Improved model-free adaptive predictive control method for direct data-driven control of a wastewater treatment process with high performance

2022 ◽  
Vol 110 ◽  
pp. 11-23
Author(s):  
Shuai Zhang ◽  
Ping Zhou ◽  
Yongfang Xie ◽  
Tianyou Chai
Keyword(s):  
Predictive Control ◽  
High Performance ◽  
Treatment Process ◽  
Control Method ◽  
Data Driven ◽  
Wastewater Treatment Process ◽  
Model Free ◽  
Direct Data ◽  
Improved Model ◽  
Adaptive Predictive Control

scholarly journals An Improved Model-Free Current Predictive Control Method for SPMSM Drives

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 134672-134681
Author(s):  
Xuerong Li ◽  
Yang Wang ◽  
Xingzhong Guo ◽  
Xing Cui ◽  
Shuo Zhang ◽  
...  
Keyword(s):  
Predictive Control ◽  
Control Method ◽  
Model Free ◽  
Free Current ◽  
Improved Model

scholarly journals Adaptive Predictive Control: A Data-Driven Closed-Loop Subspace Identification Approach

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaosuo Luo ◽  
Yongduan Song
Keyword(s):  
Predictive Control ◽  
Closed Loop ◽  
Control Method ◽  
Negative Impact ◽  
Data Driven ◽  
Recursive Identification ◽  
Subspace Identification ◽  
Identification Algorithm ◽  
Closed Loop Identification ◽  
Adaptive Predictive Control

This paper presents a data-driven adaptive predictive control method using closed-loop subspace identification. As the predictor is the key element of the predictive controller, we propose to derive such predictor based on the subspace matrices which are obtained through the closed-loop subspace identification algorithm driven by input-output data. Taking advantage of transformational system model, the closed-loop data is effectively processed in this subspace algorithm. By combining the merits of receding window and recursive identification methods, an adaptive mechanism for online updating subspace matrices is given. Further, the data inspection strategy is introduced to eliminate the negative impact of the harmful (or useless) data on the system performance. The problems of online excitation data inaccuracy and closed-loop identification in adaptive control are well solved in the proposed method. Simulation results show the efficiency of this method.

scholarly journals Model predictive control for autonomous ground vehicles: a review

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Shuyou Yu ◽  
Matthias Hirche ◽  
Yanjun Huang ◽  
Hong Chen ◽  
Frank Allgöwer
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
High Performance ◽  
Control Method ◽  
Theoretical Background ◽  
Future Research ◽  
Centralized Control ◽  
Ground Vehicles ◽  
Uncertain Environments ◽  
Autonomous Ground Vehicles

AbstractThis paper reviews model predictive control (MPC) and its wide applications to both single and multiple autonomous ground vehicles (AGVs). On one hand, MPC is a well-established optimal control method, which uses the predicted future information to optimize the control actions while explicitly considering constraints. On the other hand, AGVs are able to make forecasts and adapt their decisions in uncertain environments. Therefore, because of the nature of MPC and the requirements of AGVs, it is intuitive to apply MPC algorithms to AGVs. AGVs are interesting not only for considering them alone, which requires centralized control approaches, but also as groups of AGVs that interact and communicate with each other and have their own controller onboard. This calls for distributed control solutions. First, a short introduction into the basic theoretical background of centralized and distributed MPC is given. Then, it comprehensively reviews MPC applications for both single and multiple AGVs. Finally, the paper highlights existing issues and future research directions, which will promote the development of MPC schemes with high performance in AGVs.

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