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Gradient feature-based model predictive controlalgorithm of distribution processes |
| WANG Xin, XU Zuhua, ZHAO Jun, SHAO Zhijiang |
| National Center for International Research on Quality-Targeted Process Optimization and Control, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China |
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Abstract In the control of distribution processes, the traditional integral square error performance index only considers the area between the output curve and the target curve, which ignores the structural features of the distribution curve. A gradient feature-based model predictive control algorithm that takes into account the curve similarities is developed for distribution processes. The algorithm first models the distribution process curve with B-splines. Then, the algorithm quantifies the similarity between the curves based on gradient features and optimizes the design by combining numerical and gradient information. The composite trapezoidal rule is then used to discretize the optimization proposition. Finally, the optimization proposition is solved to get the optimal solution. Simulations show that this algorithm improves the similarity between the output curve and the target curve during curve switching with natural transitions of the curve shape.
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| Keywords
model predictive control
distribution process
curve similarity
gradient feature
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Issue Date: 14 May 2019
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2019,
Vol. 59
