechanical coupling model and a control system model that disregards flexible body deformation, the system achieves stability in 0.02 s and 0.027 s with overshoots of 13% and 27%, respectively. These outcomes confirm the accuracy and importance of considering flexible body deformation in the optimization performance of a linear motor feeding system. At the same time, the time required to reach the steady state of the rigid–flexible electromechanical coupling model optimized by the adaptive genetic algorithm is shortened from 0.035 s to 0.02 s. The sinusoidal signal response curve of the optimized system does not exhibit any peak overshoot compared with that of the nonoptimized system, and the response speed is also faster. These results demonstrate the effectiveness of the rigid–flexible electromechanical coupling model optimized by the nonlinear adaptive genetic algorithm. The displacement response curves of the linear motor feeding system under different workbench loads are obtained through experiments and compared with those obtained from simulations to verify the established model and the correctness of the proposed method.

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Guo, Zhi Jun, Dong Dong Yue und Jing Bo Wu. „Optimization of Regenerative Braking Control Strategy for Pure Electric Vehicle“. Applied Mechanics and Materials 872 (Oktober 2017): 331–36. http://dx.doi.org/10.4028/www.scientific.net/amm.872.331.

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The regenerative braking strategy for precursor pure electric vehicle was studied in this paper. Firstly, a constraint optimization model was established for the braking force distribution, in which both braking stability and recovery efficiency of braking energy were taken into account. Secondly, Particle Swarm Optimization (PSO) algorithm was applied to optimize the multi key parameters in the model. Finally, the optimized braking torque of the motor was obtained at different speed, different braking strength and different battery charge state. A vehicle model was built to validate the optimized results through simulation. The results showed that, compared with the original control strategy, the optimized control strategy not only could increase the braking stability effectively, but also improve the energy recovery efficiency in a certain extent.

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Dey, Debadeepta, Tian Liu, Boris Sofman und James Bagnell. „Efficient Optimization of Control Libraries“. Proceedings of the AAAI Conference on Artificial Intelligence 26, Nr. 1 (20.09.2021): 1983–89. http://dx.doi.org/10.1609/aaai.v26i1.8383.

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A popular approach to high dimensional control problems in robotics uses a library of candidate “maneuvers” or “trajectories”. The library is either evaluated on a fixed number of candidate choices at runtime (e.g. path set selection for planning) or by iterating through a sequence of feasible choices until success is achieved (e.g. grasp selection). The performance of the library relies heavily on the content and order of the sequence of candidates. We propose a provably efficient method to optimize such libraries, leveraging recent advances in optimizing submodular functions of sequences. This approach is demonstrated on two important problems: mobile robot navigation and manipulator grasp set selection. In the first case, performance can be improved by choosing a subset of candidates which optimizes the metric under consideration (cost of traversal). In the second case, performance can be optimized by minimizing the depth in the list that is searched before a successful candidate is found. Our method can be used in both on-line and batch settings with provable performance guarantees, and can be run in an anytime manner to handle real-time constraints.

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Ma, Teng, Fengrong Bi, Xu Wang, Congfeng Tian, Jiewei Lin, Jie Wang und Gejun Pang. „Optimized Fuzzy Skyhook Control for Semi-Active Vehicle Suspension with New Inverse Model of Magnetorheological Fluid Damper“. Energies 14, Nr. 6 (17.03.2021): 1674. http://dx.doi.org/10.3390/en14061674.

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To improve the performance of vehicle suspension, this paper proposes a semi-active vehicle suspension with a magnetorheological fluid (MRF) damper. We designed an optimized fuzzy skyhook controller with grey wolf optimizer (GWO) algorithm base on a new neuro-inverse model of the MRF damper. Because the inverse model of the MRF damper is difficult to establish directly, the Elman neural network was applied. The novelty of this study is the application of the new inverse model for semi-active vibration control and optimization of the semi-active suspension control method. The calculation results showed that the new inverse model can accurately calculate the required control current. The fuzzy skyhook control method optimized by the grey wolf optimizer (GWO) algorithm was established based on the inverse model to control the suspension vibration. The simulation results showed that the optimized fuzzy skyhook control method can simultaneously reduce the amplitude of vertical acceleration, suspension deflection, and tire dynamic load.

