This paper presents an approach to predefined-time terminal sliding mode control (PT-TSMC) for Euler–Lagrange systems (ELSs) with actuator control input saturation, leveraging fuzzy compensation techniques. Initially, the paper focuses on tunable predefined-time stability (PTS), providing adjustable parameters to fine-tune the system’s stability time, thereby enhancing the adaptability of the controller design. Based on this approach, a control input system is formulated to ensure predefined-time stability for Euler–Lagrange systems. Additionally, a strategy is proposed to enhance system performance, including robustness improvement, chattering reduction, and singularity elimination, through the design of an adaptive fuzzy logic system (AFLS). The AFLS estimates unstructured model uncertainties and compounded disturbances, seamlessly integrating them into the control system. Notably, this approach effectively addresses the challenge of handling unknown model data. Finally, comprehensive comparative simulations demonstrate the effectiveness of the proposed method, showcasing its commendable control performance.

This paper introduces a novel prescribed performance model-free controller tailored for industrial robot arms, seamlessly integrating adaptive sliding mode control (ASMC) and time-delay estimation (TDE). Leveraging TDE, our controller adeptly estimates both the inherent dynamics of the robot and unstructured uncertainties such as disturbances and parameter variations. However, TDE, which relies on past angular acceleration and input torque, inevitably introduces errors. To mitigate these, our approach compensates for current TDE errors using past error information. Additionally, we introduce a fixed-time sliding mode surface from prescribed performance control and an auxiliary system to improve performance under input saturation. Moreover, we propose an adaptive law to ensure the positivity of the adaptive parameter by considering the current adaptive parameter value and the sampling period. Through extensive simulated studies conducted on industrial robot arms, we demonstrate the effectiveness of our control approach, showcasing robustness, reduced chattering, and high accuracy across diverse scenarios.

In this paper, a novel output feedback tracking control scheme based on neural sliding mode without joint velocity measurement and the high order sliding mode observer for the uncertainty of robot manipulators are presented. Two second-order sliding mode observers are designed to estimate joint velocities and dynamic uncertainties of the robot manipulator, respectively. The first-second order sliding mode observer is used to estimate the state vector in a finite time without filtration. To estimate the uncertainties without filtration, the second second-order sliding mode nonlinear observer is designed. By integrating two observers, the resulting observer can theoretically obtain exact estimations of both joint velocities and dynamic uncertainties. They are used to design an output feedback tracking control scheme based on neural sliding mode controller. This proposed control scheme can reduce chattering and improves tracking performances. Finally, the simulation for a 2-DOF robot manipulator is given to show the effectiveness of this control strategy.

In order to enhance the performance of bearing defect classification, feature extraction and dimensionality reduction have become important. In order to extract the effective features, wavelet kernel local fisher discriminant analysis (WKLFDA) is first proposed; herein, a new wavelet kernel function is proposed to construct the kernel function of LFDA. In order to automatically select the parameters of WKLFDA, a particle swarm optimization (PSO) algorithm is employed, yielding a new PSO-WKLFDA. When compared with the other state-of-the-art methods, the proposed PSO-WKLFDA yields better performance. However, the use of a single global transformation of PSO-WKLFDA for the multiclass task does not provide excellent classification accuracy due to the fact that the projected data still significantly overlap with each other in the projected subspace. In order to enhance the performance of bearing defect classification, a novel method is then proposed by transforming the multiclass task into all possible binary classification tasks using a one-against-one (OAO) strategy. Then, individual PSO-WKLFDA (I-PSO-WKLFDA) is used for extracting effective features of each binary class. The extracted effective features of each binary class are input to a support vector machine (SVM) classifier. Finally, a decision fusion mechanism is employed to merge the classification results from each SVM classifier to identify the bearing condition. Simulation results using synthetic data and experimental results using different bearing fault types show that the proposed method is well suited and effective for bearing defect classification.

This paper is concerned with compression of multiple inertial sensor data. The compressed data consists of ADPCM compressed data and an adaptive refinement data. Each sensor data is first compressed using ADPCM. To reduce the quantization errors, refinement quantizers are then used, where the total number of bits for the refinement quantizers is fixed. Which inertial sensor data should be further refined is adaptively determined based on quantization error bounds. In the proposed method, total number of bits for the compressed data is fixed; however the number of bits for individual inertial sensor is adaptively determined. Through experiments, it is shown that the proposed method provides better compression quality than the standard ADPCM with the same total number of bits.

A null space damping method is proposed which solves the stability problem commonly encountered in existing local joint torque optimization techniques applied to redundant manipulators. The damped joint motion is quite stable and globally outperforms undamped techniques in the sense of torque minimization capability. In addition, simulation results show that the resulting damped joint motion becomes conservative after an initial transient stage for cyclic end-effector trajectories, while undamped pseudo-inverse solutions are reported to never lead to conservative motion. Three undamped and damped joint torque optimization algorithms are considered and discussed with comparison to the previous literature. The effectiveness of the proposed null space damping method is demonstrated by computer simulation.>