Data-driven Soft Sensors in the process industry

Online adaptive least squares support vector machine and its application in utility boiler combustion optimization systems

This paper presents a hybrid control strategy, combining Radial Basis Function (RBF) network with conventional proportional, integral, and derivative (PID) controllers, for the greenhouse climate control. A model of nonlinear conservation laws of enthalpy and matter between numerous system variables affecting the greenhouse climate is formulated. RBF network is used to tune and identify all PID gain parameters online and adaptively. The presented Neuro-PID control scheme is validated through simulations of set-point tracking and disturbance rejection. We compare the proposed adaptive online tuning method with the offline tuning scheme that employs Genetic Algorithm (GA) to search the optimal gain parameters. The results show that the proposed strategy has good adaptability, strong robustness and real-time performance while achieving satisfactory control performance for the complex and nonlinear greenhouse climate control system, and it may provide a valuable reference to formulate environmental control strategies for actual application in greenhouse production.

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Nonlinear Adaptive PID Control for Greenhouse Environment Based on RBF Network

An integrated predictive model with an on-line updating strategy for iron precipitation in zinc hydrometallurgy

https://ir.nctu.edu.tw:443/bitstream/11536/22388/1/000321407800004.pdf

Original article: Optimized FPGA design, verification and implementation of a neuro-fuzzy controller for PMSM drives

Radial basis function (RBF) neural network (NN) is powerful computational tools that have been used extensively in the areas of pattern recognition, systems modeling and identification. This paper proposes an adaptive PID control method based on RBF neural network identification. This approach can on-line identify the controlled plant with the RBF neural network identifier and the weights of the adaptive PID controller are adjusted timely based-on the identification of the plant and self-learning capability of RBFNN. Simulation result shows that the proposed controller has the adaptability, strong robustness and satisfactory control performance in the nonlinear and time varying system

Adaptive PID control based on RBF neural network identification

Support vector machines (SVM) is a novel machine learning method based on small-sample statistical learning theory (SLT), and is powerful for the problem with small sample, nonlinearity, high dimension, and local minima.SVM have been very successful in pattern recognition ,fault diagnoses and function estimation problems. Least squares support vector machines (LS-SVM) is an SVM version which involves equality instead of inequality constraints and works with a least squares cost function. This paper discusses least squares support vector machines (LS-SVM) estimation algorithm and introduces applications of the novel method for the nonlinear control systems. Then identification of MIMO models and soft-sensor modeling based on least squares support vector machines (LS-SVM) is proposed. The simulation results show that the proposed method provides a powerful tool for identification and soft-sensor modeling and has promising application in industrial process applications

Ming-guang Zhang

Papers

Adaptive PID control based on RBF neural network identification

Study on least squares support vector machines algorithm and its application