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XIN, Bin, Jie CHEN und Zhi-Hong PENG. „Intelligent Optimized Control: Overview and Prospect“. Acta Automatica Sinica 39, Nr. 11 (2013): 1831. http://dx.doi.org/10.3724/sp.j.1004.2013.01831.

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Veraguas, Julio Backhoff, A. Max Reppen und Ludovic Tangpi. „Stochastic Control of Optimized Certainty Equivalents“. SIAM Journal on Financial Mathematics 13, Nr. 3 (28.07.2022): 745–72. http://dx.doi.org/10.1137/21m1407732.

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Wang, Ning, Mohammed Abouheaf, Wail Gueaieb und Nabil Nahas. „Model-Free Optimized Tracking Control Heuristic“. Robotics 9, Nr. 3 (29.06.2020): 49. http://dx.doi.org/10.3390/robotics9030049.

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Many tracking control solutions proposed in the literature rely on various forms of tracking error signals at the expense of possibly overlooking other dynamic criteria, such as optimizing the control effort, overshoot, and settling time, for example. In this article, a model-free control architectural framework is presented to track reference signals while optimizing other criteria as per the designer’s preference. The control architecture is model-free in the sense that the plant’s dynamics do not have to be known in advance. To this end, we propose and compare four tracking control algorithms which synergistically integrate a few machine learning tools to compromise between tracking a reference signal and optimizing a user-defined dynamic cost function. This is accomplished via two orchestrated control loops, one for tracking and one for optimization. Two control algorithms are designed and compared for the tracking loop. The first is based on reinforcement learning while the second is based on nonlinear threshold accepting technique. The optimization control loop is implemented using an artificial neural network. Each controller is trained offline before being integrated in the aggregate control system. Simulation results of three scenarios with various complexities demonstrated the effectiveness of the proposed control schemes in forcing the tracking error to converge while minimizing a pre-defined system-wide objective function.

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Lafont, F., und J. F. Balmat. „Optimized fuzzy control of a greenhouse“. Fuzzy Sets and Systems 128, Nr. 1 (Mai 2002): 47–59. http://dx.doi.org/10.1016/s0165-0114(01)00182-8.

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15

Nagy, Endre. „Adaptive control through optimized trajectory tracking“. IFAC Proceedings Volumes 37, Nr. 12 (August 2004): 379–84. http://dx.doi.org/10.1016/s1474-6670(17)31498-2.

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16

Ralston, Patricia A. S., Keith R. Watson, Ashutosh A. Patwardhan und Pradeep B. Deshpande. „A computer algorithm for optimized control“. Industrial & Engineering Chemistry Process Design and Development 24, Nr. 4 (Oktober 1985): 1132–36. http://dx.doi.org/10.1021/i200031a039.

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17

Mettin, Robert, und Thomas Kurz. „Optimized periodic control of chaotic systems“. Physics Letters A 206, Nr. 5-6 (Oktober 1995): 331–39. http://dx.doi.org/10.1016/0375-9601(95)00644-i.

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18

Hodgson, Clague P., und Renee Z. Fisk. „Hybridization probe size control: optimized ‘oligolabelling’“. Nucleic Acids Research 15, Nr. 15 (1987): 6295. http://dx.doi.org/10.1093/nar/15.15.6295.

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19

Martín-Sánchez, Juan M, João M Lemos und José Rodellar. „Survey of industrial optimized adaptive control“. International Journal of Adaptive Control and Signal Processing 26, Nr. 10 (24.07.2012): 881–918. http://dx.doi.org/10.1002/acs.2313.

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20

Wang, Hao, Lixin Zhang, Xue Hu und Huan Wang. „Design and Optimization of Precision Fertilization Control System Based on Hybrid Optimized Fractional-Order PID Algorithm“. Processes 11, Nr. 12 (05.12.2023): 3374. http://dx.doi.org/10.3390/pr11123374.

