The calculation of a linear quadratic regulator (LQR) control weighting matrix determines whether it can achieve optimal control. In order to solve the problem of complex calculation and low efficiency of this matrix,...
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The calculation of a linear quadratic regulator (LQR) control weighting matrix determines whether it can achieve optimal control. In order to solve the problem of complex calculation and low efficiency of this matrix, this paper proposed geneticalgorithm-particleswarm optimizatio algorithm (GAPSO) combining geneticalgorithm and particleswarmalgorithm to solve the weighting matrix of LQR clutch oil pressure control strategy, so as to control the design efficiency and precision of the strategy. Subsequently, the shifting quality of a tractor was improved. A dynamic model of the shifting process was established using a comprehensive indicator that combined jerk and sliding friction work as a quadratic function. An LQR-weighted matrix calculated using GAPSO enabled optimal control strategy designing for clutch hydraulic pressure. Furthermore, the shift process from the 11th to 12th gear was analyzed via simulations during plowing operations. The simulation results show that the genetic operation of crossover and variation is introduced into PSO, and the optimization ability of PSO is improved effectively. Compared with the clutch oil pressure control strategy calculated by GAPSO with LQR weighted matrix and LQR clutch oil pressure control strategy calculated by PSO with LQR weighted matrix, the clutch heat load is slightly increased, the smoothness of shifting is greatly improved, and the shifting quality of tractors is improved. Overall, this study provides valuable insights for designing and computationally analyzing optimal clutch LQR control.
In this paper, a wind retrieval method based on genetic algorithm-particle swarm optimization (GA-PSO) for the coherent Doppler wind lidar (CDWL) is proposed. The algorithm incorporates an advanced optimization framew...
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In this paper, a wind retrieval method based on genetic algorithm-particle swarm optimization (GA-PSO) for the coherent Doppler wind lidar (CDWL) is proposed. The algorithm incorporates an advanced optimization framework that considers wind field spatial continuity, simultaneously enhancing retrieval accuracy and computational efficiency. Comprehensive validations of the GA-PSO algorithm are conducted using a 1.5 mu m all-fiber CDWL through ground-based and airborne experiments. In ground-based experiments, the GA-PSO algorithm extends the detection range by 20%similar to 30% compared with traditional methods. The validation against meteorological tower data demonstrates excellent agreement, with mean deviations better than 0.27 m/s for horizontal wind speed and 3.07 degrees for horizontal wind direction and corresponding RMSE values better than 0.36 m/s and 6.04 degrees, respectively. During high-altitude airborne experiments at 5.5 km, the GA-PSO algorithm recovers up to 31% more horizontal wind speed and direction information compared with traditional algorithms, demonstrating exceptional performance in low signal-to-noise ratio (SNR) conditions. Both simulation analysis and field experiments demonstrate that the GA-PSO algorithm achieves processing speeds comparable to traditional real-time methods, establishing its suitability for real-time, three-dimensional wind retrieval applications.
Spectrum sensing is a critical function in cognitive radio networks, enabling the identification of available frequency bands without interfering with primary users. To improve the effectiveness of energy detection, w...
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Spectrum sensing is a critical function in cognitive radio networks, enabling the identification of available frequency bands without interfering with primary users. To improve the effectiveness of energy detection, we propose an adaptive double-threshold method that dynamically adjusts the upper and lower thresholds based on the signal-to-noise ratio (SNR) of cognitive nodes. This research introduces a novel framework for determining the optimal weighting coefficients necessary for these threshold adjustments. Specifically, we present the hybrid Whale-Chimp optimizationalgorithm (WCOA), which ensures stable threshold adaptation, mitigates the sensitivity to minor coefficient fluctuations, and keeps thresholds within an optimal range. Furthermore, we integrate the adaptive double-threshold method with a hybrid detection approach combining Energy Detection and Maximum-Minimum Eigenvalue (MME), which is further fine-tuned using the proposed Innovative Hybrid Whale-Chimp algorithm. Our approach effectively addresses the limitations of conventional energy detection methods, particularly under low SNR conditions. Collaborative interactions among cognitive nodes enhance detection accuracy, leading to faster spectrum sensing and improved detection probabilities. The proposed method offers a reliable solution for efficient spectrum sensing while safeguarding the integrity of primary users.
Network slicing is an important technology for implementing ondemand networking based on the 5 G network architecture of SDN/NFV. By analyzing the main scenarios of 5 G, a network slice orchestration algorithm based o...
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Network slicing is an important technology for implementing ondemand networking based on the 5 G network architecture of SDN/NFV. By analyzing the main scenarios of 5 G, a network slice orchestration algorithm based on GA-PSO optimization under the SDN/NFV architecture is *** algorithm uses the particleswarmoptimizationalgorithm to quickly converge on the characteristics of the global optimal solution, and design the evaluation function of network slice performance. Moreover, the ability of geneticalgorithm to quickly search randomly is used to update and optimize the network slice, and the particleswarm is used to chase the local optimal solution and the global optimal solution to obtain the optimal network slice. Simulation experiment results show that the algorithm can realize the personalized creation of network slices in multi-service scenarios, give full play to the advantages of SDN's centralized control mode, and reduce network energy consumption while improving network resource utilization.
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