Granular Prediction and Dynamic Scheduling Based on Adaptive Dynamic Programming for the Blast Furnace Gas System

Time-varying gain extended state observer-based adaptive optimal control for disturbed unmanned helicopter

In this paper, the robust adaptive optimal tracking control problem is addressed for the disturbed unmanned helicopter based on the time-varying gain extended state observer (TVGESO) and adaptive dynamic programming (ADP) methods. Firstly, a novel TVGESO is developed to tackle the unknown disturbance, which can overcome the drawback of initial peaking phenomenon in the traditional linear ESO method. Meanwhile, compared with the nonlinear ESO, the proposed TVGESO possesses easier and rigorous stability analysis process. Subsequently, the optimal tracking control issue for the original unmanned helicopter system is transformed into an optimization stabilization problem. By means of the ADP and neural network techniques, the feedforward controller and optimal feedback controller are skillfully designed. Compared with the conventional backstepping approach, the designed anti-disturbance optimal controller can make the unmanned helicopter accomplish the tracking task with less energy. Finally, simulation comparisons demonstrate the validity of the developed control scheme.

Linear Fuzzy Information-Granule-Based Fuzzy C-Means Algorithm for Clustering Time Series

This article aims to design a trend-oriented-granulation-based fuzzy C -means (FCM) algorithm that can cluster a group of time series at an abstract (granular) level. To achieve a better trend-oriented granulation of a time series, l1 trend filtering is firstly carried out to result in segments which are then optimized by the proposed segment merging algorithm. By constructing a linear fuzzy information granule (LFIG) on each segment, a granular time series which well reflects the linear trend characteristic of the original time series is produced. With the novel designed distance that can well measure the trend similarity of two LFIGs, the distance between two granular time series is calculated by the modified dynamic time warping (DTW) algorithm. Based on this distance, the LFIG-based FCM algorithm is developed for clustering time series. In this algorithm, cluster prototypes are iteratively updated by the specifically designed granule splitting and merging algorithm, which allows the lengths of prototypes to change in the process of iteration. This overcomes the serious drawback of the existing approaches, where the lengths of prototypes cannot be changed. Experimental studies demonstrate the superior performance of the proposed algorithm in clustering time series with different shapes or trends.

A Modified Genetic Algorithm With New Encoding and Decoding Methods for Integrated Process Planning and Scheduling Problem

Due to the complementarity of the process planning and shop scheduling, their integration can greatly facilitate the development of the intelligent manufacturing system. In the last decade, the integrated process planning and scheduling (IPPS) problem has become a research hotspot in the manufacturing system area. It is an NP-hard problem and is more complicated than the job shop scheduling problem. Although some progress has been obtained in the IPPS field, there are still many unsolved open problems. In this article, the novel integrated encoding and decoding methods are proposed by considering the OR-node of the process network graph. Moreover, a modified genetic algorithm (MGA) is designed based on the proposed coding methods. The process planning and the scheduling parts can be represented simultaneously in one individual. As for the precedence constraints between operations, the specifically designed operators are able to guarantee the feasibility of the operation sequence during the searching procedure. Then, the superiority of MGA is verified by updating nine new records on 37 well-known open problems, four of them reach their lower bounds. In addition, the proposed algorithm is also tested on a real-world case from a nonstandard equipment workshop in a Chinese machine tool company, which produces a common module of a packaging machine. The results show that the proposed MGA can solve the real-world case better than the comparative algorithms.