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In order to mitigate time-varying, lag, and nonlinearity impacts on fertilization systems and achieve precise control of liquid conductivity, we propose a novel hybrid-optimized fractional-order proportional-integral-derivative (PID) algorithm. This algorithm utilizes a fuzzy algorithm to tune the five parameters of the fractional-order PID algorithm, employs the Smith predictor for structural optimization, and utilizes Wild Horse Optimizer, improved by genetic algorithms, to optimize fuzzy rules. We conducted MATLAB simulations, precision experiments, and stability tests on this controller. MATLAB simulation results, along with precision experiment results, indicate that compared to PID controllers, Smith predictor-optimized PID controllers, and fuzzy-tuned fractional-order PID controllers, the proposed controller has the narrowest steady-state conductivity range, the shortest settling time, and the lowest overshoot, showcasing excellent overall dynamic performance. Stability test results demonstrate that the controller maintains stable operation under different pressure conditions. Therefore, this control system from our study achieves superior control effectiveness, providing a viable approach for the control of nonlinear time-delay systems.

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Komijani, Hossein, Mojtaba Masoumnezhad, Morteza Mohammadi Zanjireh und Mahdi Mir. „Robust Hybrid Fractional Order Proportional Derivative Sliding Mode Controller for Robot Manipulator Based on Extended Grey Wolf Optimizer“. Robotica 38, Nr. 4 (13.06.2019): 605–16. http://dx.doi.org/10.1017/s0263574719000882.

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SUMMARYThis paper presents a novel robust hybrid fractional order proportional derivative sliding mode controller (HFOPDSMC) for 2-degree of freedom (2-DOF) robot manipulator based on extended grey wolf optimizer (EGWO). Sliding mode controller (SMC) is remarkably robust against the uncertainties and external disturbances and shows the valuable properties of accuracy. In this paper, a new fractional order sliding surface (FOSS) is defined. Integrating the fractional order proportional derivative controller (FOPDC) and a new sliding mode controller (FOSMC), a novel robust controller based on HFOPDSMC is proposed. The bounded model uncertainties are considered in the dynamics of the robot, and then the robustness of the controller is verified. The Lyapunov theory is utilized in order to show the stability of the proposed controller. In this paper, the EGWO is developed by adding the emphasis coefficients to the typical grey wolf optimizer (GWO). The GWO and EGWO, then, are applied to optimize the proposed control parameters which result in the optimized GWO-HFOPDSMC and EGWO-HFOPDSMC, respectively. The effectivenesses of the optimized controllers (GWO-HFOPDSMC and EGWO-HFOPDSMC) are completely verified by comparing the simulation results of the optimized controllers with the typical FOSMC and HFOPDSMC.

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Tsuchiya, T., und T. Egami. „Efficiency Optimized Speed Control - Application of Optimal Control and Adaptive Control“. IFAC Proceedings Volumes 20, Nr. 5 (Juli 1987): 351–56. http://dx.doi.org/10.1016/s1474-6670(17)55395-1.

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Zhang, Daifeng, Haibin Duan und Yijun Yang. „Active disturbance rejection control for small unmanned helicopters via Levy flight-based pigeon-inspired optimization“. Aircraft Engineering and Aerospace Technology 89, Nr. 6 (02.10.2017): 946–52. http://dx.doi.org/10.1108/aeat-05-2016-0065.

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Purpose The purpose of this paper is to propose a control approach for small unmanned helicopters, and a novel swarm intelligence algorithm is used to optimize the parameters of the proposed controller. Design/methodology/approach Small unmanned helicopters have many advantages over other unmanned aerial vehicles. However, the manual operation process is difficult because the model is always instable and coupling. In this paper, a novel optimized active disturbance rejection control (ADRC) approach is presented for small unmanned helicopters. First, a linear attitude model is built in hovering condition according to small perturbation linearization. To realize decoupling, this model is divided into two parts, and each part is equipped with an ADRC controller. Finally, a novel Levy flight-based pigeon-inspired optimization (LFPIO) algorithm is developed to find the optimal ADRC parameters and enhance the performance of controller. Findings This paper applies ADRC method to the attitude control of small unmanned helicopters so that it can be implemented in practical flight under complex environments. Besides, a novel LFPIO algorithm is proposed to optimize the parameters of ADRC and is proved to be more efficient than other homogenous methods. Research limitations/implications The model of proposed controller is built in the hovering action, whereas it cannot be used in other flight modes. Practical implications The optimized ADRC method can be implemented in actual flight, and the proposed LFPIO algorithm can be developed in other practical optimization problems. Originality/value ADRC method can enhance the response and robustness of unmanned helicopters which make it valuable in actual environments. The proposed LFPIO algorithm is proved to be an effective swarm intelligence optimizer, and it is convenient and valuable to apply it in other optimized systems.

