David T Westwick

• PhD (1995, McGill University)
• Professor at University of Calgary

Tube thoracostomy (chest tube insertion) is a surgical procedure that treats pneumothorax, a potentially life-threatening condition where air accumulates between the chest wall and the lungs. The literature reports high complication rates for this procedure, including accidental fatality due to poor manual depth control during tool insertion. We hy...

The real-time identification of low-frequency electromechanical oscillations within interconnected power systems is of significant interest due to its vital role in assessing the system’s stability status. These oscillations are often comprised of multiple modes, each with distinct frequencies and damping characteristics. This study introduces a me...

This paper presents a novel control mechanism based on the Internal Model Principle (IMP) to mitigate low-frequency electromechanical oscillations in power systems. By identifying and incorporating the dynamic behaviors of oscillations into the feedback loop, the proposed method effectively eliminates disturbances, enhancing system stability and pe...

Traditionally, the placement and routing stages of a physical design are performed separately. Because of the additional complexities arising in advanced technology nodes, they have become more interdependent. Therefore, creating efficient cooperation between the routing and placement steps has become an important topic in Electronic Design Automat...

Astronauts are at risk for pneumothorax, a condition where injury or disease introduces air between the chest wall and the lungs (i.e., the pleural cavity). In a worst-case scenario, it can rapidly lead to a fatality if left unmanaged and will require prompt treatment in situ if developed during spaceflight. Chest tube insertion is the definitive t...

Tube thoracostomy (TT), where a tube is placed between the lungs and the chest wall to drain air and/or fluids introduced by injury or disease, is a crucial procedure with both routine and emergent applications. It has high complication rates (up to 37.9%) [1], particularly for residents [2] and emergency physicians [3]. Inadver- tent tissue punctur...

Serious complications during chest tube insertion are relatively rare, but can have catastrophic repercussions. We propose semi-automating tool insertion to help protect against non-target tissue puncture, and report first steps collecting and characterizing needle-tissue interaction forces in a tissue phantom used for chest tube insertion training...

System identification uses measurements of a dynamic system's input and output to reconstruct a mathematical model for that system. These can be mechanical, electrical, physiological, among others. Since most of the systems around us exhibit some form of nonlinear behavior, nonlinear system identification techniques are the tools that will help us...

Nonlinear Auto-Regressive eXogenous input (NARX) models are a popular class of nonlinear dynamical models. Often a polynomial basis expansion is used to describe the internal multivariate nonlinear mapping (P-NARX). Resorting to fixed basis functions is convenient since it results in a closed form solution of the estimation problem. The drawback, h...

Nonlinear Auto-Regressive eXogenous input (NARX) models are a popular class of nonlinear dynamical models. Often a polynomial basis expansion is used to describe the internal multivariate nonlinear mapping (P-NARX). Resorting to fixed basis functions is convenient since it results in a closed form solution of the estimation problem. The drawback, h...

A common process control application is the cascaded two-tank system, where the level is controlled in the second tank. A nonlinear system identification approach is presented in this work to predict the model structure parameters that minimize the difference between the estimated and measured data, using benchmark datasets. The general suggested s...

System identification uses measurements of a dynamic system’s input and output to reconstruct a mathematical model for that system. These can be mechanical, electrical, physiological, among others. Since most of the systems around us exhibit some form of nonlinear behavior, nonlinear system identification techniques are the tools that will help us...

Nonlinear Auto-Regressive eXogenous input (NARX) models are a popular class of nonlinear dynamical models. Often a polynomial basis expansion is used to describe the internal multivariate nonlinear mapping (P-NARX). Resorting to fixed basis functions is convenient since it results in a closed form solution of the estimation problem. The drawback, h...

In the overview paper on nonlinear system identification Schoukens and Ljung (2019), it was indicated that reliable expressions to calculate the variance of an estimated nonlinear model are lacking, especially if the disturbing noise is entering the nonlinear regressors. In this study, we provide a better view on the driving mechanisms of the varia...

The decoupling algorithm proposed by Dreesen et al. (SIAM J. Matrix Anal. Appl., 2015) was developed for MIMO polynomials. Polynomial based models are commonly used in nonlinear system identification despite their poor extrapolation behaviour and the often poor numerical conditioning of the resulting estimation problems. Several alternatives have b...

In this work, a generic impedance modeling technique is proposed. The technique is able to identify a circuit model that is most suitable for fitting measured impedance magnitude data using a genetic algorithm solver as well as the optimum circuit model parameters. Experimentally measured and simulated data sets with different noise levels are used...

