Efficient and high-precision identification of dynamic parameters is the basis of model-based robot control. Firstly, this paper designed the structure and control system of the developed lower extremity exoskeleton r...
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Efficient and high-precision identification of dynamic parameters is the basis of model-based robot control. Firstly, this paper designed the structure and control system of the developed lower extremity exoskeleton robot. The dynamics modeling of the exoskeleton robot is performed. The minimum parameter set of the identified parameters is determined. The dynamic model is linearized based on the parallel axis theory. Based on the beetle antennae search algorithm (BAS) and particle swarmoptimization (PSO), the beetle swarm optimization algorithm (BSO) was designed and applied to the identification of dynamic parameters. The update rule of each particle originates from BAS, and there is an individual's judgment on the environment space in each iteration. This method does not rely on the historical best solution in the PSO and the current global optimal solution of the individual particle, thereby reducing the number of iterations and improving the search speed and accuracy. Four groups of test functions with different characteristics were used to verify the performance of the proposed algorithm. Experimental results show that the BSO algorithm has a good balance between exploration and exploitation capabilities to promote the beetle to move to the global optimum. Besides, the test was carried out on the exoskeleton dynamics model. This method can obtain independent dynamic parameters and achieve ideal identification accuracy. The prediction result of torque based on the identification method is in good agreement with the ideal torque of the robot control.
Accurate and efficient canal water scheduling in irrigation districts is crucial for promoting agricultural watersaving policies, especially in the context of global climate instability. However, many studies neglecte...
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Accurate and efficient canal water scheduling in irrigation districts is crucial for promoting agricultural watersaving policies, especially in the context of global climate instability. However, many studies neglected the role of gates in scheduling, and there were serious water distribution fairness problems in the actual irrigation district management of the farmland upstream and downstream. In this research, a novel method was proposed by integrating the traditional planning model (Dynamic Programming) with the canal water distribution framework. The former facilitated the effective coordination of gates and canals at multi-levels, while the latter addressed water requirements under various optimization objectives. A total of 32 scheduling schemes were obtained by applying the model to Dagong irrigation district, and the novel beetle swarm optimization algorithm (BSO) and the mature Particle swarmoptimizationalgorithm (PSO) were used to solve the problem, respectively. The new model also obtained the following results while solving the problems. (1) The main canal's Theoretical Flow Rate (TFR) is the critical factor influencing both the time efficiency and water utilization of the water distribution schemes. (2) A generalizable scheduling scheme is developed based on the observed flow and time distribution patterns in the main and sub-main canals, though its accuracy remains limited. (3) The comparison between the computational results of the BSO and PSO verifies the applicability of the former in this field. In conclusion, the model proposed enhances the efficiency and availability of the irrigation water delivery, applicable to other irrigation districts facing similar water allocation challenges.
Accurate and stable wind speed prediction can alleviate the uncertain impacts of wind power generation caused by nonlinear characteristics of wind speed, and then improve the reliability of wind power. In this paper, ...
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Accurate and stable wind speed prediction can alleviate the uncertain impacts of wind power generation caused by nonlinear characteristics of wind speed, and then improve the reliability of wind power. In this paper, a hybrid model for wind speed prediction based on mode decomposition, parameter optimization and basic prediction model is proposed. First, the extreme-point symmetric mode decomposition (ESMD) is employed to adaptively decompose the denoised wind speed time series into sub-sequences with different frequencies. Second, a fractional-order beetleswarm opti-mization (FO-BSO) for parameter optimization of the Least squares support vector machine (LSSVM) is proposed. Through benchmark functions and non-parametric statistical test, the advantages of the FO-BSO in accuracy, stability and convergence speed are verified. Subsequently, the ESMD-FO-BSO-LSSVM prediction model is established, and three groups of wind speed datasets with different sampling locations and sampling frequencies are selected for simulation experiments. The results show that the coefficient of determination of 1-step prediction of the proposed model in three datasets are 0.9856, 0.9713, 0.9940, which has 2.43%, 3.38%, 3.08% average promotion than that of 7 comparative models. And the accuracy and stability of ESMD-FO-BSO-LSSVM model in multi-step wind speed prediction have also achieved better performance than 7 competitors.& COPY;2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).
The iteration times and learning efficiency of kernel incremental extreme learning machines are always affected by the redundant nodes. A hybrid deep kernel incremental extreme learning machine (DKIELM) based on the i...
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The iteration times and learning efficiency of kernel incremental extreme learning machines are always affected by the redundant nodes. A hybrid deep kernel incremental extreme learning machine (DKIELM) based on the improved coyote and beetleswarmoptimization methods was proposed in this paper. A hybrid intelligent optimizationalgorithm based on the improved coyote optimizationalgorithm (ICOA) and improved beetle swarm optimization algorithm (IBSOA) was proposed to optimize the parameters and determine the number of effectively hidden layer neurons for the proposed DKIELM. A Gaussian global best-growing operator was adopted to replace the original growing operator in the intelligent optimizationalgorithm to improve COA searching efficiency and convergence. In the meantime, IBSOA was designed based on tent mapping inverse learning and dynamic mutation strategies to avoid falling into a local optimum. The experimental results demonstrated the feasibility and effectiveness of the proposed DKIELM with encouraging performances compared with other ELMs.
Based on the high proportion of renewable energy connected to the active distribution network, this article studies the joint planning of demand-side response and energy storage. Firstly, a two-level optimization mode...
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Based on the high proportion of renewable energy connected to the active distribution network, this article studies the joint planning of demand-side response and energy storage. Firstly, a two-level optimization model is established for the planning of active distribution network. The upper level objective function is the investment, operation and maintenance cost of energy storage and fan, and the lower level objective function is the annual network loss cost. Then, the improved Longhorn algorithm is used to solve the two-layer programming model, and the combination of Longhorn beard algorithm and particle swarmoptimizationalgorithm not only improves the iteration speed, but also improves the global searching ability. Finally, the example analysis proves the effectiveness of joint planning to solve the high proportion of renewable energy, thus improving the economic benefits.
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