MOTIVATION: Clustering single-cell RNA sequencing (scRNA-seq) data plays a vital role in the study of cellular heterogeneity. Many algorithms have been developed to cluster scRNA-seq data. However, traditional clustering algorithms often fail to capture local consistency, whereas biclustering algorithms suffer from issues such as cell loss, poor adaptability to high-dimensional data, and iterative selection challenges. RESULTS: In this paper, we introduce scDBic, a novel deep learning-based biclustering algorithm specialized for scRNA-seq data. It comprises three main steps: cell clustering with a deep autoencoder, gene clustering, and identification of key gene clusters using the reverse strategy. The key idea is that the deep autoencoder captures the main information of gene expression and the reverse strategy identifies the key genes of cell groups. Therefore, cell clustering performance can be improved. The results demonstrate that our algorithm not only discovers cell groups in scRNA-seq data but also identifies the key genes of the cell groups. Furthermore, the clustering performance of our algorithm is better than that of traditional clustering and biclustering algorithms. This novel technique can be directly applied to discover cell groups and identify key genes in cell groups. AVAILABILITY AND IMPLEMENTATION: The source code and test data are freely available at GitHub (https://github.com/Xiaoqi-Tang/scDBic) and archived on Zenodo (DOI: 10.5281/zenodo.18676401).

The equipment used in kinematic calibration schemes is generally expensive and time-consuming, and requires skilled engineers. This paper proposes a new measurement method for identifying kinematic errors on a six degree of freedom industrial robot with a single laser displacement sensor. The three-dimensional deviations of the end-effector are calculated by an algorithm with data collected from the scanning procedure. Then, the kinematic parameters are identified by combining the joint angles, the deviations involved error information and an error model. The accuracy and efficiency are improved by the laser displacement sensor and the simple scanning procedure. The method presented in this paper can also be expanded to other serial industrial robots. The experimental results validate the effectiveness of the proposed method.

We proposed a new learning controller for decentralized tracking control of nonlinear robot manipulators. When the desired trajectory of each subsystem of the robot lasts for a finite duration, it can be approximated by a Fourier series with constant harmonic magnitudes. For each subsystem of the robot, a learning controller is designed to individually control each harmonic component of the actual output, although it is cross-related to other components in nonlinear systems. The learning algorithm is designed such that each harmonic magnitude of the actual output converges to that of the desired trajectory within the system bandwidth. Since this decentralized learning controller is designed in Fourier space instead of time domain, the system's time-delay could be easily compensated. This learning controller is only based on the local input and output information; no a priori structure or parameters of the system model are required. The experimental results on a 3-DOF direct-drive robot are presented.

A decentralized learning control scheme for tracking control of robot manipulators is presented. In this scheme, each joint is considered as a subsystem and controlled independently. The interactions from other subsystems are treated as deterministic uncertainties. The desired trajectory and the output of each subsystem are reparametrized by Fourier series (FS). A learning controller designed in Fourier space regulates each harmonic component individually by forcing Fourier coefficients (FCs) of the actual output approach to the corresponding FCs of the desired trajectory which are known constants. Since the information of the system phase-delay is included in FCs, the time-delay of the system can be easily compensated. The learning controller only uses the input and output information of the subsystem, no a priori knowledge about the system models is required. The asymptotic convergence of the tracking is proved in the paper. The experimental results on a 3 DOF direct-drive robot show that convergence rate is faster compared with many other learning controllers and the performance of the closed-loop system is dramatically improved.

The problem of how to improve the transient performance of a single-link flexible robot arm rotating in a horizontal plane using FFT method is addressed in this paper. Lyapunov's direct method is used to prove the stability of the closed-loop system under the proposed control law. However, the performance of the system is limited by factors such as nonlinearity, lower resonate frequency and time-delay. In order to further improve the tracking performance, we proposed to use FFT to analyze the tracking error. Through the analysis we can obtain the information of the system in the frequency-domain. The information then is fed back to the system, so that the performance of the system can be enhanced. The experimental results of a single flexible arm are presented in this paper for illustration.

A model-free robust/adaptive decentralized controller for nonlinear uncertain robot manipulators is presented. The proposed controller consists of a PD controller, a robust/adaptive controller and dynamic compensator. The dynamic compensator is online up-dated based on the Taylor expansion in terms of the historical information of the individual local inputs to the system. The result obtained from the dynamic compensator is used to cancel the nonlinearity and uncertainties within the system to be controlled. The robust/adaptive controller is used to handle the effect of the residual dynamics, which is the difference of the dynamics of the system and the estimated one. The stability analysis of the closed-loop system is performed based on a modified version of Lyapunov's theory. Experimental results on the tracking control of a 3-DOF direct drive robot are presented for illustration.