Repetitive motion is an important operation in manufacturing processes and other applications. It is thus necessary to design control systems that can track repetitive-motion reference signals for dual-input-single-ou...
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ISBN:
(数字)9798350355369
ISBN:
(纸本)9798350355376
Repetitive motion is an important operation in manufacturing processes and other applications. It is thus necessary to design control systems that can track repetitive-motion reference signals for dual-input-single-output (DISO) systems. This paper proposes a control scheme to realize precise repetitive motion in DISO systems. The proposed design method utilizes adaptive feed-forward cancellation (AFC) to compensate the position error signal for the reference signal. This approach contributes to improving DISO system control performance. In addition, the control system can eliminate the negative impact due to the waterbed effect. The proposed control system was implemented in an experimental system, and the experimental results indicate the effectiveness of the proposed method.
In order to improve tracking performance of a class of linear discrete time-invariant multi-agent systems, an adaptive optimal iterative learning control strategy is designed. To design the control protocol, a paramet...
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ISBN:
(数字)9798350361674
ISBN:
(纸本)9798350361681
In order to improve tracking performance of a class of linear discrete time-invariant multi-agent systems, an adaptive optimal iterative learning control strategy is designed. To design the control protocol, a parameter adaptive algorithm is constructed to estimate the Markov parameters of the system. Then, virtual leader is used instead of expected trajectory. By optimizing the index function and incorporating estimated parameters into the learning process. An adaptive iterative learning optimal control strategy is constructed. Through rigorous analysis, the parameter estimation error is bounded, the tracking error is convergent, and the proof process is independent of system parameters. Finally, numerical simulations validate the effectiveness of this designed control strategy.
Snake robots, exhibiting a high degree of redundancy, are a class of biomimetic robots capable of forming a helical rolling gait for climbing trees and pipes. The surface structure of naturally grown trees is irregula...
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ISBN:
(数字)9798350364798
ISBN:
(纸本)9798350364804
Snake robots, exhibiting a high degree of redundancy, are a class of biomimetic robots capable of forming a helical rolling gait for climbing trees and pipes. The surface structure of naturally grown trees is irregular, which may hinder the snake robots from achieving optimal surface conformation. Additionally, the presence of numerous branching structures in pipes introduces unpredictability in pipe climbing. To address these challenges, we propose an adaptive climbing strategy for snake robots based on compliant control. By adjusting a pre-set elasticity coefficient, the snake robot modulates the output torque of each joint, thereby altering the target angle of the joint. This adjustment enables the snake robot to adapt to unstructured environments. We conducted an experiment on climbing curved pipes to verify the effectiveness of this method. The result demonstrate that under unknown conditions, the robustness of the snake robot’s climbing motion is enhanced by modifying the pre-set elasticity coefficient based on compliant control. This finding underscores the potential of compliant control in improving the adaptability and performance of Snake robots in complex environments.
In the k-committee election problem, we wish to aggregate the preferences of n agents over a set of alternatives and select a committee of k alternatives that minimizes the cost incurred by the agents. While we typica...
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Underwater communication is widely regarded as one of the most significant challenges due to the unique physical properties of water. Among the available communication methods, radiofrequency communication offers high...
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ISBN:
(数字)9798331534189
ISBN:
(纸本)9798331534196
Underwater communication is widely regarded as one of the most significant challenges due to the unique physical properties of water. Among the available communication methods, radiofrequency communication offers higher data transmission rates than acoustic communication. However, higher frequencies suffer from substantial attenuation in water, making it difficult to achieve long-distance transmission. Addressing the tradeoff between transmission rate and communication distance is essential for achieving optimal performance. Unlike conventional systems that rely on fixed operating frequencies, this study introduces a dynamic frequency selection algorithm. This algorithm dynamically adjusts the operating frequency in real-time based on channel conditions and transmission rate feedback, ensuring a more adaptive and efficient communication system. To validate this approach, a dipole antenna with multiple impedance matching was designed and tested in a water tank environment. The experimental results revealed that bandwidth performance varied across different frequencies. Transmission rate analysis showed that higher frequencies provided higher data transmission over short distances. Conversely, lower frequencies exhibited better signal stability and reliability for long-distance communication due to their reduced attenuation. These findings highlight the importance of adaptive frequency selection in underwater radiofrequency communication, where a balance between transmission rate and communication distance must be achieved to support various operational requirements.
In this paper, we propose an algorithm that can be used on top of a wide variety of self-supervised (SSL) approaches to take advantage of hierarchical structures that emerge during training. SSL approaches typically w...
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ISBN:
(数字)9798331510831
ISBN:
(纸本)9798331510848
In this paper, we propose an algorithm that can be used on top of a wide variety of self-supervised (SSL) approaches to take advantage of hierarchical structures that emerge during training. SSL approaches typically work through some invariance term to ensure consistency between similar samples and a regularization term to prevent global dimensional collapse. Dimensional collapse refers to data representations spanning a lower-dimensional subspace. Recent work has demonstrated that the representation space of these algorithms gradually reflects a semantic hierarchical structure as training progresses. Data samples of the same hierarchical grouping tend to exhibit greater dimensional collapse locally compared to the dataset as a whole due to sharing features in common with each other. Ideally, SSL algorithms would take advantage of this hierarchical emergence to have an additional regularization term to account for this local dimensional collapse effect. However, the construction of existing SSL algorithms does not account for this property. To address this, we propose an adaptive algorithm that performs a weighted decomposition of the denominator of the InfoNCE loss into two terms: local hierarchical and global collapse regularization respectively. This decomposition is based on an adaptive threshold that gradually lowers to reflect the emerging hierarchical structure of the representation space throughout training. It is based on an analysis of the cosine similarity distribution of samples in a batch. We demonstrate that this hierarchical emergence exploitation (HEX) approach can be integrated across a wide variety of SSL algorithms. Empirically, we show performance improvements of up to 5.6% relative improvement over baseline SSL approaches on classification accuracy on Imagenet with 100 epochs of training.
Ransomware attacks have increasingly exploited the NT File System (NTFS) to encrypt critical data, leading to significant disruptions and financial losses across various sectors. The introduction of a pass-through mec...
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In this paper, we provide an efficient algorithm to construct almost optimal (k, n, d)-superimposed codes with runlength constraints. A (k, n, d)superimposed code of length t is a t × n binary matrix such that an...
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The adaptive regularization algorithm for unconstrained nonconvex optimization was shown in [20, 7] to require, under standard assumptions, at most O(ϵ3/(3−q)) evaluations of the objective function and its derivatives...
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This work presents an adaptive algorithm-based system identification scheme-based echo canceller. The algorithms take into account FIR filters whose taps are selected using a stochastic gradient-based technique to red...
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ISBN:
(数字)9798350365092
ISBN:
(纸本)9798350365108
This work presents an adaptive algorithm-based system identification scheme-based echo canceller. The algorithms take into account FIR filters whose taps are selected using a stochastic gradient-based technique to reduce the signal error obtained from the system. Matlab is used to discuss and simulate the following adaptive filters: traditional LMS, LMF and LMMN, which is a mixture of LMF and LMS. Multiple delayed and attenuated replicas were added to a vocal input in order to approximate the echo. The mean-square error (MSE) was taken into consideration when comparing the algorithms.
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