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Komatsu, Wilson, Cláudio José de Oliveira Júnior und Paulo Sérgio Valle Carvalho. „Direct Water Heater Power Control For Reduced Harmonics And Flicker Content With Optimized Half-cycle Power Control“. Eletrônica de Potência 11, Nr. 3 (01.11.2006): 175–80. http://dx.doi.org/10.18618/rep.2006.3.175180.

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25

Ratheesh, S., und Jeba Vins. „Control of self-excited induction generator based wind turbine using current and voltage control approaches“. Al-Qadisiyah Journal for Engineering Sciences 16, Nr. 3 (30.10.2023): 209–17. http://dx.doi.org/10.30772/qjes.2023.143509.1033.

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The self-excited induction generator (SEIG) is widely applied in the wind energy conversion system (WECS) to enhance power generation. The power generation from WECS is varied in terms of varying wind speed. Hence, to improve the working of SEIG-based WECS, the multi-stage coati optimized proportional integral (CPI) fractional order proportional integral derivative (FOPID) controller is proposed in this work. The proposed coati optimized proportional integral - fractional order proportional integral derivative controls the SEIG’s grid side converter and generator side converter. The coati optimization algorithm optimizes the controller parameters of a proposed multi-state controller. The proposed CPI-FOPID controls both the voltage at the grid side converter and the current at a generator side converter. Moreover, the pitch angle of the wind turbine (WT) is controlled by the fuzzy-based tilt integral derivative (F-TID) controller. The proposed work will be implemented on the Matlab/Simulink platform, and the THD of 0.63% has proven the efficacy of the proposed methodology.

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K, Renuka. „An Optimized Intelligent Transport System to Control Traffic in Internet of Vehicles“. Journal of Advanced Research in Dynamical and Control Systems 51, SP3 (28.02.2020): 420–31. http://dx.doi.org/10.5373/jardcs/v12sp3/20201277.

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Fiedler, M., A. Plozner, B. Rutzinger und W. Scherleitner. „Control of mechanical properties of high-strength steels through optimized welding processes“. Paton Welding Journal 2016, Nr. 7 (28.07.2016): 32–36. http://dx.doi.org/10.15407/tpwj2016.07.06.

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Qi, Ben Sheng, Kang Wang, Xuan Xuan Xiao und Hong Xia Miao. „Design and Implementation of Self-Balancing Electric Vehicle Control System“. Applied Mechanics and Materials 738-739 (März 2015): 950–54. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.950.

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In order to further optimize the control system of self-balancing electric vehicle, the method of linear quadratic regulator (LQR) based on genetic algorithm (GA) was presented in this paper. Firstly, the mathematical model of self-balancing electric vehicle was established by Lagrange equation, and then matrix Q and R in LQR which is used to control self-balancing electric vehicle system were optimized by GA. Thus the optimal control of self-balancing electric vehicle control system was realized. The optimization method was proved to be effective by comparing the simulation results of the optimized controller with the original.

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Luo, Rongfu, Zenghui Wang und Yanxia Sun. „Optimized Luenberger Observer-Based PMSM Sensorless Control by PSO“. Modelling and Simulation in Engineering 2022 (22.08.2022): 1–17. http://dx.doi.org/10.1155/2022/3328719.

