Huihe Shao

A linear matrix inequality (LMI) approach to robust H/sub 2/ sampled-data control for linear uncertain systems

In this paper, we consider the H/sub 2/ sampled-data control for uncertain linear systems by the impulse response interpretation of the H/sub 2/ norm. Two H/sub 2/ measures for sampled-data systems are considered. The robust optimal control procedures subject to these two H/sub 2/ criteria are proposed. The development is primarily concerned with a multirate treatment in which a periodic time-varying robust optimal control for uncertain linear systems is presented. To facilitate multirate control design, a new result of stability of hybrid system is established. Moreover, the single-rate case is also obtained as a special case. The sampling period is explicitly involved in the result which is superior to traditional methods. The solution procedures proposed in this paper are formulated as an optimization problem subject to linear matrix inequalities. Finally, we present a numerical example to demonstrate the proposed techniques.

Delay-dependent dissipative control for linear time-delay systems

Abstract This paper deals with the problem of delay-dependent dissipative control for a class of linear time-delay systems. We develop the design methods of dissipative static state feedback and dynamic output feedback controllers such that the closed-loop system is quadratically stable and strictly ( Q , S , R )-dissipative. Sufficient conditions for the existence of the quadratic dissipative controllers are obtained by using linear matrix inequality (LMI) approach. Furthermore, a procedure of constructing such controllers from the solutions of LMIs is given. It is shown that the solvability of a dissipative controller design problem is implied by the feasibility of LMIs. The main results of this paper unify the existing results on H ∞ control and passive control.

Designing a soft sensor for a distillation column with the fuzzy distributed radial basis function neural network

A soft sensor is a model which is used to estimate the unmeasurable output of an industrial process, it is very useful in process control because it can be used to control and monitor many industrial processes. But designing a soft sensor is usually difficult because its modeling is often based on case data. These data have the features of discreteness, nonlinearity, contradiction, and complexity. In this paper, modeling based on case data is defined as a case based modeling problem. In order to solve the case based modeling, problem and successfully design a sort sensor, this paper constructs a kind of fuzzy distributed radial basis function neural network. The fuzzy distributed RBP neural network is easy to solve the case based modeling problem. In this paper, it is applied in designing a soft sensor for a high purity distillation column. The simulation is based on the actual operation data and analysis data of the distillation column. The results show that the fuzzy distributed RBF neural network based soft sensor has good performance. Thus, the fuzzy distributed RBF neural network has successfully solved the case based modelling problem. It is very promising in process control.

Application of support vector machine nonlinear classifier to fault diagnoses

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.

An ANN's evolved by a new evolutionary system and its application

The paper presents a new evolutionary system for evolving artificial neural networks (ANN). In the process of evolution, the network architecture and the node weights of ANN are evolved alternately, and the evolution value of network architecture is related to the error value of ANN evolved by node weights. An evolved ANN has been used in modelling product quality estimator for a fractionator of the hydrocracking unit in the oil refining industry. The results show that it has good accuracy and generalisation ability.

PI tuning for processes with large dead time

In this paper, a simple PI controller tuning method for processes with large dead time is proposed. Firstly, a first-order plus dead time model is identified based on relay feedback experiment, which Nyquist plot is extremely close to that of the real processes with large dead time over the frequency range of /spl omega//sub 0//spl sim//spl omega//sub c/. Then PI controller are designed based on sensitivity specification. Simulation examples show the effectiveness and feasibility of the proposed PI tuning method in handling processes with large dead time.

Sampled-data control for time-delay systems

Abstract The sampled-data systems are hybrid ones involving continuous time and discrete time signals, which makes the traditional analysis and synthesis methodologies of time-delay systems unable to be directly used in the cases of hybrid systems with time-delay. The primary disadvantages of current design techniques of sampled-data control are their inabilities to deal effectively with time-delay and the model uncertainty. In this paper, we generalized the analysis methodology of time-delay systems to that of the hybrid systems with time-delay and uncertainty, which developed a design procedure of sampled-data control for time-delay systems. Asymptotic stability of the time-delay hybrid systems was developed. The time-delay dependent robust sampled-data control for the time-varying delay of an uncertain linear system was then discussed. The results were described as linear matrix inequalities, which can be solved using newly released LMITool.

A novel method of PID tuning for integrating processes

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.

A Novel Systematic Method of Quality Monitoring and Prediction Based on FDA and Kernel Regression

Abstract A novel systematic quality monitoring and prediction method based on Fisher discriminant analysis (FDA) and kernel regression is proposed. The FDA method is first used for quality monitoring. If the process is under normal condition, then kernel regression is further used for quality prediction and estimation. If faults have occurred, the contribution plot in the fault feature direction is used for fault diagnosis. The proposed method can effectively detect the fault and has better ability to predict the response variables than principle component regression (PCR) and partial least squares (PLS). Application results to the industrial fluid catalytic cracking unit (FCCU) show the effectiveness of the proposed method.

An Improved Fuzzy Predictive Control Algorithm and Its Application to an Industrial CSTR Process

A finite horizon predictive control algorithm, which applies a saturated feedback control law as its local control law, is presented for nonlinear systems with time-delay subject to input constraints. In the algorithm, N free control moves, a saturated local control law and the terminal weighting matrices are solved by a minimization problem based on linear matrix inequality (LMI) constraints online. Compared with the algorithm with a nonsatu-rated local law, the presented algorithm improves the performances of the closed-loop systems such as feasibility and optimality. This model predictive control (MPC) algorithm is applied to an industrial continuous stirred tank reactor (CSTR) with explicit input constraint. The simulation results demonstrate that the presented algorithm is effective.