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The mechanical design of a robot often influences the choice of control strategy, especially for high-dimensional manipulator systems with multiple inputs and outputs. Striking a balance between hardware and software, it remains a significant challenge to design a control framework that is both easy to implement and high performing. This article addresses this concern by developing a systematic control architecture for trajectory tracking problems, focusing solely on position measurements. The approach involves constructing a dynamic model of the manipulator in the joint space through parameter identification techniques. A nonsmooth observer is then devised to estimate unmeasured states, unknown disturbances, and uncertain nonlinear functions in real time, which are incorporated into a nonsmooth feedback control design to provide a control solution. The stability of the system is ensured using the homogeneous system theory and Lyapunov theorems. To validate the effectiveness and feasibility of the proposed tracking control approach, extensive evaluations are conducted on a six-degree-of-freedom manipulator, including tests for tracking performance and repeatability.

The control coupling and stability of three-phase photovoltaic systems with constant power loads (CPLs) are two key technical issues that urgently need to be addressed. However, current methods are difficult to solve both technical challenges simultaneously. To address these issues, this article proposes a novel decoupled continuous control set model predictive control (CCS-MPC) strategy for LC-filter T-type three-phase four-leg three-level voltage source inverters (3P4L-3L-VSIs) driving CPLs. The control strategy is developed in αβγ frame. The model of 3P4L-3L-VSIs is first transformed from abc frame to the αβγ frame to remove the coupling effects in the abc frame. Then based on the decoupled model in the αβγ frame, a novel CCS-MPC strategy is proposed to provide balanced sinusoidal voltages. The α and β components are regulated by the A, B, and C legs, while the zero-sequence component is regulated by the N leg. To confirm the effectiveness of the proposed strategy, both simulation and experimental results are presented to verify the proposed decoupled CCS-MPC strategy. The results show that the proposed control achieves low computational burden and total harmonic distortion and can increase the stability margin especially for CPLs compared with the commonly used PI-based control, PR-based control, and deadbeat control.

This article investigates a generalized dynamic predictive control (GDPC) strategy with a novel autonomous tuning mechanism of the horizon for a class of nonlinear systems subject to mismatched disturbances. As a new incremental function for the predictive control method, the horizon can be determined autonomously with respect to the system working conditions, instead of selecting a fixed value via experience before, which is able to effectively improve the control performance optimization ability to a certain extent considering different system perturbation levels. To this aim, firstly, a nonrecursive composite control framework is constructed based on a series of disturbance observations via higher-order sliding modes. Secondly, by designing a simple one-step scaling gain update mechanism into the receding horizon optimization, the horizon can be therefore adaptively tuned according to its real-time practical operating conditions. A three-order numerical simulation and a typical engineering application of permanent magnet synchronous motor drive system are carried out to demonstrate the effectiveness and conciseness of the proposed GDPC method.

Nowadays, the realization of obstacle avoidance for robot manipulators are generally based on offline path planning, which may be insufficient for real-time dynamic obstacle avoidance scenarios. In order to achieve safe and smooth avoidance of dynamic obstacles, a low-latency motion planning algorithm, which takes into account the dynamic planning is of practical significance. Toward this end, this article proposes a cascaded nonlinear model predictive control (MPC) assigned with a two-stage optimization problem. Specially, the high-level MPC combines artificial potential field as a motion planner to generate foresight smooth trajectories. The low-level MPC acts as a trajectory tracker and a safety protector, taking along hard constraints to avoid collisions and singularities. In addition, a super-twisting observer is deployed to enhance the motion estimation accuracy of dynamic obstacles. Experimental results show that the proposed approach is beneficial to achieve safe and smooth dynamic obstacle avoidance in real-world scenarios.

This brief investigates the use of a new cascaded generalized super-twisting observer (GSTO) for sensorless permanent magnet synchronous motor (PMSM) drives. Specifically, the developed first-stage observer extracts information about internal back-EMF, and then, ensures that the estimation error converges to an arbitrarily small region within a finite time. Meanwhile, the second-stage observer reconstructs the position and speed information synchronously, which can improve the transient performance to a certain extent compared with the arctangent function calculation-based technique. The benefits of the proposed strategy lie in that the cascaded configuration of the two observers minimizes the high-frequency noise of the back-EMF reconstruction system and provides accurate estimations of rotor position and speed. Furthermore, the chattering problem commonly seen in the conventional sliding mode observer (SMO) can be avoided in the presented scheme. Experimental results validate the effectiveness of the proposed methodology.

In vision-based bearing defect detection, random shapes and multiscale defects can result in inaccurate feature extraction, and complex backgrounds can interfere with defect localization. Therefore, a novel bearing defect detection network (BDDN) is proposed in this article, aiming to improve the accuracy of bearing defect detection, which consists of deformable ResNet50 network (DRN) and double attention feature pyramid network (DA-FPN). In the DRN, it is found that using deformable convolution for stages 1–3 of ResNet50 is able to extract features that are closer to the actual defect regions through rich experiments. The DA-FPN is employed to reduce the interference of complex backgrounds for defect detection, which consists of a feature-enhanced attention module (FEAM) and a feature-communication attention module (FCAM). The FEAM obtains more refined high-level semantic information in channel feature extraction, while the FCAM efficiently fuses defect features across different levels. Experimental results demonstrate that the BDDN effectively detects bearing cover surface defects, achieving a mean average precision of 87.9%. In addition, it outperforms several defect detection models on the public Northeastern University dataset (NEU-DET) in terms of accuracy.

This article proposes a moving discretized control set model predictive control (MDCS-MPC) strategy with a simple parameter identification scheme for dual-active-bridge (DAB) converters. The value of the leakage inductance of the high-frequency transformer is estimated online within the digital microcontroller with an easy-implement gradient descent method. The identification scheme is isolated from the control loop. Therefore, the parameter identification scheme can improve the reliability of DAB converters when the leakage inductance changes due to temperature or other influence factors. The effectiveness of the proposed control strategy is verified by experiments.