Xiaoping Ma

A New Heuristic for Minimal Makespan in No-Wait Hybrid Flowshops

The no-wait hybrid flowshop scheduling problem is studied to minimize the makespan. This class of problem is characterized by the processing of n jobs through m stages with one or more machines at each stage, and there is no-wait restriction between stages. An integer programming model is first formulated. Then the complete scheduling scheme for a given job sequence is built, and a new heuristic based on the scheduling scheme is proposed. Computational experience demonstrates the effectiveness of the heuristic algorithm in finding near optimal schedules.

Sampled-data H∞ filtering for Markovian jump singularly perturbed systems with time-varying delay and missing measurements

ABSTRACT In this paper, sampled-data H∞ filtering problem is considered for Markovian jump singularly perturbed systems with time-varying delay and missing measurements. The sampled-data system is represented by a time-delay system, and the missing measurement phenomenon is described by an independent Bernoulli random process. By constructing an ϵ-dependent stochastic Lyapunov–Krasovskii functional, delay-dependent sufficient conditions are derived such that the filter error system satisfies the prescribed H∞ performance for all possible missing measurements. Then, an H∞ filter design method is proposed in terms of linear matrix inequalities. Finally, numerical examples are given to illustrate the feasibility and advantages of the obtained results.

Robust Regularized Random Vector Functional Link Network and Its Industrial Application

Production quality indices of complex industrial processes are usually hard to be measured in real time, which leads to unavailability of closed-loop operational optimization and control. Therefore, data-driven modeling techniques have been extensively employed to estimate production quality indices online. However, the conventional data-driven modeling methods often fail to achieve good performance because of interference from outliers. To solve the above-mentioned problem, this paper proposes an improved random vector functional link network (RVFLN) using a novel training method, which adopts a ridge regularized model with weighted factor for each training sample to evaluate the output weights. The robustness of the model has been achieved by employing a nonparametric kernel density estimation method to assign the weighted factors according to the training sample. To ensure the quality and computational load of the network in online applications, various online learning versions are presented according to the scope of data sampling. The improved RVFLN called robust regularized RVFLN has been validated using UCI, Statlib standard data sets, and an industrial grinding operation data. Results show that our proposed modeling technique perform favorably, and demonstrate its good potential for real world applications.

Sampled-data control for singularly perturbed systems with actuator saturation

The issue of the sampled-data control for singularly perturbed systems (SPSs) with input saturation is considered. Based on the Lyapunov-Krasovskii functional and linear matrix inequality (LMI) approach, the design method of sampled-data controller for SPSs with actuator saturation is proposed. Then, the estimation of stability region is given by solving the convex optimization problem. Finally, numerical examples are provided to demonstrate the merits of the obtained results.