R. Doraiswami

A real-time fault diagnosis and parameter tracking scheme

A method for achieving the detection, isolation, and estimation of faults is presented. The systems considered are SISO systems represented by input-output models. Assuming that the model parameters are multilinear in the underlying (physical) parameters, the influence of each physical parameter on the model parameters (called the influence vector) can be interpreted as a fault template line associated with that physical parameter. The influence matrix is assumed to have been computed off-line at the nominal (fault-free) point, and stored for later use in online fault diagnosis. After a fault is detected, as indicated by a change in the identified models, the faulty physical parameter is first isolated and then estimated using the influence matrix. To avoid the use of persistently exciting test inputs required by the identification process in this parametric-model scheme, a nonparametric-model method is also developed based on the estimates of the impulse response of the system.

On-Line Identification of Unstable Processes Subject to Unbounded Disturbances

An on-line scheme for identifying stable or unstable linear time-invariant processes subject to a class of deterministic and/or stochastic disturbances is proposed. The disturbances are restricted to be the outputs of some unknown linear time-invariant system. The identification scheme consists of 1) a probing signal input which is a sum of sinusoids (exponentially increasing sum of sinusoids when the process is unstable or the disturbances are unbounded), 2) linear time -varying filters which have the properties of asymptotic stability, asymptotic signal tracking and asymptotic noise anhilation and 3) a parameter update algorithm which uses the filtered input, output data. The frequency response of the process (at the probing signal frequencies) are also estimated. The proposed scheme is verified by simulation.

Multiprocessor Fault-Tolerant Digital Control System

A design and implementation of a fault-tolerant digital control system is proposed. The system incorporates 1) analytical redundancy based on Luenberger observers to detect, identify and monitor faults which occur in the controlled process such as sensor, actuator, A/D and D/A faults and excessive load torques and 2) triple modular redundancy of processors to provide tolerance to permanent and transient failures within the control computer such as component failures and on- board communication bus faults. The digital control system is implemented with three single board computers in a shared bus, common memory configuration. The operation of the proposed fault tolerant multiprocessor system was evaluated in real-time using an analog computer to simulate the process.

Robustness of Optimally Designed Sampled Data Control Systems

Under certain conditions the robustness, with respect to stability, in an optimal sampled data control system is shown to be better than the optimal continuous time counterpart when there exists unmodelled process dynamics.

System Status Feature Extraction Based on Recursive Lattice and Block Linear Predictive Coding Algorithm

The control system status features are extracted by using fast recursive least-squares lattice (RLSL) and block linear predictive coding algorithm (LPCA), respectively. A short time record of control system signal is captured and the future trend of the system is predicted by analyzing the variations of the estimated auto regressive and moving average (ARMA) model parameters. The comparison of two methods applied in system performance monitoring with real-time data cases are given and their performance are evaluated.

Design of an Optimal Digital Controller with Control Delay

A quadratic optimal design is proposed for a digital control system in which there exists control delay. The design consists of: i) deriving a discrete-time equivalent plant model which includes the control delays, ii) defining a quadratic performance index which penalizes the plant states and control input continuously in time, iii) finding an optimal control law which minimizes the quadratic performance as a function of the sample period, T, and control delay, ¿, and iv) comparing the performance of the resulting design as a fuction of T, and ¿.

A Linear Time-Varying Control Strategy for Tracking with Disturbance and Measurement Error Rejection

A robust servomechanism control strategy is proposed for a class of stable discrete-time processes so that the tracking error is asymptotically zero independent of the disturbances. The tracking and the disturbance signals are assumed to be bounded and are solutions of some known difference equation. The controller consists of a tracking error driven servocompensator and a constant gain output feedback stabilizer. Only the frequency response of the process at the tracking and disturbance signal frequency are required to compute the stabilizer gains. In the presence of measurement noise, it is shown that asymptotic tracking is ensured if the tracking error input to the servocompensator is multiplied by a 1/k decay factor, where k is the time index.

Measures of Overshoot, Speed of Response and Robustness

Performance measures for a class of nonlinear discrete-time system are defined in terms of the eigenvalues and the eigenvectors of the linearized system matrix and an interactive computer-aided design of controller parameters is given. The overshoot and the speed of response are the parameters of an exponential envelope constraining the norm of the states under initial condition perturbations. The robustness is shown to be related to these measures. An interactive computer-aided design is proposed which consists of selecting a controller which yields a `small overshoot and `a large speed of response measures of the linearized closed loop system.