Qijun Xia

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.

Optimal control for large-scale systems: a recursive approach

A recursive fixed-point-type method is presented for the study of the optimal control problem of large-scale systems. The control is obtained by decomposition of the system to ‘e coupled’ subsystems so that only low-order systems are involved in algebraic computations. It is shown that the developed reduced-order parallel algorithms converge to the desired solution with the rate O(e). Owing to its recursive nature, the presented method produces a considerable saving of computation. An illustrative numerical example is given to verify the proposed approach.

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>>

Fault-tolerant control of paper machine headboxes

Abstract This paper presents a model-based fault detection and fault-tolerant control technique for the pressurized headbox of a paper machine. A bank of Kalman filters is constructed corresponding to all the possible sensor failure modes. The possibility that each failure mode hypothesis is true is calculated using measurement innovation processes. The sensor failures are detected and located based on the calculated possibilities of the hypotheses. The controller and state estimator are automatically reorganized subsequent to the occurrence of failures to ensure the stability and good performance of the closed-loop system. The issues of system hardware redundancy and computational burden as well as implemental complexity are taken into account in the system design. Simulation results have shown satisfactory performance of the headbox control system after applying the presented technique.

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.

Adaptive control of basis weight and moisture content for a paperboard machine

Abstract The development and industrial application of a MIMO adaptive control strategy for a paperboard machine are investigated. The control strategy incorporates a conventional regulatory control technique, multivariable k-incremental predictor and self-tuning control algorithm. The pulp consistency and flow rate, and the steam pressure are simultaneously manipulated to control the reel basis weight and moisture content. The control system demonstrates a satisfactory performance. The variations in reel basis weight and moisture content are greatly reduced.

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.

Systematic modeling and decoupling control of a pressurized headbox

This paper investigates the modeling and decoupling control of a pressurized headbox in Procter and Gamble Cellulose (now Weyerhaeuser Canada). An systematic modeling method which integrates process mechanism analyses and bump tests is introduced. The coupling degree of the process is analysed and the best variable paring is selected. A feasible decoupling scheme is developed and its implementation on TDC-3000 system is addressed.<<ETX>>

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>>

Composite control of discrete singularly perturbed systems with stochastic jump parameters

Abstract In this paper, a singular perturbation approach is presented to study discrete systems with stochastic jump parameters. The feedback controller design is decomposed into the design of slow and fast controllers which are combined to form the composite control. The multirate control structure allows the designer to accomodate multiple information rates and to implement required control computations. Conditions for complete separation of slow and fast regulator designs are given. It is shown that the composite feedback control is O(e) close to the optimal one, which yields an O(e2) approximation of optimal performance.