Advanced Control for Applications最新文献

As power quality disturbances become increasingly complex, it is imperative that the speed and accuracy of the inspection robot at the converter station be improved. For this purpose, this study designs a power quality disturbance feature extraction method based on the fast-adaptive S-transform. This method preserves the main feature information and eliminates redundant calculations on the basis of adaptive transform. On this basis, a power quality disturbance identification model built on a multi label lightweight gradient elevator is constructed. In the experimental results, compared to the generalized S-transform, adaptive S-transform, and S-transform, the total extraction time of the proposed method was reduced by 96.09%, 91.56%, and 91.22%, respectively. The average accuracy of extracting features for a single disturbance was 99.56%, while for complex disturbance, it was 98.24%, both of which outperformed the comparison algorithms. It is verified that the proposed method can improve the extraction accuracy of power quality disturbance signal. In the recognition of a single disturbance signal, the constructed model exhibited a high accuracy of over 99%. In recognizing composite disturbance signals, the model demonstrated high accuracy and strong stability. Its effectiveness has been confirmed through experiments. The paper aims to enhance the speed and accuracy of power quality disturbance recognition algorithms. This will assist inspection robots working in converter stations, and ensure stable and safe operation of the power grid.

The traditional data mining model of indentation has low accuracy in analyzing the linear relationship between the relevant physical quantities of the indentation, so a deep mining model for indentation data of biomaterials is designed. Firstly, the constitutive relation of the material is set by the actual indentation, and the dimension data are collected by the independent free variable function. The characteristic Raman peak is obtained according to the properties of the biological nanomaterials. The stress data are preprocessed by selecting the direction of indentation, which is convenient to observe the dislocation nucleation and deformation twin phenomenon in the process of indenting. The synergistic effect of these dislocations leads to the fact that the load displacement curve shows obvious linear relationship, so as to complete the analysis of the deep mining model of the indentation data of biological nanomaterials. The experimental results show that in the linear relationship analysis of contact depth and indentation depth, the linear relationship discreteness of the designed model is 0.44 lower than that of the traditional model and in the linear relationship analysis of contact stiffness and indentation depth, the linear relationship discreteness of the designed model is 0.38 lower than that of the traditional model, which indicates that the accuracy of the designed model is higher than that of the traditional model in analyzing the linear relationship between the relevant physical quantities of the indentation. In addition, the average accuracy of the model for five different materials is 98.23%.

The current lack of effective means for information technology to penetrate into the intelligent manufacturing process of products has led to its disconnection from the core links of industrial systems and low efficiency. Therefore, a nonstandard mechanical product lifecycle model and an industrial internet platform data access mechanism have been proposed using blockchain technology, and an industrial internet platform has been constructed based on the two, which has been experimentally verified. The experimental results showed that the calculation methods in the model could overall reduce the average storage of abnormal data in blockchain products by 53.6%. In addition, the maximum time cost of scalar multiplication did not exceed 20 ms, which was lower than that of bi-linear pairing. In the comparison of different schemes, the highest encryption computation cost of the research scheme did not exceed 100 ms, and the highest decryption computation cost did not exceed 200 ms, which was generally lower than the comparison scheme. At the same time, the max, mini, and average time consumption of encrypting order data during decryption operations were 5.74, 1.10, and 2.77 s, respectively. When the simulation request reached 2000, there were only a few exceptions on the platform server, indicating that it could support at least 2000 simultaneous requests from applications. Overall, the industrial internet process management platform constructed through research has high practicality and can meet the actual needs of intelligent manufacturing enterprises.

The paper considers the application of feedback control to orbital transfer maneuvers subject to constraints on the spacecraft thrust and on avoiding the collision with the primary body. Incremental reference governor (IRG) strategies are developed to complement the nominal Lyapunov controller, derived based on Gauss variational equations, and enforce the constraints. Simulation results are reported that demonstrate the successful constrained orbital transfer maneuvers with the proposed approach. A Lyapunov function based IRG and a prediction-based IRG are compared. While both implementation successfully enforce the constraints, a prediction-based IRG is shown to result in faster maneuvers.

With the development of market economy, cold chain logistics has become the mainstream of the current transportation industry. Reducing transportation costs and optimizing transportation routes from an environmentally friendly perspective is the main research focus. This study starts with an emphasis on environmental protection and cost savings and optimizes existing cold chain logistics expenses. Using the clustering and annealing algorithms, the path optimization model with the lowest cost is constructed and analyzed. The K-means algorithm is utilized to cluster and partition logistics areas, and then optimized simulated annealing algorithm is used to control and utilize logistics costs and resources. The experimental results show that the optimized algorithm reduces costs by 11.36% and increases the loading rate of the vehicle by 11.95%. The delivery time has been reduced by 18.1%. The two-stage algorithm can optimize and improve the path model, reduce transportation costs, improve cold chain transportation efficiency, and verify the feasibility of the model.