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In the real applications, we found that it is difficult to achieve good control performance through manually tuning proportional–integral (PI) parameters of phase locked loop (PLL) and speed-loop of Luenberger observer (LO) for the PMSM sensorless control system. Therefore, this paper is to use the particle swarm optimization (PSO) algorithm to optimize the PI parameters of PLL and speed-loop of Luenberger observer of the system. Firstly, the ranges of PLL parameters are obtained by analyzing the PLL subsystem stability. Then, the ranges of PI parameters of PLL and speed-loop are set based on theoretical estimation and empirical values. The control system model is realized in MATLAB/Simulink that considers the constraints such as the saturation. The integral time absolute error is the objective function, and the PSO with different topologies is used to optimize the PI parameters. The simulation and experimental results show that the proposed method is feasible, and the optimized parameters can effectively improve the precision of position estimation and speed estimation. Moreover, the simulations and experiments are carried out to verify the robustness of the proposed method, and the results show that the optimized system can achieve good performance when there are uncertainties or disturbances.

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Zhang, Qi, Yaoxing Wei und Xiao Li. „Quadrotor Attitude Control by Fractional-Order Fuzzy Particle Swarm Optimization-Based Active Disturbance Rejection Control“. Applied Sciences 11, Nr. 24 (07.12.2021): 11583. http://dx.doi.org/10.3390/app112411583.

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In this paper, Active Disturbance Rejection Control (ADRC) is utilized in the attitude control of a quadrotor aircraft to address the problem of attitude destabilization in flight control caused by parameter uncertainties and external disturbances. Considering the difficulty of optimizing the parameter of ADRC, a fractional-order fuzzy particle swarm optimization (FOFPSO) algorithm is proposed to optimize the parameters of ADRC for quadrotor aircraft. Simultaneously, the simulation experiment is designed, which compares with the optimized performance of traditional particle swarm optimization (PSO), fuzzy article swarm optimization (FPSO) and adaptive genetic algorithm-particle swarm optimization (AGA-PSO). In addition, the turbulent wind field model is established to verify the disturbance rejection performance of the controller. Finally, the designed controller is deployed to the actual hardware platform by using the model-based design method. The results show that the controller has a small overshoot and stronger disturbance rejection ability after the parameters are optimized by the proposed algorithm.

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Wang, Yu Qin, Quan Sheng Jiang und Han Sheng Yang. „Optimization Design of CVT Electro-Hydraulic Servo System Based on H∞ Control“. Applied Mechanics and Materials 385-386 (August 2013): 823–26. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.823.

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In order to improve ride comfort and reduce the sense of frustration generated by the car when shifting, H robust control algorithm is proposed to optimize the design of CVT electro-hydraulic servo system. The CVT electro-hydraulic servo system is optimized by the development of system control model and the design of H∞ robust controller. The simulation result indicates that the systems uncertainties can be optimized effectively by the H∞ controller, while the systems anti-interference ability and robustness is improved obviously.

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Zhu, Huibin, Tao Huang, Lizhen Bai und Wenkai Zhang. „Optimizing Active Disturbance Rejection Control for a Stubble Breaking and Obstacle Avoiding Control System“. Agriculture 14, Nr. 5 (20.05.2024): 786. http://dx.doi.org/10.3390/agriculture14050786.

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In order to improve the obstacle avoidance control performance and anti-interference ability of a stubble breaking device of a no-tillage planter, a back-propagation neural network (BPNN)-optimized fuzzy active disturbance rejection control (ADRC) controller was designed to optimize the control performance of a servo motor. Firstly, a negative feedback mathematical model was established for the obstacle avoidance control system. Then, the nonlinear state error feedback (NLSEF) parameters in the fuzzy ADRC were intelligently optimized by the BPNN algorithm. In this way, a fuzzy ADRC controller based on BPNN optimization was formed to optimize the control process of a servo motor. Matlab/Simulink (R2022b) was used to complete the simulation model design and parameter adjustment. Consequently, the response time was 0.089 s using the BPNN fuzzy ADRC controller, which was shorter than the 0.303 s of the ADRC controller and the 0.100 s of the fuzzy ADRC controller. The overshoot was 0.1% using a BPNN fuzzy ADRC controller, which was less than the 2% of the ADRC controller and the 1% of the fuzzy ADRC controller. After noise signal interference was introduced into the control system, the regression steady state time of the BPNN fuzzy ADRC controller was 0.22 s, which was shorter than the 0.56 s of the ADRC controller and the 0.45 s of the fuzzy ADRC controller. A hardware-in-the-loop simulation experimental platform of the obstacle avoidance control system was constructed. The experiment results show that the servo motor control system has a fast dynamic response, small steady-state error and strong anti-interference ability for obstacle avoidance at the target height. Then, the control system error was within the allowable range. The servo motor control effect of the BPNN fuzzy ADRC was better than the ADRC and fuzzy ADRC. This optimized servo motor control method can provide a reference for improving the obstacle avoidance control effect problem of no-tillage seeders in stubble breaking operations on rocky desertification areas.