The Nonlinear Auto-regressive eXogenous input (NARX) model has been widely used in nonlinear system identification. It’s chief disadvantages are that it is a black-box model that suffers from the curse of dimensionality, in that the number of parameters increases rapidly with the nonlinearity degree. One approach to dealing with these problems invo...

The observer/Kalman filter identification (OKID) is an algorithm widely used for the identification of state space models. The standard OKID algorithm involves the estimation of the Kalman filter and system Markov parameters, followed by the realization of a state space model of the system using the eigensystem realization algorithm (ERA). In this...

The polynomial NARX model, where the output is a polynomial function of past inputs and outputs, is a commonly used equation error model for nonlinear systems. While it is linear in the variables, which simplifies its identification, it suffers from two major drawbacks: the number of parameters grows combinatorially with the degree of the nonlinear...

The legalization step is performed after global placement where wire length and routability are optimized or during timing optimization where buffer insertion or gate sizing are applied to meet timing requirements. Therefore, an ideal legalization approach must preserve the quality of the input placement in terms of routability, wire length, and ti...

This paper addresses both the design of an optimal variable setpoint and a setpoint-tracking control loop for the dissolved oxygen concentration in a biological wastewater treatment process. Although exact knowledge of influent changes during rain/storm events is unrealistic, we take advantage of the fact that during dry weather conditions the infl...

The polynomial NARX model, where the output is a polynomial function of past inputs and outputs, is a commonly used equation error model for nonlinear systems. While it is linear in the variables, which simplifies its identification, it suffers from two major drawbacks: the number of parameters grows combinatorially with the degree of the nonlinear...

Abstract: This paper proposes a Lyapunov-based adaptive controller with fuzzy setpoint regulator for biological wastewater treatment. In such systems biological organisms remove unwanted substances including nitrates, ammonia, and organic material. The feedback controls manipulate aeration and flow rates in order to affect the growth rate of the bi...

This paper proposes a method for designing and tracking an optimal set point for a biological wastewater treatment, where the set point changes in real time in order to respond to changing influent disturbance. The objectives are to minimize energy consumption even while meeting or exceeding effluent quality standards, even during extreme weather e...

Factoring the third-order Volterra kernel of a Wiener-Hammerstein model to recover the impulse responses of its two constituent linear systems is a common example in the multilinear algebra literature. Since recent progress in regularization-based system identification has enabled the practical estimation of the third-order Volterra kernel, these t...

Providing flexibility and user-interpretability in nonlinear system identification can be achieved by means of block-oriented methods. One of such block-oriented system structures is the parallel Wiener-Hammerstein system, which is a sum of Wiener-Hammerstein branches, consisting of static nonlinearities sandwiched between linear dynamical blocks....

The main motivation for this paper is to improve an acoustic leak detection system for pipelines by using blind source separation. In this setup hundreds of microphones are used to continuously monitor a pipeline. We propose to use a source separation scheme to eliminate overlapping sounds in the measured signals making is easier to detect and loca...

Providing flexibility and user-interpretability in nonlinear system identification can be achieved by means of block-oriented methods. One of such block-oriented system structures is the parallel Wiener-Hammerstein system, which is a sum of Wiener-Hammerstein branches, consisting of static nonlinearities sandwiched between linear dynamical blocks....

Many engineering optimization problems include unavoidable uncertainties in parameters or variables. Ignoring such uncertainties when solving the optimization problems may lead to inferior solutions that may even violate problem constraints. Another challenge in most engineering optimization problems is having different conflicting objectives that...

The placement problem has become more complex and challenging due to a wide variety of complicated constraints imposed bymodern process technologies. Some of the most challenging constraints and objectives were highlighted during the most recent ACM/IEEE International Symposium on Physical Design (ISPD) contests. In this article, the framework of E...

This paper presents a generalized predictive control (GPC) technique to regulate the activated sludge process found in a bioreactor used in wastewater treatment. The control strategy can track dissolved oxygen setpoint changes quickly, adapting to the system uncertainties and disturbances. Tests occur on an Activated Sludge Model No. 1 benchmark of...

Clock buffer and wire sizing are intertwined problems that also greatly impact power consumption and skew in clock trees. Due to their complexity, they are often solved separately, leading to suboptimal solutions. In this brief, we propose a new formulation for cooptimization of buffer and wire sizes for high-performance clock trees. Using the prop...

In this paper, a Prediction Error based algorithm is developed for the identification of a Wiener system, a linear dynamic subsystem followed by a static non-linearity, in the presence of a non-stationary disturbance added before the static non-linearity. This structure represents a non-stationary process disturbance, as is common in chemical proce...

