International Journal of System Control and Information Processing
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International Journal of System Control and Information Processing (3 papers in press)
A joint data-driven process monitoring method using knowledge propagation based on manifold clustering by Chuanfang Zhang, Kaixiang Peng, Jie Dong Abstract: Compared with existing process monitoring approaches, a joint data-driven method using knowledge propagation based on manifold clustering is proposed for fault detection, which utilizes historical data containing knowledge information (labeled data). The main contributions of this work are as follows: (1) Two transformation matrices are derived based on manifold learning and clustering method. (2) Different from conventional data-driven fault detection method, knowledge propagation based on manifold clustering is used to extract the features of unlabeled data. (3) According to extracted features, the fault detection approach is proposed. The proposed method is applied to Tennessee Eastman (TE) process. The simulation results indicate that the proposed monitoring scheme can effectively monitor the working conditions of the process and identify fault types. Keywords: Joint data-driven; Process monitoring; Fault detection; Manifold clustering; Knowledge propagation.
Multi-batch two-dimension iterative learning PID control based on Markov parameter tuning method by Yanping Liu, Cuimei Bo, Jun Li, Junhua Yin, Shuqi Tang Abstract: Aiming at the characteristics of semi-batch reaction process, a two-dimension iterative learning PID control algorithm based on Markov tuning method is improved for temperature control system. Under the framework of two- dimension system, considering the repeatability in the batch direction and the dynamic characteristics in the time direction, a two-dimension iterative learning PID control method (2D-ILC-PID) is designed. Furthermore the controller parameters are tuned by Markov tuning method to improve the converging rate of the 2D-ILC-PID controller. An ideal semi-batch reaction model is used to test the control scheme, simulation results show that 2D-ILC-PID can effectively overcome the several different types of disturbances, and the parameters obtained by Markov tuning method can make the tracking process converge faster. Keywords: Batch process; Iterative learning; Markov tuning method; 2D-ILC-PID controller?Semi-batch reaction.
Parameter optimization research for hydraulic turbine regulating system based on CGABC algorithm by Xiaoxia Tan, Jing Chen, Gonggui Chen Abstract: Artificial bee colony (ABC) algorithm is regularly taken in the parameter optimization of the hydraulic turbine regulating system (HTRS). The chaotic and global ABC (CGABC) algorithm is proposed to overcome the slow convergence speed and low convergence precision that existed in standard ABC algorithm. Among them, global optimal is used to enhance the global exploration ability, and chaos optimization is adopted to increase the diversity of bee colony. The simulation results show that compared with fuzzy strategy, ZN, DE and ABC algorithm, the proposed CGABC algorithm significantly improves the dynamic transition process of HTRS. Simultaneously, the simulation experiment is carried out on HTRS with different governor parameters and controlled parameters when it is under the frequency and load disturbance condition. The results show that the dynamic characteristics and robustness is determined by the parameter selection, and the appropriate combination of parameters allows HTRS to achieve optimal dynamic performance and robustness. Keywords: Hydraulic turbine regulating system; CGABC algorithm; parameter optimization; frequency and load disturbance condition; dynamic performance; robustness.