This paper introduces cross-entropy optimization method and Multi-mode Proportional-Integral-Derivative (Multi-mode PID) control algorithm. With features of simple control parameter setting, low computation complexity...
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ISBN:
(纸本)9789881563842
This paper introduces cross-entropy optimization method and Multi-mode Proportional-Integral-Derivative (Multi-mode PID) control algorithm. With features of simple control parameter setting, low computation complexity and high robustness, the cross-entropy optimization method is applied to iterative optimization in Multi-mode PID controllerparameters design. hi present paper, simulation investigations for an industrial process are carried out, and the feasibility, effectiveness and good performance of the proposed method is verified.
This paper introduces cross-entropy optimization method and Multi-mode Proportional-Integral-Derivative(Multi-mode PID) control *** features of simple control parameter setting,low computation complexity and high robu...
详细信息
ISBN:
(纸本)9781479947249
This paper introduces cross-entropy optimization method and Multi-mode Proportional-Integral-Derivative(Multi-mode PID) control *** features of simple control parameter setting,low computation complexity and high robustness,the cross-entropy optimization method is applied to iterative optimization in Multi-mode PID controllerparameters *** present paper,simulation investigations for an industrial process are carried out,and the feasibility,effectiveness and good performance of the proposed method is verified.
Autonomous flying robots (AFRs) have captured significant interest owing to their agile maneuverability, adaptability, and economical viability. However, the pursuit of enhancing their trajectory tracking performance ...
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Autonomous flying robots (AFRs) have captured significant interest owing to their agile maneuverability, adaptability, and economical viability. However, the pursuit of enhancing their trajectory tracking performance presents an ongoing challenge. In light of this, our work introduces an innovative strategy that integrates optimization metaheuristic algorithms with a robust hybrid control framework for AFRs, resulting in an optimized and robust controller tailored for autonomous quadrotor robots. By optimizing the controllerparameters, we aim to minimize the tracking error and improve the overall performance of AFRs. To evaluate our approach, this study comprehensively analyzes four metaheuristic algorithms in addition to the Improved Grey Wolf optimization (I-GWO) which outperforms others in quality, convergence rate, and robustness. The proposed I-GWO integration yields a tracking error of 23.25, surpassing Grey Wolf Optimizer (GWO) (24.36), Artificial Bee Colony (ABC) (29.63), and Sine Cosine Algorithm (SCA) (2481.56). The I-GWO has also achieved its minimum objective value within less than 20 iterations compared to other algorithms. Extensive simulations show that our framework achieves optimal and accurate trajectory tracking, critical for safe and efficient AFR operations in various applications. This study emphasizes the importance of choosing suitable optimization algorithms and provides a systematic method for tuning controller gains applicable to different AFR types and control problems. Our contributions could advance more reliable and advanced AFR development in areas such as agriculture, inspection, monitoring, and search and rescue operations. A supplemental animated simulation of this work is available at https://***/aJMq8ROW51g.
Among various technologies to tackle the twin challenges of sustainable energy supply and climate change, energy saving through advanced control plays a crucial role in decarbonizing the whole energy system. Modern co...
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Among various technologies to tackle the twin challenges of sustainable energy supply and climate change, energy saving through advanced control plays a crucial role in decarbonizing the whole energy system. Modern control technologies, such as optimal control and model predictive control do provide a framework to simultaneously regulate the system performance and limit control energy. However, few have been done so far to exploit the full potential of controller design in reducing the energy consumption while maintaining desirable system performance. This paper investigates the correlations between control energy consumption and system performance using two popular control approaches widely used in the industry, namely the PI control and subspace model predictive control. Our investigation shows that the controller design is a delicate synthesis procedure in achieving better trade-off between system performance and energy saving, and proper choice of values for the control parameters may potentially save a significant amount of energy.
Parametric optimization of flexible satellite controller is an essential for almost all modern satellites. Particle swarm algorithm is a global optimization algorithm but it suffers from two major shortcomings, that o...