The torque and angle data used to identify models of limb impedance are usually generated under closed-loop conditions. In many cases, the joint position is controlled using a powerful position servo, so that the joint interacts with a very stiff environment. Under these conditions, the data may be treated as if they had been obtained in open-loop...

In this paper, a continuous stirred tank reactor (CSTR) is identified in closed loop using a direct prediction error based approach. This common process control system is represented by Hammerstein model, a memoryless non-linearity in cascade with a linear dynamic subsystem. In direct closed-loop identification, the process and noise models are ide...

This paper presents a generalized predictive control (GPC) to regulate the dissolved oxygen concentration in an activated sludge process. Firstly, an unconstrained GPC controls the dissolved oxygen concentration in the presence of white measurement noise. However, adjusting to influent changes is more important in practice than rejecting white nois...

In this paper, a continuous stirred tank reactor (CSTR) is identified in closed loop using a direct prediction error based approach. This common process control system is represented by Hammerstein model, a memoryless non-linearity in cascade with a linear dynamic subsystem. In direct closed-loop identification, the process and noise models are ide...

In this paper, an algorithm is developed for the identification of a Hammerstein system in the presence of non-stationary measurement noise in the form of an Auto Regressive Integral Moving Average (ARIMA) model. Many systems used in the chemical process control industry can be modelled with the Hammerstein structure, a block oriented model consist...

In this paper, an algorithm is developed for the identification of a Hammerstein system in the presence of non-stationary measurement noise in the form of an Auto Regressive Integral Moving Average (ARIMA) model. Many systems used in the chemical process control industry can be modelled with the Hammerstein structure, a block oriented model consist...

This paper looks at the problem of controlling an incinerator that burns waste gas to generate power. The system is modelled as a standard utility boiler using one known and one unknown (waste) fuel input. Standard linear controls have trouble dealing with large variations in the waste input, and in practice boiler shutdowns can occur. In this work...

Steam generators offer a challenging control problem, exhibiting significant nonlinearities in both state and output equations. Here we consider a boiler heated by waste-gas incineration, modeled as a standard utility boiler using one known and one unknown (waste) fuel input. A nonlinear adaptive control design accounts for uncertainty in the plant...

In this paper, we present and analyze four efficient models that produce significantly improved results by optimizing conflicting power and skew objectives in the clock network buffer sizing problem. Each model is in geometric programming format and has certain advantages, such as maximum reduction in power, robustness to process variation, and str...

In this work, a non-iterative identification approach is presented for estimating a Single Input Single Output (SISO) Wiener model, comprising an Infinite Impulse Response (IIR) discrete transfer function followed by static non-linearity. Global Orthogonal Basis Functions and orthogonal Hermite polynomials are used as expansion bases for the linear...

This paper extends the algorithms used to fit standard support vector machines (SVMs) to the identification of auto-regressive exogenous (ARX) input Hammerstein models consisting of a SVM, which models the static nonlinearity, followed by an ARX representation of the linear element. The model parameters can be estimated by minimizing an ε-insensiti...

In this paper, a non-iterative method for identifying a single input single output Wiener model consisting of an IIR digital filter in cascade with a potentially non-invertible static non-linearity is developed. Initially, the linear and nonlinear elements are expanded onto bases consisting of IIR Laguerre filters and polynomials, respectively. Ext...

Most engineering problems involve optimizing different and competing objectives. To solve multi-objective problems, normally a weighted sum of the objectives is optimized. However, how the weights are assigned can greatly affect the outcome. Therefore, many designers have to resort to producing the Pareto surface - a time-consuming procedure. In th...

Placement is a stage in the design of digital circuits where the locations of the circuit components are determined, while minimizing the total length of wires connecting them. A priori individual length estimates can be used to improve the quality of a placement solution. However, finding such estimates is a daunting task. A technique based on Rad...

Minimizing power and skew for clock networks are critical and difficult tasks which can be greatly affected by buffer sizing. However, buffer sizing is a non-linear problem and most existing algorithms are heuristics that fail to obtain a global minimum. In addition, existing buffer sizing solutions do not usually consider manufacturing variations....

Microelectrode arrays (MEA) are non-invasive tools for recording brain cell activity and have been successfully applied to a variety of neurons. However, MEAs fail where consistent stimulation of neurons is desired over an extended period of time. Here, a model is presented to study features that provide optimum stimulation threshold from different...

In this paper, we present a self-tuning multi-objective framework for geometric programming that provides a fine trade-off between the competing objectives. The significance of this framework is that the designer does not need to perform any tuning of weights of objectives. The proposed framework is applied to gate sizing and clock network buffer s...

