Data-based mechanistic modelling, forecasting, and control.

This article briefly reviews the main aspects of the generic data based mechanistic (DBM) approach to modeling stochastic dynamic systems and shown how it is being applied to the analysis, forecasting, and control of environmental and agricultural systems. The advantages of this inductive approach to modeling lie in its wide range of applicability. It can be used to model linear, nonstationary, and nonlinear stochastic systems, and its exploitation of recursive estimation means that the modeling results are useful for both online and offline applications. To demonstrate the practical utility of the various methodological tools that underpin the DBM approach, the article also outlines several typical, practical examples in the area of environmental and agricultural systems analysis, where DBM models have formed the basis for simulation model reduction, control system design, and forecastin

Clustering of tethered satellite system simulation data by an adaptive neuro-fuzzy algorithm

Recent developments in neuro-fuzzy systems indicate that the concepts of adaptive pattern recognition, when used to identify appropriate control actions corresponding to clusters of patterns representing system states in dynamic nonlinear control systems, may result in innovative designs. A modular, unsupervised neural network architecture, in which fuzzy learning rules have been embedded is used for on-line identification of similar states. The architecture and control rules involved in Adaptive Fuzzy Leader Clustering (AFLC) allow this system to be incorporated in control systems for identification of system states corresponding to specific control actions. We have used this algorithm to cluster the simulation data of Tethered Satellite System (TSS) to estimate the range of delta voltages necessary to maintain the desired length rate of the tether. The AFLC algorithm is capable of on-line estimation of the appropriate control voltages from the corresponding length error and length rate error without a priori knowledge of their membership functions and familarity with the behavior of the Tethered Satellite System

Nonlinear Estimation and Control Methods for Mechanical and Aerospace Systems under Actuator Uncertainty

Air flow velocity field control is of crucial importance in aerospace applications to prevent the potentially destabilizing effects of phenomena such as cavity ow oscillations, flow separation, flow-induced limit cycle oscillations (LCO) (flutter), vorticity, and acoustic instabilities. Flow control is also important in aircraft applications to reduce drag in aircraft wings for improved flight performance. Although passive flow control approaches are often utilized due to their simplicity, active flow control (AFC) methods can achieve improved flight performance over a wider range of time-varying operating conditions by automatically adjusting their level of control actuation in response to real-time sensor measurements. Although several methods for AFC have been presented in recent literature, there remain numerous challenges to be overcome in closed-loop nonlinear AFC design. Additional challenges arise in control design for practical systems with limited onboard sensor measurements and uncertain actuator dynamics. In this thesis, robust nonlinear control methods are developed, which are rigorously proven to achieve reliable control of fluid flow systems under uncertain, time-varying operating conditions and actuator model uncertainty. Further, to address the practical control design challenges resulting from sensor limitations, this thesis research will investigate and develop new methods of sliding mode estimation, which are shown to achieve finite-time state estimation for systems with limited onboard sensing capabilities. The specific contributions presented in this thesis include: 1) the application of proper orthogonal decomposition (POD)-based model order reduction techniques to develop simplified, control-oriented mathematical models of actuated fluid flow dynamic systems; 2) the rigorous development of nonlinear closed-loop active flow control techniques to achieve asymptotic regulation of fluid flow velocity fields; 3) the design of novel sliding mode estimation and control methods to regulate fluid flow velocity fields in the presence of actuator uncertainty; 4) the design of a nonlinear control method that achieves simultaneous fluid flow velocity control and LCO suppression in a flexible airfoil; and 5) the analysis of a discontinuous hierarchical sliding mode estimation method using a differential inclusions-based technique

Parametric Macromodels of Digital I/O Ports

This paper addresses the development of macromodels for input and output ports of a digital device. The proposed macromodels consist of parametric representations that can be obtained from port transient waveforms at the device ports via a well established procedure. The models are implementable as SPICE subcircuits and their accuracy and efficiency are verified by applying the approach to the characterization of transistor-level models of commercial devices

Nonlinear state and parameter estimation and Model Predictive Control of nitrogen purification columns

