Yiqun Ying

Adaptive fading Kalman filter with an application

Abstract A new adaptive state estimation algorithm, namely adaptive fading Kalman filter (AFKF), is proposed to solve the divergence problem of Kalman filter. A criterion function is constructed to measure the optimality of Kalman filter. The forgetting factor in AFKF is adaptively adjusted by minimizing the defined criterion function using measured outputs. The algorithm remains convergent and tends to be optimal in the presence of model errors. It has been successfully applied to the headbox of a paper-making machine for state estimation.

A new state estimation algorithm-adaptive fading Kalman filter

A novel adaptive state estimation algorithm, namely the adaptive fading Kalman filter (AFKF), is proposed to solve the divergence problem of the Kalman filter. A criterion function is constructed to measure the optimality of the Kalman filter. The forgetting factor in the adaptive fading Kalman filter is adaptively adjusted by minimizing the defined criterion function using measured outputs. The algorithm achieves optimality and convergence simultaneously. The filter uses a variable exponential weighting approach to compensate the model errors and unknown drifts. This algorithm has been successfully applied to the headbox of a paper-making machine for state estimation.<<ETX>>

Bilinear Decoupling Control and Its Industrial Application

In this paper, the state feedback decoupling control for bilinear systems is discussed. The sufficient condition for the existence of decoupling controller and the design procedure are given. A real world example, the headbox control of a paper machine, demonstrates the efficiency of the proposed method.

Bilinear state-disturbance composite observer and its application

ABSTRACT Like linear systems, the state and disturbance observers for bilinear systems must be considered in order to realize the state feedback and/or disturbance feedforward control in the case where some of the states and/or disturbances are unmeasurable. This paper first addresses the design issue of minimal order state observers for bilinear systems; then discusses a new method of designing minimal order state-disturbance composite observers. Finally, the application of this state-disturbance composite observer to the headbox control system in the papermaking process is presented as an example. The simulation results and on-line performance evaluation for this composite observer are discussed.

Fault-Tolerant Control

The increasing complexity of modern engineering production systems and requirements for high quality products have created a need for control systems with fault-tolerance. This chapter presents two algorithms for fault-tolerance analysis and fault-tolerant controller design. In Section 6.2, a model-based fault detection and fault-tolerant control technique for a pressurized headbox is presented. The sensor failures are detected and then located. The controller and state estimator are automatically reorganized subsequently to the occurrence of the failures to ensure the stability and acceptable performance of the closed loop system. In Section 6.3, a linear quadratic optimal system with the highest fault-tolerance is developed for the drying section. Quantitative relationship between fault-tolerance and controller designing parameters are established. An iterative algorithm is proposed to design control system with the highest fault-tolerance.

New technique for decoupling control

Abstract A new technique for decoupling controller design is proposed. A reference model representing the desired closed-loop dynamic behaviour is chosen. A quadratic performance index is used to measure both coupling and tracking errors in the closed-loop system. The system state equation is organized in incremental canonical form. The optimal decoupling control law is obtained by minimizing the performance index. This algorithm has been applied to a paper machine headbox control system. The performance of the system under different conditions is satisfactory.

Near-optimum regulators for singularly perturbed systems with random parameters

A singular perturbation approach is presented to study systems that are subject to random parameters. A near-optimum state regulator is composed of two subsystem regulators (slow and fast regulators) with the matrix coefficients obtained from two reduced-order independent suboptimal control problems. The conditions for complete separation of slow and fast regulator designs are given. A recursive algorithm for the design of regulators with random parameters is also given. It is shown that the composite feedback control is O( epsilon ) close to the optimal one, which yields an O( epsilon /sup 2/) approximation of optimal performance.<<ETX>>

Decomposition Method for Solving the Gains of Kalman Filter in Singularly Perturbed Systems

In this paper, a decomposition method is introduced to get the solution of the optimal gains of Kalman filters in singularly perturbed systems by solving two reduced order linear equations. The decomposition is achieved via the use of the Changs transformation applied to the Hamiltonian matrix of the singularly perturbed kalman filters. Since the decoupling transformation can be obtained, up to an arbitrary degree of accuracy at very low cost, this approach produces an efficient numerical method for solving the gains of Kalman filters. A numerical example is given to demonstrate the efficiency of the method.

IOMCS for Pulp and Paper Processes

The previous nine chapters present a number of control algorithms and their applications to unit operation of paper machines. The growing complexity of industrial processes and the need for higher efficiency, great flexibility, better product quality, lower cost and environment protection have changed the face of industrial practice. Mill wide information management, decision-making automation and operation support have become very crucial for the modern pulp and paper company to stay competitive internationally. Artificial intelligence can play an important role in attaining the above goal. Considering that most of the nowadays modern pulp and paper mills have successfully installed distributed computer control systems (DCS) and information management systems, it is of very significant economic benefit to improve the existing systems by adding “intelligence” and enhancing functionality. This chapter presents an intelligent on-line monitoring and control system (IOMCS) for pulp and paper processes. IOMCS is a real-time intelligent system which links with DCS and a mill wide information system. It takes advantage from the DCS value-added data in the information management system. The system fulfills functions such as monitoring the process for abnormal situations; advising evasive and corrective operation actions to operators; pulp quality prediction; and operation optimization. Unlike in the previous chapters, we will not limit our attention to paper machines, but to whole pulp and paper processes. The techniques proposed in this chapter are applicable to paper machines.

Process Dynamics and Modeling

Paper machines are very complex processes. Identification models, sometimes, cannot meet operational requirements. It is very difficult to develop paper machine dynamic models solely by theoretical mechanism analysis. In this chapter, a systematic modeling method for paper machines is proposed, which combines mechanism analysis with experimental method. The model structure and part of model parameters are determined from mechanism analysis. The remaining model parameters are obtained from experimental data. The modeling process for a pressurized headbox and a paper machine which produces condenser tissue (with an open headbox) is presented.