The combined cooling, heating, and power (CCHP) co-generation system is an alternative for developing sustainable energy systems. Inside a multi-energy CCHP microgrid, electric, heat, and cool demands are supplied with a high efficiency. Integrating various energy conversion technologies and storage systems allows managing different resources and taking advantage of electric market participation. However, the uncertainties associated with source demand and prices should be taken into account. In this regard, this paper proposes stochastic programming to optimize the operation cost and emission penalty of a multi-energy CCHP microgrid considering the mathematical model of components and related uncertainties. Using the proposed optimization problem, the system operator can derive bidding/offering curves in the electric market. To mitigate the financial risks, the conditional value-at-risk (CVaR) approach is integrated to provide different risk-averse strategies. From the results, it is found that under the risk-averse strategy, by paying 0.4% more money, the risk of CCHP's operation cost instability will be reduced by approximately 16.82%.

Nowadays, controlling industrial processes and choosing the proper method for this purpose is very important. DC–DC converters are widely used in industrial applications, such as DC–DC step-up converters of 150 and 250 V in power drives and power supplies of portable electrical and hybrid vehicles. A predictive control algorithm is a method to deal with such complex processes. Many methods have been proposed as predictor control, leading to better and more accurate evolution with problems. This study proposes a fuzzy explicit predictive control method for adjusting the boost DC–DC converter. A fuzzy method for selecting the weights of the cost function of the predictive control algorithm of the DC–DC converter will be presented. In addition, for better evaluation and analysis, the designed controller is compared with similar methods, and the simulation results show that the controller designed on this system has had a proper performance with other methods in realizing the desired goals.

This article focuses on improving the control approach for a synchronous reluctance motor (SynRM) drive powered by a two-level pulse width modulation (PWM) inverter. While classical sliding mode control (SMC) has been extensively used in control system design, it comes with various drawbacks such as pronounced chattering effects, considerable transient state errors, and reduced robustness. These limitations hinder its practical applicability. To enhance the performance of the SynRM, this paper introduces a novel strategy that combines direct vector control (DVC) with advanced sliding mode control (ASMC), here referring to third-order sliding mode command (TOSMC), for regulating speed and dq-axis stator currents. The primary objective of this approach is to achieve precise and efficient control while minimizing total harmonic distortion (THD) in current and reducing output torque fluctuations. Notably, this strategy capitalizes on the strengths of TOSMC and DVC. The efficacy of the proposed control scheme is verified through two sets of thorough simulations realized in MATLAB/Simulink environment. The first set of simulations encompasses the load–torque test, where the motor is subjected to two different levels of load torque. The results from these tests showcase the control scheme's performance under varying load conditions. The second set of simulations involves the speed variation test, where intentional changes are applied to the motor's speed. This test assesses the control approach's ability to handle dynamic speed changes effectively. The proposed control strategy is further compared with conventional control methods, including proportional–integral and second-order sliding mode command (SOSMC) controls. The results consistently demonstrate the superior performance of the novel approach in terms of accurate control, robustness, and overall stability. The combination of DVC and TOSMC offers a promising avenue for achieving enhanced motor control in the presence of load disturbances and speed variations.

A fault diagnosis and localization approach for distributed distribution networks is created using an upgraded quantum genetic algorithm to swiftly identify and detect flaws in the network. In this method, the dynamic rotation strategy in gradient descent method is used to update the quantum gate to enhance the convergence speed, that is, the gradient descent quantum genetic algorithm is constructed. The results of single fault and multiple fault simulation test on the distribution network model of regional node of distributed power supply show that the average iteration of gradient descent quantum genetic algorithm 85.36, 86.35, 88.24, and 88.69 times can reach the target optimal value. In four different cases, the algorithm of gradient descent quantum genetic algorithm can reach the optimal by iterating 88, 91, 92, and 90 times, respectively. Compared with other algorithms, the convergence rate of gradient descent quantum genetic algorithm is the fastest in the four experimental cases. The consistency between the output score and the real score of the gradient descent quantum genetic algorithm is above 0.9. The results above show that the algorithm is effective. The optimization ability and stability of the algorithm are also stronger, and it has certain application potential.

This article addresses a trajectory tracking control problem concerning an autonomous underwater vehicle's pitch and yaw channel dynamics in the presence of model uncertainties, underwater disturbances, and input saturation. Three different observers are introduced to estimate unknown state variables: a Luenberger-type cubic observer, a sliding mode observer, and a high-gain observer (HGO). Initially, a backstepping controller is employed to tackle the tracking problem, extending it to incorporate backstepping sliding mode control (SMC). The mentioned observers are utilized in both aspects of the controller design. Our proposed control law assesses trajectory tracking performance by introducing virtual control inputs, with the sliding surface designed to guide the current state variables toward approximating the virtual state variables. By combining backstepping and SMC, ensure that the state variables of the closed-loop system converge to the desired state using the HGO. A rigorous analysis is incorporated to validate the robust performance of our proposed control law under conditions of model uncertainties and underwater disturbances. Furthermore, the control law is extended for anti-windup compensation, mitigating adverse effects on stern and rudder plane saturation levels. Lyapunov stability theory is adopted to establish the stability of the closed-loop system. Our simulation results convincingly demonstrate the effectiveness of the HGO-based backstepping SMC law compared to alternative control approaches.