Kie Chung Cheung

Passivity criteria for continuous-time neural networks with mixed time-varying delays ☆

Abstract This paper is concerned with the problem of passivity analysis for uncertain continuous-time neural networks with mixed time-varying delays. The mixed time-varying delays consist of both discrete and distributed delays, in which the discrete delays are assumed to be varying within a given interval. In addition, the uncertainties are assumed to be norm-bounded. By employing a novel Lyapunov–Krasovskii functional, new passivity delay-interval-dependent criteria are established to guarantee the passivity performance. When estimating an upper bound of the derivative of the Lyapunov–Krasovskii functional, we handle the terms related to the discrete and distributed delays appropriately so as to develop less conservative results. These passivity conditions are presented in terms of linear matrix inequalities, which can be easily solved via standard numerical software. Some numerical examples are given to illustrate the effectiveness of the proposed method.

An Improved Incremental Learning Approach for KPI Prognosis of Dynamic Fuel Cell System

The key performance indicator (KPI) has an important practical value with respect to the product quality and economic benefits for modern industry. To cope with the KPI prognosis issue under nonlinear conditions, this paper presents an improved incremental learning approach based on available process measurements. The proposed approach takes advantage of the algorithm overlapping of locally weighted projection regression (LWPR) and partial least squares (PLS), implementing the PLS-based prognosis in each locally linear model produced by the incremental learning process of LWPR. The global prognosis results including KPI prediction and process monitoring are obtained from the corresponding normalized weighted means of all the local models. The statistical indicators for prognosis are enhanced as well by the design of novel KPI-related and KPI-unrelated statistics with suitable control limits for non-Gaussian data. For application-oriented purpose, the process measurements from real datasets of a proton exchange membrane fuel cell system are employed to demonstrate the effectiveness of KPI prognosis. The proposed approach is finally extended to a long-term voltage prediction for potential reference of further fuel cell applications.

An Advanced PLS Approach for Key Performance Indicator-Related Prediction and Diagnosis in Case of Outliers

In the process industry, the key performance indicator (KPI)-related prediction and fault diagnosis are important steps to guarantee the product quality and improve economic benefits. A popular monitoring method as it has been, the partial least squares (PLS) algorithm is sensitive to outliers in training datasets, and cannot efficiently distinguish faults related to KPI from those unrelated to KPI due to its oblique projection to the input space. In this paper, a novel robust data-driven approach, named advanced partial least squares (APLS), is presented to handle process outliers under an improved framework of PLS. By means of a weighting strategy, APLS can remove the impact of outliers on process measurements and establish a more accurate model than PLS for fault diagnosis in the monitoring scheme, whose effectiveness has been verified through the Tennessee Eastman (TE) benchmark process. Simulation results demonstrate that the proposed approach is suitable not only for the KPI-related process prediction but also for the diagnosis of KPI-related faults.

Direct voltage control of magnetorheological damper for vehicle suspensions

The paper presents a study on the direct voltage control of a magnetorheological (MR) damper for application in vehicle suspensions. As MR damper dynamics is highly nonlinear, the direct control system design for an MR damper is difficult. Representing an MR damper by a Takagi?Sugeno (TS) fuzzy model enables the linear control theory to be directly applied to design the MR damper controller. In this paper, first the MR damper dynamics is represented by a TS fuzzy model, and then an H? controller that considers the suspension performance requirements and the constraint on the input voltage for the MR damper is designed. Furthermore, considering the case that not all the state variables are measurable in practice, the design of an H? observer with immeasurable premise variables and the design of a robust controller are proposed, respectively. Numerical simulations are used to validate the effectiveness of the proposed approaches.

Stability, stabilization and L 2 -gain analysis of periodic piecewise linear systems

In this paper, the stability, stabilization and L 2 -gain problems are investigated for periodic piecewise linear systems, in which not all subsystems are Hurwitz. First, some sufficient and necessary conditions for the exponential stability are established. By employing a discontinuous Lyapunov function with time-varying Lyapunov matrix, stabilization and L 2 -gain conditions of periodic piecewise linear systems are proposed by allowing the corresponding Lyapunov function to be possibly non-monotonically decreasing over a period. A state-feedback periodic piecewise controller is developed to stabilize the system, and the corresponding algorithm is proposed to compute the controller gain. The L 2 -gain criteria with continuous time-varying Lyapunov matrix and piecewise constant Lyapunov matrices are studied as well. Numerical examples are given to show the validity of the proposed techniques.

H∞ control of periodic piecewise vibration systems with actuator saturation

In this paper, an H∞ controller with actuator saturation consideration is proposed to attenuate the vibration of periodic piecewise vibration systems. Based on a continuous Lyapunov function with a time-varying Lyapunov matrix, the H∞ performance index of periodic piecewise vibration systems is studied first. On the basis of the obtained H∞ criterion, the conditions of designing a state-feedback active vibration controller are proposed in matrix inequality form with actuator saturation taken into account. Because of the nonconvexity of the conditions, a corresponding algorithm to compute the controller gain is developed as well. A representative numerical example is used to verify the effectiveness of the proposed method.

Control of vehicle suspension using an adaptive inerter

In this paper, we study the suspension performances with an adaptive inerter under the assumption that the inertance may be adjusted in real-time. A quarter-car model with an inerter installed in parallel with a spring and a damper is considered. First, for a given suspension system, a study of the effects of using a fixed inerter is provided. It shows the difficulty in choosing a fixed inerter to satisfy the vehicle performances at the sprung mass natural frequency without deteriorating significantly at the unsprung mass natural frequency. Then, a state-feedback H 2 controller for an active suspension system is designed. The active force is approximated by an inerter with adjustable inertance. Ride quality, suspension deflection and tyre deflection performances are considered independently as the prime objective. Simulation results show that comparing with the passive suspension with fixed inerter, for ride quality and tyre deflection, the suspension with adaptive inerter can achieve improvement at the sprung mass natural frequency at the expense of a relatively small deterioration at the unsprung mass natural frequency. For suspension deflection, a better performance can be achieved at high frequencies with almost no loss of performance at other frequencies. This method is easier to implement in practice than the optimized passive suspension with a fixed inerter, which calls for complicated layout.

Generalized H ∞ model reduction for stable two-dimensional discrete systems

For model reduction, the approximation performance sometimes needs to be enhanced over a specific frequency range. Motivated by this fact, the paper investigates generalized

Motion-based active disturbance rejection control for a non-linear full-car suspension system

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