Surface electromyography (sEMG) is a promising technology that can capture muscle activation signals to control robots through novel human–machine interfaces (HMIs). This technology has already been applied in scenarios such as prosthetic design, assisted robot control, and rehabilitation training. This article provides an overview of sEMG-based robot control, covering two important aspects: (1) sEMG signal processing and classification methods and (2) robot control strategies and methods based on sEMG. First, the article outlines the general steps in sEMG signal processing and summarizes the commonly used methods for data acquisition, pre-processing, and feature extraction. In addition, machine-learning-based pattern recognition methods have been introduced for sEMG signal classification. Subsequently, user intent-based robot control strategies are classified into three categories: full-human continuous control, semi-autonomous continuous control, and discrete control, and their control methods and applicable scenarios are compared. Finally, this article discusses the advantages, disadvantages, and future development prospects of sEMG-based robot control. This review provides a comprehensive overview of sEMG-based robot control, from signal processing and classification methods to robot control strategies and methods, aiming to guide future research on selecting filters, feature sets, and pattern recognition methods and to assist in establishing sEMG-driven robot control frameworks.

We present a generic methodology for machinery fault diagnosis through pattern recognition techniques. The proposed method has the advantage of dealing with complicated signatures, such as those present in the vibration signals of rolling element bearings with and without defects. The signature varies with the location and severity of bearing defects, load and speed of the shaft, and different bearing housing structures. More specifically, the proposed technique contains effective feature extraction, good learning ability, reliable feature fusion, and a simple classification algorithm. Examples with experimental testing data were used to illustrate the idea and effectiveness of the proposed method.

In this paper, an adaptive controller is developed for the pressure tracking of the pressurized toolhead in order to maintain the constant contact stress for the polishing process. This is a new polishing control method, which combines the adaptive control theory and the constant stress theory of the contact model. By using an active pneumatic compliant toolhead, a recursive least-squares estimator is developed to estimate the pneumatic model, and then a minimum-degree pole-placement method is applied to design a self-tuning controller. The simulation and experiment results of the proposed controller are presented and discussed. The main advantage of the constant contact stress control is high figuring accuracy.

Presented in this paper is a method for the design of modular reconfigurable machine tools (MRMTs). An MRMT is capable of using a minimal number of modules through reconfiguration to perform the required machining tasks for a family of parts. The proposed method consists of three steps: module identification, module determination, and layout synthesis. In the first step, the module components are collected from a family of general-purpose machines to establish a module library. In the second step, for a given family of parts to be machined, a set of needed modules are selected from the module library to construct a desired reconfigurable machine tool. In the third step, a final machine layout is decided though evaluation by considering a number of performance indices. Based on this method, a software package has been developed that can design an MRMT for a given part family.