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Aparisi, Francisco, und Marco A. de Luna. „Synthetic- control charts optimized for in-control and out-of-control regions“. Computers & Operations Research 36, Nr. 12 (Dezember 2009): 3204–14. http://dx.doi.org/10.1016/j.cor.2009.02.024.

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34

Cheng, Xiao Hong, und Qi Li. „Optimization Control of the Ball Mill Mechanical Equipment“. Applied Mechanics and Materials 602-605 (August 2014): 1283–86. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.1283.

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The current system for thermal power system operation stability ball mill, poor uniformity of pulverized coal that often occurs overpressure, breaking coal, over-temperature phenomena such as coal or blocking rational analysis, the use of predictive control, self-optimizing control the principle of the system is to optimize the steel mill, and combined with its control system elaborated feasibility optimized implementation.

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Lim, Ingyu, und Kira L. Barton. „Pareto iterative learning control: Optimized control for multiple performance objectives“. Control Engineering Practice 26 (Mai 2014): 125–35. http://dx.doi.org/10.1016/j.conengprac.2014.01.011.

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36

Zhang, Jing Jun, Xiao Pin Guo, Li Li He und Rui Zhen Gao. „Study of Fuzzy Control for Intelligent Cantilever Beam Based on Genetic Algorithm“. Advanced Materials Research 204-210 (Februar 2011): 25–30. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.25.

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The design of fuzzy controller is the key of fuzzy control system, while the core of fuzzy controller design lies in fuzzy rules, whose performance determines the control effect of fuzzy system. General fuzzy rules are obtained from expert experience, in which much subjectivity exists. In this paper, a fuzzy controller is designed by taking an intelligent cantilever beam as the research object. And a method using the genetic algorithm to optimize fuzzy rules is proposed and the genetic coding as well as the fitness function is confirmed. Finally, the simulation model of intelligent cantilever beam is built by Matlab/Simulink, and the vibration control effects of fuzzy controller optimized by genetic algorithm are compared with those un-optimized. The simulation results indicate that the vibration amplitude of intelligent cantilever beam has a significant decrease and the vibration decay rate has a significant increase after the fuzzy rules optimized.

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Kong, Zhi Guo, Hong Wei Zhang und Zi Ning Tang. „Coordinated Control for Novel Full Hybrid Vehicles“. Advanced Materials Research 860-863 (Dezember 2013): 1073–77. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.1073.

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In order to improve the performance of a new type of full hybrid electric bus, this paper puts forward a set of coordinated control method to adjust the operation of the engine and two motors. In the engine start-stop logic control, comprehensive consideration of SOC, the speed of the bus and the accelerator pedal stroke are performed, while hysteresis control is introduced to improve the stability of the control; In the engine working point adjusting control, not only the engine speed command rate of change was optimized, but also the output torque rate was optimized to match the air injection and exhaust, etc. Further, the method based on dynamic constraints was used to optimize the working point adjustment process. At present, there are hundreds of busses operates in route. Results verify the feasibility and effectiveness of the control method. The vehicle has good fuel economy, and the dynamic performance and driving comfort are also greatly improved.

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Jian, Fang, und Jun Yi Liu. „PSO in Two-Wheeled Self-Balancing Robot Control Research“. Advanced Materials Research 898 (Februar 2014): 534–37. http://dx.doi.org/10.4028/www.scientific.net/amr.898.534.

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Two self-balancing robot is a nonlinear, multivariable, intrinsically unstable motion control system, For traditional LQR controller feedback matrix optimization problem is difficult to determine, Particle swarm optimization algorithm to optimize the LQR controller feedback matrix, The simulation results show, Utilizing particle swarm optimized LQR controller improves the stability of the system, reducing the system overshoot and oscillation frequency.

