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ABSTRACT: A novel measurement method of temperature model for bioreactor has been proposed. Temperature is the key parameter in monitoring
the bioreactor operation. However, the system input signal of bioreactor is delayed, and model parameters are uncertain, so
the output of temperature is non-steady-state. Many dynamic measurements are not steady so that it cannot be described by
variables constant in time. In this paper, we adopt the monopulse signal as input so that the output of the bioreactor system
is steady. This method has a powerful ability to steady the output of the bioreactor. In view of the measurement results,
it can be seen that the model dynamic measurement approaches the real process. The analytical expression of the monopulse
response for the temperature model of the bioreactor is obtained. The novel measurement approach is simple and can be easily
adopted by industry.
Keywordsmeasurement method-temperature model-monopulse response-time-variant-bioreactor
Frontiers of Electrical and Electronic Engineering in China 04/2012; 5(2):218-223.
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ABSTRACT: This paper is derived for solving a non-linear discrete-continuous systems optimal control problem by iterating on a sequence of simplified problems in discrete form. A mixed approach with a discrete cost function and continuous state variable system description is used as the basis of the design, and it is shown how the global problem can be decomposed into local subsystem problems and a coordinator within a hierarchical framework. The correct optimal solution to a real system in which model-reality difference exists can be obtained from the system model by interconnected costate prediction iterative solution. The algorithm efficiency and convergence properties are demonstrated by simulation study.
ISA Transactions 02/2008; 47(1):113-8. · 1.11 Impact Factor
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ABSTRACT: A novel nonlinear multivariate quality estimation and prediction method based on kernel partial least-squares (KPLS) was proposed in this article. KPLS is a promising regression method for tackling nonlinear problems because it can efficiently compute regression coefficients in high-dimensional feature space by means of the nonlinear kernel function. It is an efficient method for estimating and predicting quality variables in the nonlinear process by mapping data from the original space into a high-dimensional feature space. It only requires the use of linear algebra, making it as simple as linear multivariate projection methods, and it can handle a wide range of nonlinearities because of its ability to use different kernel functions. Its application results from a simple example, and real data of an industrial oil refinery factory show that the proposed method can effectively capture the nonlinear relationship among variables. In addition, it has a better estimation performance than the partial least-squares (PLS) and other linear approaches.
01/2008;
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ABSTRACT: In this paper, a novel nonlinear process monitoring and fault detection method based on kernel ICA is proposed. The Kernel ICA method is a two-phase algorithm, KPCA first spheres data and makes the data structure become as linearly separable as possible using an implicit nonlinear mapping determined by kernel. Then ICA seeks the projection directions in the KPCA whitened space, making the distribution of the projected data as non-gaussian as possible. The application to the FCCU simulated process indicates that the proposed process monitoring method based on Kernel ICA can effectively capture the nonlinear relationship in process variables. Its performance significantly outperforms monitoring method based on ICA or KPCA.
Information and Automation, 2006. ICIA 2006. International Conference on; 01/2007
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ABSTRACT: The estimation methodology of process variables usually consists of three parts: classification of process variables, gross error detection, and data reconciliation. In this paper, we proposed a modified M-estimator method for the covariance estimator which depends on the results from robust statistics to reduce the effect of the gross errors. We consider the Lagrange multipliers method and successive linearization method for nonlinear data reconciliation. Finally, the example of a coking plant is presented to illustrate the effectiveness of the revised M-estimator method and nonlinear data reconciliation methods. In this paper, the classifying, estimating, and adjusting of the process variables are based on a components balance and total flow rates balance. The comparative results of the introduced methods are given and demonstrate the successful application of the proposed method to reconcile actual plant data from a complex chemical process.
11/2006;
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ABSTRACT: In this paper, a class of unknown perturbed nonlinear systems is theoretically stabilized by using adaptive neural network control. The systems, with disturbances and nonaffine unknown functions, have low triangular structure, which generalizes both strict-feedback uncertain systems and pure-feedback ones. There do not exist any effective methods to stabilize this kind of systems. With some new conclusions for Nussbaum-Gain functions (NGF) and the idea of backstepping, semiglobal, uniformal, and ultimate boundedness of all the signals in the closed-loop is proved at equilibrium point. The two problems, control directions and control singularity, are well dealt with. The effectiveness of proposed scheme is shown by simulation on a proper nonlinear system.
IEEE Transactions on Neural Networks 04/2006; 17(2):509-14. · 2.95 Impact Factor
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ABSTRACT: A planning method of cast for steelmaking continuous casting production scheduling in CIMS is studied. The cast plan model is established. An adaptive operator genetic algorithm is proposed to solve the optimum cast plan problem. The computation with practical data shows that the model and the solving method are very effective.
Control Applications, 2004. Proceedings of the 2004 IEEE International Conference on; 10/2004
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ABSTRACT: Consider the two-machine no-wait flowshop problem where the setup and removal times of a job at each machine are separated from the processing time. The scheduling objective is to minimize the sum of the total flowtime. Two optimization properties are first developed. In view of the NP-hard of this problem, optimal solutions seem unnecessary especially when the number of jobs is large. Then, combined the developed optimization properties with the greedy search a heuristic algorithm is developed. Computational experience demonstrates the effectiveness of the heuristic algorithm in finding near optimal schedules.
Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on; 07/2004
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ABSTRACT: PID control is widely used to control stable processes, however, its application to integrating processes is less common. In this paper, we proposed a new PID controller tuning method for integrating processes with time delay to meet a new robust specification. With the proposed PID tuning method, we can obtain a loop transfer function with the real part close to -0.5. This guarantees both robustness and performance. Simulation examples are given to show the performance of the method.
