Hongyi Li

Bio: Hongyi Li is an academic researcher from Bohai University. The author has contributed to research in topics: Control theory & Fuzzy control system. The author has an hindex of 52, co-authored 120 publications receiving 11184 citations. Previous affiliations of Hongyi Li include University of Portsmouth & Harbin Institute of Technology.

Adaptive Sliding-Mode Control for Nonlinear Active Suspension Vehicle Systems Using T–S Fuzzy Approach

Abstract: This paper deals with the adaptive sliding-mode control problem for nonlinear active suspension systems via the Takagi-Sugeno (T-S) fuzzy approach. The varying sprung and unsprung masses, the unknown actuator nonlinearity, and the suspension performances are taken into account simultaneously, and the corresponding mathematical model is established. The T-S fuzzy system is used to describe the original nonlinear system for the control-design aim via the sector nonlinearity approach. A sufficient condition is proposed for the asymptotical stability of the designing sliding motion. An adaptive sliding-mode controller is designed to guarantee the reachability of the specified switching surface. The condition can be converted to the convex optimization problems. Simulation results for a half-vehicle active suspension model are provided to demonstrate the effectiveness of the proposed control schemes.

Reliable Fuzzy Control for Active Suspension Systems With Actuator Delay and Fault

Abstract: 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.

Output-Feedback-Based $H_{\infty}$ Control for Vehicle Suspension Systems With Control Delay

Abstract: This paper deals with the problem of output-feedback H∞ control for a class of active quarter-car suspension systems with control delay. The dynamic system of the suspension systems is first formed in terms of the control objectives, i.e., ride comfort, road holding, suspension deflection, and maximum actuator control force. Then, the objective is to the design of the dynamic output-feedback H∞ controller in order to ensure asymptotic stability of the closed-loop system with H∞ disturbance attenuation level and the output constraints. Furthermore, using Lyapunov theory and linear matrix inequality (LMI) approach, the existence of admissible controllers is formulated in terms of LMIs. With these satisfied conditions, a desired dynamic output-feedback controller can be readily constructed. Finally, a quarter-vehicle model is exploited to demonstrate the effectiveness of the proposed method.

The sector bound approach to quantized feedback control | NOVA. The University of Newcastle's Digital Repository

Abstract: This paper studies a number of quantized feedback design problems for linear systems. We consider the case where quantizers are static (memoryless). The common aim of these design problems is to stabilize the given system or to achieve certain performance with the coarsest quantization density. Our main discovery is that the classical sector bound approach is nonconservative for studying these design problems. Consequently, we are able to convert many quantized feedback design problems to well-known robust control problems with sector bound uncertainties. In particular, we derive the coarsest quantization densities for stabilization for multiple-input-multiple-output systems in both state feedback and output feedback cases; and we also derive conditions for quantized feedback control for quadratic cost and H/sub /spl infin// performances.

A Review on Basic Data-Driven Approaches for Industrial Process Monitoring

Abstract: Recently, to ensure the reliability and safety of modern large-scale industrial processes, data-driven methods have been receiving considerably increasing attention, particularly for the purpose of process monitoring. However, great challenges are also met under different real operating conditions by using the basic data-driven methods. In this paper, widely applied data-driven methodologies suggested in the literature for process monitoring and fault diagnosis are surveyed from the application point of view. The major task of this paper is to sketch a basic data-driven design framework with necessary modifications under various industrial operating conditions, aiming to offer a reference for industrial process monitoring on large-scale industrial processes.

An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data

Abstract: Intelligent fault diagnosis is a promising tool to deal with mechanical big data due to its ability in rapidly and efficiently processing collected signals and providing accurate diagnosis results. In traditional intelligent diagnosis methods, however, the features are manually extracted depending on prior knowledge and diagnostic expertise. Such processes take advantage of human ingenuity but are time-consuming and labor-intensive. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed for intelligent diagnosis of machines. In the first learning stage of the method, sparse filtering, an unsupervised two-layer neural network, is used to directly learn features from mechanical vibration signals. In the second stage, softmax regression is employed to classify the health conditions based on the learned features. The proposed method is validated by a motor bearing dataset and a locomotive bearing dataset, respectively. The results show that the proposed method obtains fairly high diagnosis accuracies and is superior to the existing methods for the motor bearing dataset. Because of learning features adaptively, the proposed method reduces the need of human labor and makes intelligent fault diagnosis handle big data more easily.

Data-Based Techniques Focused on Modern Industry: An Overview

Abstract: This paper provides an overview of the recent developments in data-based techniques focused on modern industrial applications. As one of the hottest research topics for complicated processes, the data-based techniques have been rapidly developed over the past two decades and widely used in numerous industrial sectors nowadays. The core of data-based techniques is to take full advantage of the huge amounts of available process data, aiming to acquire the useful information within. Compared with the well-developed model-based approaches, data-based techniques provide efficient alternative solutions for different industrial issues under various operating conditions. The main objective of this paper is to review and summarize the recent achievements in data-based techniques, especially for complicated industrial applications, thus providing a referee for further study on the related topics both from academic and practical points of view. This paper begins with a brief evolutionary overview of data-based techniques in the last two decades. Then, the methodologies only based on process measurements and the model-data integrated techniques will be further introduced. The recent developments for modern industrial applications are, respectively, presented mainly from perspectives of monitoring and control. The new trends of data-based technique as well as potential application fields are finally discussed.