M.B. Zaremba

Iterative learning control synthesis based on 2-D system theory

An algorithm is presented for iterative learning of the control input for a linear discrete-time multivariable system. Necessary and sufficient conditions are stated for convergence of the proposed algorithm. The algorithm synthesis and analysis are based on two-dimensional (2-D) system theory. A numerical example is given. >

Robust iterative learning control design is straightforward for uncertain LTI systems satisfying the robust performance condition

This note demonstrates that the design of a robust iterative learning control is straightforward for uncertain linear time-invariant systems satisfying the robust performance condition. It is shown that once a controller is designed to satisfy the well-known robust performance condition, a convergent updating rule involving the performance weighting function can be directly obtained. It is also shown that for a particular choice of this weighting function, one can achieve a perfect tracking. In the case where this choice is not allowable, a sufficient condition ensuring that the least upper bound of the /spl Lscr//sub 2/-norm of the final tracking error is less than the least upper bound of the /spl Lscr//sub 2/-norm of the initial tracking error is provided. This sufficient condition also guarantees that the infinity-norm of the final tracking error is less than the infinity-norm of the initial tracking error.

Cp-based Decision Making for Sme

Abstract Constraint programming (CP) is an emergent software technology for declarative description and effective solution of large combinatorial problems, which has proven to be useful, especially in such areas as integrated production planning. In that context, the CP can be considered as a well-suited framework for the development of decision-making software supporting small and medium size enterprises (SME) in the course of Production Process Planning (PPP). The aim of the paper is to present the CP modelling framework as well as to illustrate its application to decision making in the case of a new production order evaluation. The paper emphasises benefits derived from CP-based Decision Support Systems and focuses on constraint satisfaction driven decision making rather than on optimal solution searching.

Internal model-based robust iterative learning control for uncertain LTI systems

Investigates the combination of an iterative learning control (ILC) with an internal model control (IMC) for uncertain linear time-invariant (LTI) systems. The convergence of the iterative process is investigated and reformulated as a general robust control problem. For a certain choice of the IMC and ILC filters, we prove that the condition of convergence to zero of the iterative process is nothing but the robust performance condition of the IMC structure. Using the general robust control formulation, we propose a design procedure for the ILC-IMC filters using the /spl mu/-synthesis approach.

Exponential convergence of an iterative learning controller for time-varying nonlinear systems

This paper addresses the issue of convergence rates in the tracking problem for a class of time varying nonlinear systems, using a PD-like iterative learning algorithm. It is shown that, under certain sufficient conditions involving the tracking horizon and the system parameters, the controller guarantees exponential convergence-with respect to the iteration index k-of the infinity norm of the tracking error. As a particular case, a class of linear time-varying systems is also considered, and sufficient conditions leading to exponential convergence are derived. The theoretical analysis, confirmed by the simulation results, demonstrates that the tracking horizon plays a crucial role in the convergence rates of the learning process.

Iterative learning control for non-linear systems described by a blended multiple model representation

This paper deals with the design of gain-scheduling-based iterative learning controllers for continuous-time non-linear systems described by a blended multiple model representation. Sufficient conditions guaranteeing the convergence of the infinity norm as well as the u -norm of the tracking error are derived. The effectiveness of the proposed control scheme is illustrated on an example of a non-affine-in-input system.

Neural processing-type fiber-optic strain sensor

A neural-processing-type strain sensor insensitive to thermal variation is presented and calibration of the device through modulation of the processing systems internal parameters is described. The sensor exploits the variation of the far-field polarization pattern in a single-mode birefringent fiber under the influence of longitudinal strain. A temperature-compensating fiber element is built in, making the sensor assembly immune to thermal variation. Sampling of the sensor output and parallel distributed processing of the samples are integrated within the sensor. The processor manages both a training function and a generalization function. The training function modulates a small-size linear network built into the system. In the working phase, the generalization function is used to recover measurement information. If the sensor is thermally compensated, the network gives a reading of the measurand with an error not exceeding 0.1%. Applicability of the processing system to bimodal sensor output is also described. >

ROBUST ILC DESIGN IS STRAIGHTFORWARD FOR UNCERTAIN LTI SYSTEMS SATISFYING THE ROBUST PERFORMANCE CONDITION

This paper demonstrates that the design of a robust feedback-based Iterative Learning Control (ILC) is straightforward for uncertain linear time invariant (LTI) systems satisfying the robust performance condition. It is shown that once a controller is designed to satisfy the well known robust performance condition, a convergent updating rule involving the performance weighting function can be directly obtained. It is also shown that for a particular choice of this weighting function, one can achieve a perfect tracking.

INS/GPS Navigation Data Fusion Using Fuzzy Adaptive Kalman Filtering

Abstract In this paper, a method based on Adaptive Fuzzy Kalman Filtering has been applied to fuse position signals from the Global Positioning System (GPS) and Inertial Navigation System (INS) for the navigation of autonomous mobile vehicles. The presented method is of particular importance for guidance, navigation, and control of flying vehicles and has been validated in 3-D environments. The Extended Kalman Filter (EKF) and the noise characteristic are modified using Fuzzy Logic Adaptive System and compared with the performance of a regular EKF. It has been demonstrated that the Fuzzy Adaptive Kalman Filter gives better results in terms of accuracy than the EKF.

Predicting outcome for hospitalized cardiac patients using a combined neural network and rough set approach

Describes a hospitalization prediction system based on neural network technology. The paper focuses on predicting the following output variables identified as crucial for the purposes of the project: the length of stay in hospital, the length of stay in the intensive care unit, and the outcome of hospitalization defined as a transferred discharged or deceased patient. The general approach adopted for solving the problem consists of first applying inductive learning based on the techniques of rough sets to generate a reduced set of input data and a small set of rules specific to the output variable. The results serve to define and structure the architecture of the neural system in terms of the number of neural networks and their input variables, as well as to dynamically select the networks that best fit the type of information describing the current patient. A database of over 1000 cardiac patients, admitted to the Ottawa General Hospital over a period of 4 years was used.