Decision and Control, 2003. Proceedings. 42nd IEEE Conference on; 01/2004
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ABSTRACT: Predictive PI (PPI) control form, capable of time delay compensation, has been put forward recently. This control algorithm is essentially a PI controller with enhanced derivative action, and is not only suitable for long time delay process, but also of simple structure and excellent robust stability. The performance of PPI controller is demonstrated and compared with traditional PID controller by different tuning methods.
Decision and Control, 2003. Proceedings. 42nd IEEE Conference on; 01/2004
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Computers & Chemical Engineering. 01/2004; 28:1489-1498.
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ABSTRACT: PID control is widely used to control stable processes; however, its application to integrating processes is less common. In this paper, we proposed a simple PID controller tuning method for integrating processes with time delay to meet a new robust specification. With the proposed PID tuning method, we can obtain a loop transfer function with the real part close to -0.5. This guarantees both robustness and performance. Simulation examples are given to show the performance of the method.
American Control Conference, 2003. Proceedings of the 2003; 07/2003
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ABSTRACT: This paper proposes a novel data reconciliation method based on Kernel Principal Component Analysis (KPCA). In the proposed method, essential information of nonlinear system is extracted by KPCA. Then the data is reconstructed by small count of important nonlinear principle components, which represent essential information of nonlinear system, and noise is removed from reconciliated data. The proposed method is applied to nonlinear data reconciliation of ternary distillation column. Effective results show that proposed method provides a new method for nonlinear data reconciliation.
American Control Conference, 2003. Proceedings of the 2003; 07/2003
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ABSTRACT: Artificial neural networks (ANNs) such as radial basis function networks (RBF NNs) have been successfully used in soft sensor modeling. However, the generalization ability of conventional ANNs is not very well. For this reason, we present a novel soft sensor modeling approach based on support vector machines (SVMs). Since standard SVMs have the limitation of speed and size in training large data set, we hereby propose least squares support vector machines (LS_SVMs) and apply it to soft sensor modeling. Systematic analysis is performed and indicates that the proposed method provides satisfactory performance with excellent approximation and generalization property. Monte Carlo simulations show that our soft sensor modeling approach achieves superior performance to the conventional method based on RBF NNs.
American Control Conference, 2003. Proceedings of the 2003; 07/2003
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ABSTRACT: PID control is widely used to control stable processes; however, its application to unstable processes is less common. In this paper, we proposed a simple PID controller tuning method for unstable processes with time delay to meet a new robust specification. With the proposed PID tuning method, we can obtain a loop transfer function with the real part close to -0.5. This guarantees both robustness and performance. Simulation examples are given to show the performance of the method.
American Control Conference, 2003. Proceedings of the 2003; 07/2003
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ABSTRACT: The sufficient conditions of stability for discrete-time linear systems with time delay have been proposed by some researchers in the past few years, yet these results may be conservative in application. In this paper, the stability analysis of these systems is discussed, and the necessary and sufficient condition of stability is derived by method other than constructing Lyapunov function and solving Riccati inequality. The root locations of system characteristic polynomial, which is obtained by augmentation approach and Laplace expansion, determine the stability of discrete-time linear systems with time delay, the system being stable if and only if all roots lie within the unit circle. In order to analyze robust stability of system characteristic polynomial effectively, Kharitonov theorem and edge theorem are applied. An example shows the practicability of these methods.
American Control Conference, 2003. Proceedings of the 2003; 07/2003
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American Control Conference, 2003. Proceedings of the 2003; 02/2003
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ABSTRACT: Support vector machine (SVM) is a novel machine learning method based on statistical learning theory. SVM is a powerful tool for solving problems with small samples, nonlinearities and local minima, and is of excellent performance in classification. In the paper, the SVM nonlinear classification algorithm is reviewed. The SVM nonlinear classifier is applied to deal with fault diagnosis. SVM is easy to implement for fault diagnosis. Effective results are obtained of using the SVM for fault diagnosis.
Intelligent Control and Automation, 2002. Proceedings of the 4th World Congress on; 02/2002
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ABSTRACT: The canonical genetic algorithm (CGA) applies selection, crossover and mutation operators to solve difficult optimization problems. This paper introduces a new approach to CGA. It applies only mutation and selection operators. It is a non-crossover genetic algorithm (NCGA). The proof of global convergence of NCGA is presented in this paper. The simulation on the NP-complete traveling salesman problem (TSP) shows that NCGA is much faster than the CGA. In terms of computation efficiency, NCGA is a very promising approach. This paper casts doubt on the need of the crossover operator in GAs.
Intelligent Control and Automation, 2002. Proceedings of the 4th World Congress on; 02/2002
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ABSTRACT: A model of a walking-beam reheating furnace is constructed. The model consists of three sub-models, automatic combustion control model (ACC), dynamic model of the combustion process, and control loops model. The ACC model calculates the setpoints of furnace temperature such that the slabs in the furnace can be heated to discharging temperature. A dynamic model, which is mainly discussed in the paper, describes the behavior of the furnace under the state of rolling line and fuel flux provided by the control loop model. The control loop model, or distributed control system model control the fuel flux of each zone according to setpoints of furnace temperature and state of the furnace. The structure of the model is discussed in detail in the paper. The numerical examples show the validity of the model. The model can be used to develop new energy-saving techniques, or to realize quality optimization etc.
American Control Conference, 2002. Proceedings of the 2002; 02/2002
