Kostas Tsakalis

Adaptive control of linear time-varying plants: a new model reference controller structure

The problem of developing a control law which can force the output of a linear time-varying plant to track the output of a stable linear time-invariant reference model is discussed. It is shown that the standard model reference controller, used for linear time-invariant plants, cannot guarantee zero tracking error in general when the plant is time-varying. A new model reference controller is proposed which guarantees stability and zero tracking error for a general class of linear time-varying plants with known parameters. When the time-varying plant parameters are unknown but vary slowly with time, it is shown that the new controller can be combined with a suitable adaptive law so that all the signals in the closed loop remain bounded for any bounded initial conditions and the tracking error is small in the mean. The assumption of slow parameter variations in the adaptive case can be relaxed if some information about the frequency or the form of the fast varying parameters is available a priori. Such information can be incorporated in an appropriately designed adaptive law so that stability and improved tracking performance is guaranteed for a class of plants with fast varying parameters. >

CONTROL OF SYNCHRONIZATION OF BRAIN DYNAMICS LEADS TO CONTROL OF EPILEPTIC SEIZURES IN RODENTS

We have designed and implemented an automated, just-in-time stimulation, seizure control method using a seizure prediction method from nonlinear dynamics coupled with deep brain stimulation in the centromedial thalamic nuclei in epileptic rats. A comparison to periodic stimulation, with identical stimulation parameters, was also performed. The two schemes were compared in terms of their efficacy in control of seizures, as well as their effect on synchronization of brain dynamics. The automated just-in-time (JIT) stimulation showed reduction of seizure frequency and duration in 5 of the 6 rats, with significant reduction of seizure frequency (>50%) in 33% of the rats. This constituted a significant improvement over the efficacy of the periodic control scheme in the same animals. Actually, periodic stimulation showed an increase of seizure frequency in 50% of the rats, reduction of seizure frequency in 3 rats and significant reduction in 1 rat. Importantly, successful seizure control was highly correlated with desynchronization of brain dynamics. This study provides initial evidence for the use of closed-loop feedback control systems in epileptic seizures combining methods from seizure prediction and deep brain stimulation.

Adaptive Control of Linear Time-Varying Plants

Abstract In this paper we consider the model reference adaptive control (MRAC) problem of a class of linear time-varying (LTV) plants. The plant parameters are assumed to be smooth, bounded functions of time which satisfy the usual assumptions of MRAC for time-invariant plants, at each frozen time instant. We first show that if the plant parameters are sufficiently slowly-varying with time, a control parameter vector with smooth elements exists, such that the closed loop plant behaves almost like a linear time invariant reference model. We then use the robust adaptive law proposed in Ioannou and Tsakalis (1985) to adjust the controller parameters and establish boundedness for all signals in the adaptive loop for any bounded initial conditions. The bound for the residual tracking error depends on the speed of the plant parameter variations in such a way that as these parameters become constant the bound reduces to zero.

Autoregressive modeling and feature analysis of DNA sequences

A parametric signal processing approach for DNA sequence analysis based on autoregressive (AR) modeling is presented. AR model residual errors and AR model parameters are used as features. The AR residual error analysis indicate a high specificity of coding DNA sequences, while AR feature-based analysis helps distinguish between coding and noncoding DNA sequences. An AR model-based string searching algorithm is also proposed. The effect of several types of numerical mapping rules in th proposed method is demonstrated.

Adaptive control of linear time-varying plants

Abstract In this paper the model reference adaptive control (MRAC) problem of a class of linear time-varying (LTV) plants is considered. The plant parameters are assumed to be smooth, bounded functions of time which satisfy the usual assumptions of MRAC for time-invariant plants, at each frozen time instant. It is first shown that if the plant parameters are sufficiently slowly varying with time, a control parameter vector with smooth elements exists, such that the closed-loop plant behaves almost like a linear time-invariant reference model. The robust adaptive law proposed in Ioannou and Tsakalis (1986) IEEE Trans. Aut. Control, AC-31, 1033 is then used to adjust the controller parameters and establish boundedness of all signals in the adaptive loop for any bounded initial conditions. It is shown that the bound for the residual tracking error depends on the speed of the plant parameter variations in such a way that as these parameters become constant the bound reduces to zero.