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Parametric optimization of flexible satellite controller is an essential for almost all modern satellites. Particle swarm algorithm is a global optimization algorithm but it suffers from two major shortcomings, that of, premature convergence and low searching accuracy. To solve these problems, this paper proposes an improved particle swarm optimization (IPSO) which substitute "poorly-fitted-particles" with a cross operation. Based on decision possibility, the cross operation can interchange local optima between three particles. Thereafter the swarm is split in two halves, and random number (s) get generated by crossing the dimension of particle from both halves. This produces a new swarm. Now the new swarm and old swarm are mixed, and based on relative fitness a half of the particles are selected for the next generation. As a result of the cross operation, IPSO can easily jump out of local optima, has improved searching accuracy and accelerates the convergence speed. Some test functions with different dimensions are used to analyze the performance of IPSO algorithm. Simulation results show that the IPSO has more advantages than standard PSO and Genetic Algorithm PSO (GAPSO). In that it has a more stable performance and lower level of complexity. Thus the IPSO is applied for parametric optimization of flexible satellite control, for a satellite having solar wings and antennae. Simulation results shows that the IPSO can effectively get the best controllerparameters vis-a-vis the other optimization methods. (C) 2011 Elsevier Inc. All rights reserved.
In the manufacturing industry, the high acceleration demands lead to the lightweight designs of the stages, while it may cause undesirable vibrations. In order to obtain the most efficient vibration suppression perfor...
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In the manufacturing industry, the high acceleration demands lead to the lightweight designs of the stages, while it may cause undesirable vibrations. In order to obtain the most efficient vibration suppression performance, this paper proposes a co-optimization method to realize the simultaneous optimization of the Actuators and Sensors (AS) placement and the controllerparameters. The co-optimization method includes an inner cycle for minimizing the control energy by a Linear Quadratic Gaussian (LQG) controller, and an outer cycle for optimizing the closed-loop 1-norm is constructed as the criteria for the outer optimization cycle. Besides, the Dynamic Opposite Learning Enhanced Teaching-Learning-Based optimization (DOLTLBO) algorithm is applied to enhance the searching ability. Numerical experiments on a lightweight stage were performed to validate the co-optimization method. The results reveal that the new method has a strong ability in optimizing the AS placement and the controllerparameters, and can finally guarantee superior performances for vibration suppression.
In doubly-fed induction generator-based wind turbines (DFIG-WTs), the rotor-side controller (RSC) with optimized parameters improves wind energy utilization efficiency. With long optimization times and inadequate expl...
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In doubly-fed induction generator-based wind turbines (DFIG-WTs), the rotor-side controller (RSC) with optimized parameters improves wind energy utilization efficiency. With long optimization times and inadequate exploration and development capabilities, conventional intelligent optimization algorithms are hard to find the controllerparameters quickly in the complexity and nonlinearity of DFIG-WTs. A quantum-inspired parallel multi-layer Monte Carlo algorithm accelerated by transfer learning (QPMMCOA-TL) is proposed to shorten the optimization time of parameters and obtain the controllerparameters more satisfactorily simultaneously. The QPMMCOA-TL possesses strong optimization capabilities through an accelerated search method based on transfer learning, a diversified population coding way, a parallel multi-layer structure, way of searching in the narrowing feasible region. In the optimization process, the fitness function replaced by trained deep neural networks is transferred to the search process of the QPMMCOA-TL for shorting the optimization time. The QPMMCOA-TL is applied to test two benchmark functions and compared with seven metaheuristic algorithms for completing the validity verification. The optimization time of the QPMMCOA-TL when searching the parameters of the RSC is 1188 s, which is one-tenth or less than other algorithms. Furthermore, the reliability and stability of the optimized controller are comprehensively enhanced. (c) 2023 Elsevier B.V. All rights reserved.
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