High-purity distillation columns are highly nonlinear systems. Nonlinear Model Predictive Control of these columns is challenging. The complexity of a first-principles model of a multi-stage distillation column, which involves a large number of differential and algebraic equations, makes Model Predictive Control computationally expensive. This thesis has focused on Nonlinear Model Predictive Control based on a simplified wave model of a distillation column; the simplified wave model consists of only one ordinary differential equation along with several algebraic equations. The predictive capability of the nonlinear wave model can be improved significantly through the application of nonlinear state estimation. Off-line estimation by the least-squares method is explored to determine what parameters should be estimated to well replicate the composition profiles. The Principal Component Analysis technique is utilized to determine the number and locations of measurements that can best represent the changes of the estimated parameters over a wide range of operating conditions. The state estimator is combined with a nonlinear model predictive controller that regulates the overhead composition by adjusting the wave position via manipulation of the feed flow rate. The differential equation for the wave position includes parameters such as the wave slope and the upper limit of the vapor composition profiles that vary according to the operating conditions. These two parameters and the wave position are estimated with an extended Kalman filter designed from a simplified wave model in which the dynamics of the combined reboiler/condenser are neglected. Satisfactory reconstruction of the composition profiles produced by the rigorous dynamic simulator is achieved if the overhead vapor composition, bottoms liquid composition, and two intermediate tray liquid compositions (chosen for their sensitivity) are selected as measured outputs. A nonlinear model predictive controller is designed from the simplified wave model by utilizing the EKF state and parameter estimates to obtain an accurate estimate of the current wave parameters. The effectiveness of the NMPC strategy is evaluated for output set point changes where a rigorous dynamic simulator is used as to represent the nitrogen column

M[pi]log, Macromodeling via parametric identification of logic gates

This paper addresses the development of computational models of digital integrated circuit input and output buffers via the identification of nonlinear parametric models. The obtained models run in standard circuit simulation environments, offer improved accuracy and good numerical efficiency, and do not disclose information on the structure of the modeled devices. The paper reviews the basics of the parametric identification approach and illustrates its most recent extensions to handle temperature and supply voltage variations as well as power supply ports and tristate devices

Locally-Stable Macromodels of Integrated Digital Devices for Multimedia Applications

This paper addresses the development of accurate and efficient behavioral models of digital integrated circuits for the assessment of high-speed systems. Device models are based on suitable parametric expressions estimated from port transient responses and are effective at system level, where the quality of functional signals and the impact of supply noise need to be simulated. A potential limitation of some state-of-the-art modeling techniques resides in hidden instabilities manifesting themselves in the use of models, without being evident in the building phase of the same models. This contribution compares three recently-proposed model structures, and selects the local-linear state-space modeling technique as an optimal candidate for the signal integrity assessment of data links. In fact, this technique combines a simple verification of the local stability of models with a limited model size and an easy implementation in commercial simulation tools. An application of the proposed methodology to a real problem involving commercial devices and a data-link of a wireless device demonstrates the validity of this approac

Second-Order Fault Tolerant Extended Kalman Filter for Discrete Time Nonlinear Systems

As missing sensor data may severely degrade the overall system performance and stability, reliable state estimation is of great importance in modern data-intensive control, computing, and power systems applications. Aiming at providing a more robust and resilient state estimation technique, this paper presents a novel second-order fault-tolerant extended Kalman filter estimation framework for discrete-time stochastic nonlinear systems under sensor failures, bounded observer-gain perturbation, extraneous noise, and external disturbances condition. The failure mechanism of multiple sensors is assumed to be independent of each other with various malfunction rates. The proposed approach is a locally unbiased, minimum estimation error covariance based nonlinear observer designed for dynamic state estimation under these conditions. It has been successfully applied to a benchmark target-trajectory tracking application. Computer simulation studies have demonstrated that the proposed second-order fault-tolerant extended Kalman filter provides more accurate estimation results, in comparison with traditional first- and second-order extended Kalman filter. Experimental results have demonstrated that the proposed second-order fault-tolerant extended Kalman filter can serve as a powerful alternative to the existing nonlinear estimation approaches