Asok Ray

An observer-based compensator for distributed delays

This paper presents an algorithm for compensating delays that are distributed between the sensor(s), controller and actuator(s) within a control loop. This observer-based algorithm is specially suited to compensation of network-induced delays in integrated communication and control systems. The robustness of the algorithm relative to plant model uncertainties has been examined.

Symbolic dynamic analysis of complex systems for anomaly detection

This paper presents a novel concept of anomaly detection in complex dynamical systems using tools of Symbolic Dynamics, Finite State Automata, and Pattern Recognition, where time-series data of the observed variables on the fast time-scale are analyzed at slow time-scale epochs for early detection of (possible) anomalies. The concept of anomaly detection in dynamical systems is elucidated based on experimental data that have been generated from an active electronic circuit with a slowly varying dissipation parameter.

Integrated Communication and Control Systems: Part II—Design Considerations

Asynchronous time-division multiplexed networks, used in Integrated Communication and Control Systems (ICCS), introduce time-varying and possibly stochastic delays in the feedback control loops. The objective of this on-going research is to develop a comprehensive methodology for the analysis and design of the above class of delayed control systems. In the first part [1] of this two-part paper, we developed a discrete-time, finite-dimensional, time-varying model of the delayed control system; necessary and sufficient conditions for system stability have been established for periodically varying delays. This second part elucidates the significance of the above model relative to the system dynamic performance as well as addresses major criteria for and outlines alternative analytical approaches to ICCS design. Pertinent concepts are illustrated by simulation.

Output Feedback Control Under Randomly Varying Distributed Delays

An output feedback control law has been formulated in a stochastic setting, based on the principles of minimum variance filtering and dynamic programming, for application to processes that are subjected to randomly varying distributed delays. The proposed estimation and control law for delay compensation is built on the concept of the conventional linear quadratic Gaussian (LQG), called delay compensated linear quadratic Gaussian (DCLQG). Although the certainty equivalence property of LQG does not hold for DCLQG in general, the combined state estimation and state feedback approach of DCLQG offers a suboptimal solution to the control problem under randomly varying distributed delays. DCLQG is potentially applicable to analysis and synthesis of control systems for vehicle management of future generation aircraft where a computer network is employed for distributed processing and on-line information exchange between diverse control and decision-making functions. Results of simulation experiments are presented to demonstrate efficacy of the proposed DCLQG algorithm for flight control of an advanced aircraft.

Experimental verification of a delay compensation algorithm for integrated communication and control systems

Advances in the technology of complex control systems demand high-speed and reliable communications between the individual components and subsystems for decision making and control. This can be accomplished by integrated communication and control systems which use asynchronous time-division-multiplexed networks. Unfortunately, these networks introduce randomly varying distributed delays as a result of time-division multiplexing. A predictor-controller algorithm has been developed with the objective of mitigating the detrimental effects of the network-induced delays that are distributed between the sensor(s), controller and actuator(s) within a control loop. This paper presents the implementation and verification of the above delay compensation algorithm. Performance of the delay compensator has been experimentally verified on an IEEE 802.4 network testbed for velocity control of a d.c. servomotor.

Stochastic modeling of fatigue crack dynamics for on-line failure prognostics

This paper presents a nonlinear stochastic model of fatigue crack dynamics for real-time computation of the time-dependent damage rate and accumulation in mechanical structures. The model configuration allows construction of a filter for estimation of the current damage state and prediction of the remaining service life based on the underlying principle of extended Kalman filtering instead of solving the Kolmogorov forward equation. This approach is suitable for online damage sensing, failure prognosis, life prediction, reliability analysis, decision-making for condition-based maintenance and operation planning, and life extending control in complex dynamical systems. The model results have been verified by comparison with experimentally generated statistical data of time-dependent fatigue cracks in specimens made of 2024-T3 aluminum alloy.

LINEAR UNBIASED STATE ESTIMATION UNDER RANDOMLY VARYING BOUNDED SENSOR DELAY

The motivation for the work reported in this paper accrues from the necessity of finding stabilizing control laws for systems with randomly varying bounded sensor delay. It reports the de- velopment of reduced-order linear unbiased estimators for discrete-time stochastic parameter systems and shows how to parametrize the estimator gains to achieve a certain estimation error covariance. Both finite-time and steady-state estimators are considered. The results are potentially applicable to state estimation for stabilizing output feedback control systems. (~) 1998 Elsevier Science Ltd. All rights reserved.

State Estimation Using Randomly Delayed Measurements

This paper presents a modification of the conventional minimum variance state estimator to accommodate the effects of randomly varying delays in arrival of sensor data at the controller terminal. In this approach, the currently available sensor data is used at each sampling instant to obtain the state estimate which, in turn, can be used to generate the control signal. Recursive relations for the filter dynamics have been derived, and the conditions for uniform asymptotic stability of the filter have been conjectured

Review and comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns

Symbolic dynamic filtering (SDF) has been recently reported in literature as a pattern recognition tool for early detection of anomalies (i.e., deviations from the nominal behavior) in complex dynamical systems. This paper presents a review of SDF and its performance evaluation relative to other classes of pattern recognition tools, such as Bayesian Filters and Artificial Neural Networks, from the perspectives of: (i) anomaly detection capability, (ii) decision making for failure mitigation and (iii) computational efficiency. The evaluation is based on analysis of time series data generated from a nonlinear active electronic system.

Linear unbiased state estimation for random models with sensor delay

The motivation for the work reported in this paper accrues from the necessity of finding stabilizing control laws for systems with randomly varying distributed delays. It reports the development of full and reduced order linear unbiased estimators for discrete-time stochastic parameter systems and shows how to parametrize the estimator gains to achieve a certain estimation error covariance. Both finite-time and steady-state estimators are considered. The results are potentially applicable to state-estimate feedback control schemes for such systems.