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Kralj, Predrag, Dragan Martinović, Mato Tudor und Davor Lenac. „Optimized Marine Fresh Water Generator Control System“. Naše more 68, Nr. 1 (Februar 2021): 28–34. http://dx.doi.org/10.17818/nm/2021/1.3.

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The introductory part of this paper offers an overview of approaches to the management of marine fresh water generator and points out the most important factors influencing the processes. The second part deals with operation time and total overhaul time both influenced by evaporation intensity. Comparison and simple calculation are made of several distinct modes of operation assuming days in voyage and hours necessary for overhaul. Next section gives a thermal analysis of the processes, resulting in energy balance equation that could be used as a base for the optimal control system for one single-stage unit. In fact, the suggested control system is based on the energy balance equation that is given in this section. In the fourth part the analysis of the most important operational values is made. The changes recorded on the six-channel plotter are used to evaluate the suggested control system. The paper gives an example of optimal control system for the single stage fresh water generator and suggests further research.

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Moore, Doug. „Maneuver load control using optimized feedforward commands“. Journal of Aircraft 32, Nr. 1 (Januar 1995): 206–7. http://dx.doi.org/10.2514/3.46703.

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Kolmanovsky, Ilya V., und Dimitar P. Filev. „Terrain and Traffic Optimized Vehicle Speed Control“. IFAC Proceedings Volumes 43, Nr. 7 (Juli 2010): 378–83. http://dx.doi.org/10.3182/20100712-3-de-2013.00079.

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42

Azcoïtia, Ch, und Ayatollah Karimi. „Soft Magnetic Materials for Optimized Vibration Control“. Materials Science Forum 373-376 (August 2001): 765–68. http://dx.doi.org/10.4028/www.scientific.net/msf.373-376.765.

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43

Zhou, Yong-chao, und Tian Li. „Optimized operation plan for sewer sediment control“. Journal of Zhejiang University-SCIENCE A 11, Nr. 5 (Mai 2010): 335–41. http://dx.doi.org/10.1631/jzus.a0900082.

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44

Garcia, Cristian F., Cesar A. Silva, Jose R. Rodriguez, Pericle Zanchetta und Shafiq A. Odhano. „Modulated Model-Predictive Control With Optimized Overmodulation“. IEEE Journal of Emerging and Selected Topics in Power Electronics 7, Nr. 1 (März 2019): 404–13. http://dx.doi.org/10.1109/jestpe.2018.2828198.

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45

Tarek, Zahraa, Mohammed AL-Rahmawy und Ahmed Tolba. „Fog computing for optimized traffic control strategy“. Journal of Intelligent & Fuzzy Systems 36, Nr. 2 (16.03.2019): 1401–15. http://dx.doi.org/10.3233/jifs-18077.

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46

Guthke, R., M. Pfaff und F. Meyer. „Optimized Fuzzy Rule Generation for Fermentation Control“. IFAC Proceedings Volumes 31, Nr. 8 (Mai 1998): 277–82. http://dx.doi.org/10.1016/s1474-6670(17)40198-4.

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47

Janschek, Klaus. „Optimized system performances through balanced control strategies“. Mechatronics 18, Nr. 5-6 (Juni 2008): 262–63. http://dx.doi.org/10.1016/j.mechatronics.2008.04.003.

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Ali, Safdar, und Do-Hyeun Kim. „Optimized Power Control Methodology Using Genetic Algorithm“. Wireless Personal Communications 83, Nr. 1 (17.02.2015): 493–505. http://dx.doi.org/10.1007/s11277-015-2405-3.

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49

Ohtsuki, Yukiyoshi, und Tomotaro Namba. „Locally optimized control pulses with nonlinear interactions“. Journal of Chemical Physics 151, Nr. 16 (28.10.2019): 164107. http://dx.doi.org/10.1063/1.5127563.

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50

Abaspour, Alireza, Seyed Hossein Sadati und Mohammad Sadeghi. „Nonlinear optimized adaptive trajectory control of helicopter“. Control Theory and Technology 13, Nr. 4 (November 2015): 297–310. http://dx.doi.org/10.1007/s11768-015-4062-1.

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