Tyrone Fernando

Closed-loop glucose control in critically ill patients using continuous glucose monitoring system (CGMS) in real time

A study was conducted to determine if continuous subcutaneous glucose monitoring (from MiniMed CGMS) could be used in real-time to control blood sugar level (BSL) in patients with critical illness. A closed-loop control system was constructed to use CGMS in a real-time manner, coupled with a proportional integral (PI) control algorithm based on a sliding scale approach, for automatic intravenous infusion of insulin to patients. A total of five subjects with high BSL (>10 mmol/L) participated in formal studies of the closed-loop control system. Subjects were recruited from critically ill patients in the intensive care unit (ICU) after informed consent was obtained. Error grid analysis showed that 64.6% of the BSL readings as determined in real time using CGMS sensor, when compared to conventional BSL measurements on blood drawn from an arterial line, was clinically accurate (i.e., <20% deviation from glucometer value). In the five patients who underwent closed-loop control, the controller managed to control only one patients glycaemia without any manual intervention. Manual intervention was required due to the real-time sensor reading deviating more than 20% from the glucometer value, and also as a safety mechanism. Test on equality of mean and variance for BSL attained prior to, during, and post trial showed that the controllers performance was comparable to manual control. We conclude that the automatic sliding scale approach of closed-loop BSL control is feasible in patients in intensive care. More work is needed in the refinement of the algorithm and the improvement of real-time sensor accuracy.

Functional observers for dynamical systems

In control theory, a state observer is an auxiliary dynamical system that mirrors the behaviour of a physical system, and it is driven by input and output measurements of the physical system in order to provide an estimate of internal states of the physical system. The primary consideration in the design of an observer is that the estimate of the states should be close to the actual value of the system states. On the other hand, the functional observation problem centers on the construction of an auxiliary dynamical system, known as the functional observer or functional reconstructor, driven by the available system inputs and outputs in order to estimate a linear function or functions of the system states. Obviously, a functional observer is a general form of the state observer because when the linear functions are chosen as the individual states of the system then the problem of functional observation reduces to the problem of state observation.

Functional Observability and the Design of Minimum Order Linear Functional Observers

The design of a minimum-order linear functional observer for linear time-invariant systems has been an open problem for over four decades. This technical note provides a solution to this problem. The technical note also introduces the concept of Functional Observability/Detectability and shows that the well-known concept of Observability/Detectability is a special case of Functional Observability/Detectability.

Disturbance Decoupled Observers for Systems With Unknown Inputs

This note deals with the design of reduced-order disturbance decoupled scalar functional observers for linear systems with unknown inputs. Based on a parametric approach, existence conditions are derived and a design procedure for finding reduced-order scalar functional observers is given. The derived existence conditions are relaxed and the procedure can find first-order disturbance decoupled scalar functional observers for some cases where the number of unknown inputs is more than the number of outputs. Also, the observer matching condition, which is the necessary requirement for the design of state observers for linear systems with unknown inputs, is not required. Numerical examples are given to illustrate the attractiveness of the proposed design method.

Existence conditions for unknown input functional observers

This article presents necessary and sufficient conditions for the existence and design of an unknown input Functional observer. The existence of the observer can be verified by computing a nullspace of a known matrix and testing some matrix rank conditions. The existence of the observer does not require the satisfaction of the observer matching condition (i.e. Equation (16) in Hou and Muller 1992, ‘Design of Observers for Linear Systems with Unknown Inputs’, IEEE Transactions on Automatic Control, 37, 871–875), is not limited to estimating scalar functionals and allows for arbitrary pole placement. The proposed observer always exists when a state observer exists for the unknown input system, and furthermore, the proposed observer can exist even in some instances when an unknown input state observer does not exist.

An Approach for Wind Power Integration Using Demand Side Resources

Renewable energy resources, especially wind power, are expected to provide a considerable portion of the world energy requirements in the near future. Large-scale wind power penetration impacts the electricity industry in many aspects and raises a number of technical challenges for the electricity network. A day-ahead network-constrained market clearing formulation is proposed which considers demand side resources. The proposed approach can provide flexible load profile and reduce the need for ramp up/down services by the conventional generators. This method can potentially facilitate a large penetration of wind power by shifting the wind power generation from the off-peak periods to the high-peak hours. The validity of the proposed approach has been verified using the IEEE 30 bus and 57 bus test systems.

Existence Conditions for Functional Observability From an Eigenspace Perspective

Two theorems on conditions for nonexistence and for existence, of built functional observers, from an eigenspace perspective are presented and proved. One more theorem on Functional Observability in terms of constructed products of matrices A,C and L0 is also presented. This theorem provides an easy way to check Functional Observability before proceeding with the design of functional observers. The existence and the nonexistence theorems are used to unify previously reported theorems on Functional Observability by showing their equivalence. The connection between the concept of Functional Observability and the well known concept of State Observability is also presented.

Partial-State Observers for Nonlinear Systems

This note deals with the design of reduced-order observers for a class of nonlinear systems. The order reduction of the observer is achieved by only estimating a required partial set of the state vector. Necessary and sufficient conditions are derived for the existence of reduced-order observers. An observer design procedure based on linear matrix inequalities is given. A numerical example is given to illustrate the design method

Time-Delay Systems: Design of Delay-Free and Low-Order Observers

This note provides a comprehensive treatment on the design of functional observers for linear systems having a time-varying delay in the state variables. The designed observers possess attractive features of being low-order and delay-free and hence they are cost effective and easy to implement. Existence conditions are derived and a design procedure for finding low-order observers is given.

Particle Filter Approach to Dynamic State Estimation of Generators in Power Systems

This paper presents a novel particle filter based dynamic state estimation scheme for power systems where the states of all the generators are estimated. The proposed estimation scheme is decentralized in that each estimation module is independent from others and only uses local measurements. The particle filter implementation makes the proposed scheme numerically simple to implement. What makes this method superior to the previous methods which are mainly based on the Kalman filtering technique is that the estimation can still remain smooth and accurate in the presence of noise with unknown changes in covariance values. Moreover, this scheme can be applied to dynamic systems and noise with both Gaussian and non-Gaussian distributions.