This paper is focused on reliable fuzzy H _∞ controller design for active suspension systems with actuator delay and fault. The Takagi-Sugeno (T-S) fuzzy model approach is adapted in this study with the consideration of the sprung and the unsprung mass variation, the actuator delay and fault, and other suspension performances. By the utilization of the parallel-distributed compensation scheme, a reliable fuzzy H _∞ performance analysis criterion is derived for the proposed T-S fuzzy model. Then, a reliable fuzzy H _∞ controller is designed such that the resulting T-S fuzzy system is reliable in the sense that it is asymptotically stable and has the prescribed H _∞ performance under given constraints. The existence condition of the reliable fuzzy H _∞ controller is obtained in terms of linear matrix inequalities (LMIs) Finally, a quarter- vehicle suspension model is used to demonstrate the effectiveness and potential of the proposed design techniques.

With recent advances in battery capacity and the development of hydrogen fuel cells, autonomous underwater vehicles (AUVs) are being used to undertake longer missions that were previously performed by manned or tethered vehicles. As a result, more advanced navigation systems are needed to maintain an accurate position over a larger operational area. The accuracy of the navigation system is critical to the quality of the data collected during survey missions and the recovery of the AUV. Many different methods for navigation in different underwater environments have been proposed in the literature. In this correspondence paper, the state of the art in navigation technologies for AUVs is investigated for theoretical and operational systems. Their suitability for use in different environments is compared and current limitations of these methods are identified. In addition, new approaches to address these current problems and areas for future research are suggested. Finally, it is concluded that only geophysically referenced methods will enable AUVs to navigate accurately over large areas and that advances in underwater feature recognition are required before these methods can be implemented in operational AUVs.

Corticomuscular coherence (CMC) is an index utilized to indicate coherence between brain motor cortex and associated body muscles, conventionally. As an index of functional connections between the cortex and muscles, CMC research is the focus of neurophysiology in recent years. Although CMC has been extensively studied in healthy subjects and sports disorders, the purpose of its applications is still ambiguous, and the magnitude of CMC varies among individuals. Here, we aim to investigate factors that modulate the variation of CMC amplitude and compare significant CMC between these factors to find a well-developed research prospect. In the present review, we discuss the mechanism of CMC and propose a general definition of CMC. Factors affecting CMC are also summarized as follows: experimental design, band frequencies and force levels, age correlation, and difference between healthy controls and patients. In addition, we provide a detailed overview of the current CMC applications for various motor disorders. Further recognition of the factors affecting CMC amplitude can clarify the physiological mechanism and is beneficial to the implementation of CMC clinical methods.

As viewpoint issue is becoming a bottleneck for human motion analysis and its application, in recent years, researchers have been devoted to view-invariant human motion analysis and have achieved inspiring progress. The challenge here is to find a methodology that can recognize human motion patterns to reach increasingly sophisticated levels of human behavior description. This paper provides a comprehensive survey of this significant research with the emphasis on view-invariant representation, and recognition of poses and actions. In order to help readers understand the integrated process of visual analysis of human motion, this paper presents recent development in three major issues involved in a general human motion analysis system, namely, human detection, view-invariant pose representation and estimation, and behavior understanding. Public available standard datasets are recommended. The concluding discussion assesses the progress so far, and outlines some research challenges and future directions, and solution to what is essential to achieve the goals of human motion analysis.

This paper reviews computational-intelligence-involved approaches in active vehicle suspension control systems with a focus on the problems raised in practical implementations by their nonlinear and uncertain properties. After a brief introduction on active suspension models, the paper explores the state of the art in fuzzy inference systems, neural networks, genetic algorithms, and their combination for suspension control issues. Discussions and comments are provided based on the reviewed simulation and experimental results. The paper is concluded with remarks and future directions.