In this paper, the adaptive iterative learning control problem of a rigid-flexible coupled manipulator system with variable tracking trajectories is investigated. The dynamics model of the rigid-flexible coupled manip...
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In this paper, a reference model based sliding mode controller for the task of platoon control is developed. Among its advantages are: maintaining a safe distance between the vehicles, rapidly achieving the maximum al...
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ISBN:
(数字)9798350362343
ISBN:
(纸本)9798350362350
In this paper, a reference model based sliding mode controller for the task of platoon control is developed. Among its advantages are: maintaining a safe distance between the vehicles, rapidly achieving the maximum allowable speed, and arriving at the exact desired position in minimum time. The approach has no limits on the platoon size, moreover only local information is used. Each vehicle only needs to monitor the distance and relative speed to its predecessor. This makes the solution easily scalable. The properties are demonstrated analytically and verified in numerical simulations.
The Perspective-n-Point (PnP) problem is a fundamental challenge in engineering that plays a crucial role in fields such as computer vision and augmented reality. This problem aims to estimate the position and orienta...
With the continuous development of mobile Internet technology, its demand for network performance is also increasing. Although the traditional multi-level structure of network resources enhances the scalability and in...
With the continuous development of mobile Internet technology, its demand for network performance is also increasing. Although the traditional multi-level structure of network resources enhances the scalability and independence of the network, it cannot be used effectively, nor can it intelligently configure network resources. To this end, this paper explores a new network resource allocation method, namely, a machine learning-based large-scale communication network resource allocation method. This paper first explains the concepts of communication network resource allocation and machine learning, and then proposes the principle of communication network resource allocation based on machine learning. Finally, through experiments, this paper analyzes that the bandwidth utilization rate, data delay and packet loss rate of the communication network resource allocation based on the method in this paper are 78%, 5ms and 0.05%, respectively, and the comprehensive performance is better than other communication network resource allocation methods.
The confrontation between UAV swarms will be an important combat style in the future, and a reasonable division of such a target air fleet is instructive to the deployment of our own resources. In this paper, we combi...
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Working performances of the components made out of 49Fe-49Co-2V alloy are closely related to the surface integrity of the drilled holes,which are influenced remarkably by the cooling *** present study focuses on the s...
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Working performances of the components made out of 49Fe-49Co-2V alloy are closely related to the surface integrity of the drilled holes,which are influenced remarkably by the cooling *** present study focuses on the surface integrity differences between wet and dry drilled 49Fe-49Co-2V alloy *** drilled hole surface roughness and topographies,metallurgical and mechanical properties,and the exit characterizations were obtained using optical microscopy(OM),scanning electron microscopy(SEM),electron backscatter diffraction microscopy(EBSD),transmission electron microscopy(TEM),energy dispersive spectroscopy(EDS)and Vickers hardness techniques,*** effects of cooling conditions on the surface integrity were concluded and the influence mechanisms were analyzed based on the force and temperature differences in drilling process with different cooling *** is found that the surface roughness and the thickness of refined-grain region of the dry drilled holes are larger than those of wet drilled holes;work hardening induced by wet drilling is more serious than dry drilling;chippings occurred in the exits of the wet drilled holes due to the material brittleness,which could be avoided by dry *** surface integrity differences of wet and dry drilled holes are closely related to the force and temperature differences in drilling process with different cooling conditions.
In recent years, there has been a considerable amount of research conducted on the topic of road damage detection using deep learning techniques, with the aim of supporting the safe driving of mobility vehicles. To da...
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Sliding mode control strategies are useful for counteracting unpredictable disturbance affecting the plant. However, they are not devoid of shortcomings, as they typically require full state information to calculate t...
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ISBN:
(数字)9798350362343
ISBN:
(纸本)9798350362350
Sliding mode control strategies are useful for counteracting unpredictable disturbance affecting the plant. However, they are not devoid of shortcomings, as they typically require full state information to calculate the control signal. Moreover, such strategies are typically focused on the evolution of a virtual sliding variable and provide only indirect information about the actual state of the plant. Motivated by these issues, in this paper a new approach to sliding mode control of discrete time systems is proposed. This approach only requires information about the output of the plant to synthesize a robust control strategy. Furthermore, it is shown to guarantee boundedness of this output while the system is operating in the sliding phase.
Safety is of utmost importance when utilizing mobile vehicles. The introduction of safe driver assistance and automated driving systems will not only solve this problem, but also reduce road accidents and make driving...
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In the process of fault detection and classification,the operation mode usually drifts over time,which brings great challenges to the *** traditional machine learning based fault classification cannot dynamically upda...
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In the process of fault detection and classification,the operation mode usually drifts over time,which brings great challenges to the *** traditional machine learning based fault classification cannot dynamically update the trained model according to the probability distribution of the testing dataset,the accuracy of these traditional methods usually drops significantly in the case of covariate *** this paper,an importance-weighted transfer learning method is proposed for fault classification in the nonlinear multi-mode industrial *** effectively alters the drift between the training and testing ***,the mutual information method is utilized to perform feature selection on the original data,and a number of characteristic parameters associated with fault classification are selected according to their mutual ***,the importance-weighted least-squares probabilistic classifier(IWLSPC)is utilized for binary fault detection and multi-fault classification in covariate ***,the Tennessee Eastman(TE)benchmark is carried out to confirm the effectiveness of the proposed *** experimental result shows that the covariate shift adaptation based on importance-weight sampling is superior to the traditional machine learning fault classification ***,IWLSPC can not only be used for binary fault classification,but also can be applied to the multi-classification target in the process of fault diagnosis.
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