M. Rao

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.

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.

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.

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.

Robust estimation and compensation for actuator and sensor failures in linear systems

A computationally feasible technique for the robust detection and estimation for actuator and sensor failures is presented. Model errors and component failures are represented by a bias vector called the failure state in system and measurement equations. A Kalman-Bucy filter is implemented to estimate the system state, and to generate the corresponding residuals. These residuals are then processed by using an adaptive fading Kalman filter to give the failure state estimate. The final state estimate is obtained by compensating model errors and component failures in the filter based on no failure assumption. The divergency of the filter based on no failure assumption is avoided by stepwise compensation of the failure state. The technique is applicable to the detection, estimation and compensation of slowly varying model errors and suddenly occurring component failures, and to the discrimination between them

Intelligent Design of Actuator and Sensor for Emergency Support Systems

Abstract A method for the design of sensor and actuator placement for emergency support systems is proposed. The objective of the design is to find the sets of sensors and actuators which satisfy the requirements of fault diagnosis and emergency handling, meanwhile minimize the cost of the control devices. The requirements for actuators and sensors in emergency support systems are clearly defined. A fault graph which represents the causal relations between process faults and measurements is developed. An “objective tree” that represents the control structure in emergency situations is introduced. The design is performed based on the fault graph and the “objective tree”. Detail example for the bleach plant of a pulp mill demonstrated.

The Multiple Performance Optimizing Index for Feedback Controller

It is shown that a good optmization performance index of a system should have three properties: representability, calculability, and compatibility. In this paper, a functional optimizing index is given, which really has representability, calculability, and compatibility, and can be used for designing gain state feedback, gain output feedback, or dynamic compensator feedback to improve multiple performance of the control system simultaneously, such as closed-loop system robust stability, global regulation performance, H¿-insensitivity, and the location of pole placement.

Robust Failure Detection, Estimation and Compensation in Linear Systems

1 Failure model of linear systems Kalman filters can not work well in the cases where the systems include significan t model errors and com pone n t failu res. A filter which is able to detect and compensate the failures is required. Two failure detection techniques [1][2] have beeni developed using the separated-bias estimation algorithm [3]. In this paper, the separated-bias estimation algorithm is combined with the adaptive fading Kalman filter [4] to give a simple and effective detection technique for actuator and sensor failures. The failure states (biases) represent component failures when some failure occurs, otherwise represent model errors. Consider a linear, discrete time, stochastic system

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.