A Synthesis of Feedback and Feedforward Control for Process Improvement Under Stationary and Nonstationary Time Series Disturbance Models

Abstract

Process adjustment strategy is an important part of the process improvement methods. The feedback control technique is used to compensate for the deviation of the output, and it has been intensively investigated. For continuous improvement and proactive strategies, feedback control has a delay and thus is not the ideal solution. In this article, motivated by a realistic manufacturing example, we propose the periodic shift disturbance models and investigate the feedforward control application from a new disturbance decomposition framework. We combine feedforward control with feedback control for maintaining the stability of the process and delivering products at target values. Then, we evaluate the performance of different control strategies for various disturbance models by using the output mean square error criterion. Sensitivity analysis of these control methods is made on different model parameter spaces, and robustness analysis for both model parameter and model structure misspecifications is presented. Two simulated examples show that the proposed control strategies can significantly reduce the variation of an evolving disturbance process.

Semi-supervised structured output prediction by local linear regression and sub-gradient descent

“…In the future, we will study how to fit the proposed algorithm to big data sets, by using big data processing framework, such as Map-Reduce of Hadoop software. We also want to apply the proposed method to various applications, such as computational biology and health care [21], [22], [23], [24], [25], [26], [27], [28], computer vision [29], [30], [31], [32], [33], [34], [35], [36], natural language processing, information retrieval [37], [38], [39], importance sampling [40], [41], and multimedia information processing [42], [43].…”

Optimizing top precision performance measure of content-based image retrieval by learning similarity function

“…The experiments over some benchmark databases show its advantages over other similarity learning methods. In the future, we will investigate to use some other similarity function as similarity measure instead of linear function, such as Bayesian network [12], [13], [14], and also to develop novel algorithms of other machine learning problems and applications besides similarity learning, to maximize top precision measure, such as importance sampling [15], [16], [17], portfolio choices [18], [19], multimedia technology [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], computational biology [30], [31], [32], [33], [34], big data processing [35], [36], [37], [38], [39], computer vision [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], information security [54], [55], [56]…”

Optimizing Top Precision Performance Measure of Content-Based Image Retrieval by Learning Similarity Function

“…The experiments over some benchmark databases show its advantages over other similarity learning methods. In the future, we will investigate to use some other similarity function as similarity measure instead of linear function, such as Bayesian network [12], [13], [14], and also to develop novel algorithms of other machine learning problems and applications besides similarity learning, to maximize top precision measure, such as importance sampling [15], [16], [17], portfolio choices [18], [19], multimedia technology [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], computational biology [30], [31], [32], [33], [34], big data processing [35], [36], [37], [38], [39], computer vision [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], information security [54], [55], [56]…”