Integrated system identification and PID controller tuning by frequency loop-shaping

A systematic design methodology for proportional-integral-derivative (PID) controllers is presented. Starting from data sets, a model of the system and its uncertainty bounds are obtained. The parameters of the controller are tuned by a convex optimization algorithm, minimizing a weighted difference between the actual loop transfer function and a target in an /spl Lscr//sub 2///spl Lscr//sub /spl infin// sense. The target selection is guided by the identified model and its uncertainty. The problem of disjoint data sets and/or different models for the same system is also addressed. The method has proved successful in numerous practical cases showing both expediency in controller design and implementation and improved performance over existing controllers.

Monitoring and control of semiconductor manufacturing processes

Concerns optical measurement techniques for semiconductor manufacturing process monitoring and control. They can provide previously impossible real-time monitoring of several process variables, in-situ or ex-situ. This further enables the applications of more sophisticated real-time control algorithms, other than SPC. The SPC-based run-to-run (RtR) control, on the other hand, is still an instrumental part of the control algorithm, whenever there is a lack of real-time sensors for measuring critical process metrics. An emerging practice is to integrate RtR with the real-time control design to provide a comprehensive control design algorithm for semiconductor manufacture. The continued trend of semiconductor industry is toward an bigger wafers and smaller devices. This requires more integrated supervisory control, able to provide even tighter control, especially for photolithography, CVD, and etch processes. A unified framework needs to be established, at least for these critical processes, to facilitate and expedite systematic control design and development. In addition, as more sophisticated algorithms are being implemented, the control-related software and hardware should be user-friendly so that it can be operated by nonexpert personnel. Finally, since chip manufacturing consists of various processes, a comprehensive control algorithm on a factory-wide basis should utilize information (process-state, wafer-state, and tool-state data) from the current process as well as upstream and downstream processes.

Robust Discrete-Time Adaptive Control

This paper proposes a discrete-time model reference adaptive control algorithm which is robust with respect to additive plant uncertainties. The algorithm employs the same controller structure as in [1] but a different adaptive law for adjusting the controller parameters. If the plant uncertainty is “small” the algorithm guarantees the boundedness of all signals in the adaptive loop and “small” residual tracking errors for any bounded initial conditions. In the absence of plant uncertainties the algorithm guarantees zero residual tracking errors.

PID controller tuning by frequency loop-shaping: application to diffusion furnace temperature control

A frequency loop-shaping technique for tuning proportional-integral-derivative controllers is applied to temperature control of a three-zone industrial diffusion furnace used in semiconductor manufacturing. In this tuning method the controller parameters are tuned so as to minimize the difference between the actual and a target loop transfer function, in an /spl Lscr//sub /spl infin// sense. The problem is formulated in the frequency domain as a convex optimization which can be solved numerically in a reliable way. This method, combined with a system identification step, has produced very satisfactory controllers for the furnace temperature control application.

Hierarchical modeling and control of re-entrant semiconductor manufacturing facilities

This paper addresses hierarchical modeling and control issues within a modern semiconductor fabrication facility. It is well known that fabs are appropriately modeled via discrete event systems. Such models, however, are difficult to analyze and even more difficult to use for design purposes. In this paper a new method for modeling the fab is presented. The method leads to models which approximate the fab over small time scales. These models provide synchronous discrete-time approximations which may be useful for analysis and control design. They also provide a natural tool for systematically addressing aggregation/de-aggregation issues. It is also shown how the fab can be approximated by a high-level flow model. Such a model is useful for making high-level long term decisions, determining realistic commands for low-level tracking policies, and for assessing achievable performance. A low-level tracking policy is presented and integrated with a high-level state variable feedback policy. The low-level tracking policy is shown to track low-frequency commands generated by the high-level controller.