IEEE/ASME Transactions on Mechatronics

An adaptive-resonance theory (ART)-based fuzzy controller is presented for the adaptive navigation of a quadruped robot in cluttered environments, by incorporating the capability of ART in stable category recognition into fuzzy-logic control for selecting the adequate rule base. The environment category and navigation mechanism are first described for the quadruped robot. The ART-based fuzzy controller, including an ART-based environment recognizer, a comparer, combined rule bases, and a fuzzy inferring mechanism, is then introduced for the purpose of the adaptive navigation of the quadruped robot. Unlike classical/conventional adaptive-fuzzy controllers, the present adaptive-control scheme is implemented by the adaptive selection of fuzzy-rule base in response to changes of the robot environment, which can be categorized and recognized by the proposed environment recognizer. The results of simulation and experiment show that the adaptive-fuzzy controller is effective.

In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs is of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are limited by the complexity of mathematical modeling. Conventional data-driven approaches, on the other hand, require massive efforts to extract the degradation features and construct the health index. In this article, a novel data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNNs) in predicting the RULs of bearings. More concretely, raw vibrations of training bearings are first processed using the Hilbert–Huang transform to construct a novel nonlinear degradation energy indicator which can be used as the training label. The CNN is then employed to identify the hidden pattern between the extracted degradation energy indicator and the raw vibrations of training bearings, which makes it possible to estimate the degradation of the test bearings automatically. Finally, testing bearings’ RULs are predicted through using an $\epsilon$-support vector regression model. The superior performance of the proposed RUL estimation framework, compared with the state-of-the-art approaches, is demonstrated through the experimental results. The generality of the proposed CNN model is also validated by performance test on other bearings undergoing different operating conditions.

A novel mechanism to generate nonrevisiting uniform coverage (NUC) paths on arbitrarily shaped object surfaces is presented in this work. Given a nonplanar surface, nonzero curvature makes traditional homeomorphic fitting of regular template coverage paths from planar regions onto the object surface non-distance-preserving. Any coverage path with a realistic tooling size derived in this way will suffer from overlaps and missing gaps when transformed onto the object surfaces, unable to uniformly cover the target. To overcome this, a discretization process is adopted to represent the object surface as a uniform unstructured mesh, with resolution set in accordance to the tool size. It is proven that a coverage skeleton path must exist by mesh subdivision refinement which, after a local optimization step to improve overlap, missing gaps, and smoothness, gives rise to template-free superior NUC paths. Extensive simulation examples are presented to prove the validity of the proposed strategy in realistic settings. The proposed scheme is able to achieve 95.9% coverage on benchmark surface tests, outperforming comparable coverage algorithms, such as a homeomorphic boustrophedon mapping, which can at best achieve 80.9% coverage, or more recent state-of-the-art methods able to reach 94.7% coverage. An accompanying video is supplied with examples, including a real-world implementation of an NUC path tracked by a manipulator. An open-source implementation has been made available.

A contouring controller for biaxial systems that integrates the effects of feedback, feedforward, and cross-coupled control is proposed in this study. Conventional approaches to contouring control suffer from the complicated contour-error model and from lack of a systematic way for controller design. The integrated controller is based on polar coordinates under which a relatively simple contour-error model can be obtained. Taking the simple contour error as a state variable, the contouring-control problem is transformed into a stabilization problem. The feedback-linearization technique incorporated with linear feedback or robust control (such as sliding-mode control) can then yield the integrated controller. The proposed method is verified both numerically and experimentally and is compared with the conventional approach. It is found that the proposed controller is better for high speed and/or noncircular contouring. In addition, it can be applied to either linear plants or nonlinear plants (like linear motors).

This paper presents an anthropomorphic robot hand, called the Gifu hand II, which has a thumb and four fingers, all the joints of which are driven by servomotors built into the fingers and the palm. The thumb has four joints with four-degrees-of-freedom (DOF), the other fingers have four joints with 3-DOF, and two axes of the joints near the palm cross orthogonally at one point, as is the case in the human hand. The Gifu hand II can be equipped with six-axes force sensor at each fingertip, and a developed distributed tactile sensor with 624 detecting points on its surface. The design concepts and specifications of the Gifu hand II, the basic characteristics of the tactile sensor, and the pressure distributions at the time of object grasping are described and discussed herein. Our results demonstrate that the Gifu hand II has a high potential to perform dexterous object manipulations like the human hand.

Smart structures are civil or mechanical structures that can automatically and intelligently react to external dynamic loadings such as vibration shocks, strong winds, destructive waves, and earthquakes. The use of magnetorheological (MR) dampers has been of increasing interest in smart structures as they have reliable, stable and fail-safe operations, small energy requirements, and fast responses. The challenges of MR damper structural control rest with the complex dynamics involved, high nonlinearity due to the force–velocity hysteresis, nonaffinity, and constraints of the control system with the magnetization current as its input. To address these problems, this paper presents the modeling and control design as well as the implementation results of a second-order sliding mode controller for the MR dampers embedded in the building structures subject to quake-induced vibrations. Based on the static hysteresis model of the MR damper using computationally tractable algebraic expressions, algorithms are proposed to control directly the magnetization current to the dampers, configured in a differential mode to counteract the offset force. The effectiveness of the proposed technique is verified in simulation by using a building model under quake-like excitations. The experimental results are provided on a laboratorial setup tested on a shake table.