In this paper, the Hammerstein identification problem with correlated inputs is studied in a prediction error framework using separable least squares methods. Thus, the identification is recast as an optimization over the parameters used to describe the nonlinearity. A sufficient condition is derived that guarantees that the identification problem...

Microelectrode arrays (MEA) are non-invasive tools for recording brain cell activity and have been successfully applied to a variety of neurons. However, MEAs fail where consistent stimulation of neurons is desired over an extended period of time. Here, a model is presented to study features that provide optimum stimulation threshold from different...

This paper presents a design of an advanced adaptive controller for coker-off-gas boilers. We aim to achieve a high level of performance and stability, even in the presence of poorly-modeled nonlinear effects and unmeasured input waste-fuel gas. The proposed control uses adaptive parameters in the nonlinear control, and a neural network to compensa...

The iterative optimizations often used to identify Wiener–Hammerstein models, pairs of linear filters separated by memoryless nonlinearities, require good initial estimates of the linear elements in order to avoid them getting caught in local minima. Previous work has shown that initial estimates of the two linear elements can be formed by splittin...

Stimulating microelectrode arrays will most likely be used in future neuroprosthetic devices as well as for the treatment of neurological disorders. These electrodes should be configured to stimulate neurons for long periods of time without causing neural damage. Optimizing neural stimulation within the physiological ranges, however, requires desig...

Estimates of joint or limb impedance are commonly used in the study of how the nervous system controls posture and movement, and how that control is altered by injury to the neural or musculoskeletal systems. Impedance characterizes the dynamic relationship between an imposed perturbation of joint position and the torques generated in response. Whi...

This paper presents an algorithm for the identification of Hammerstein cascades with hard nonlinearities. The nonlinearity of the cascade is described using a B-spline basis with fixed knot locations; the linear dynamics are described using a state-space model. The algorithm automatically estimates both the order of the linear system and the number...

The parameters of a Wiener-Hammerstein model, a nonlinear block structure comprising two linear filters separated by a memoryless nonlinearity, may be identified using an iterative nonlinear least squares optimization, however avoiding suboptimal local minima in the error surface requires a good initial estimate of the parameter vector. The Best Li...

Reliable induction motor modeling is critical in power system planning and operation. This paper considers the identifiability of induction motor parameters, with a particular emphasis placed on using subset selection and shrinkage methods to allow the identification methods to focus on the most significant parameters. The proposed approach is vali...

Clustering algorithms have been used to improve the speed and quality of placement. Traditionally, clustering focuses on the local connections between cells. In this paper, a new clustering algorithm that is based on the estimated lengths of circuit interconnects and the connectivity is proposed. In the proposed algorithm, first an a priori length...

When approximating dynamic systems with Laguerre basis functions (LBFs) it is important to tune the Laguerre pole such that the expansion can be both parsimonious and accurate. Expressing the sum of squared errors (SSE) as a function of the Laguerre pole leads to an objective function that has many local minima and therefore cannot be optimized dir...

Joint stiffness, the dynamic relationship between the angular position of a joint and the torque acting about it, describes the dynamic, mechanical behavior of a joint during posture and movement. Joint stiffness arises from both intrinsic and reflex mechanisms, but the torques due to these mechanisms cannot be measured separately experimentally, s...

Microelectrode array (MEA) is a noninvasive method for recording brain cell activity and has been successfully applied to an array of neurons. However, using MEAs to consistently stimulate neurons over an extended period of time has been less successful and continues to be problematic. In order to optimize the neuron stimulation in a physiological...

The ARX structure is often used in linear and nonlinear system identification because it is compact and linear in the variables. However, the ARX structure includes a noise model which shares the same poles as the deterministic system which is not always appropriate. In this paper, we consider the extension of an SVM based identification technique...

This paper presents an approach for the identification of a Wiener model, a dynamic linear system followed by a static nonlinearity, in the presence of colored measurement noise. A Box-Jenkins model structure is proposed where the process model consists of a recursive digital filter followed by a polynomial nonlinearity, while the noise model is re...

This paper presents some of the experiences gained from the interdisciplinary design course offered at the university of Calgary in the 2004-2005 academic year. It also provides a few proposals and recommendations to improve the course (or similar versions) in the future. The components of the course—lecture content, group structure, design project...

A multivariable control strategy based on model predictive control techniques for the control of variable-speed variable-pitch wind turbines is proposed. The proposed control strategy is described for the whole operating region of the wind turbine, i.e., both partial and full load regimes. Pitch angle and generator torque are controlled simultaneou...

The force and position data used to construct models of limb impedance are often obtained from closed-loop experiments. If the system is tested in a stiff environment, it is possible to treat the data as if they were obtained in open loop. However, when limb impedance is studied in a compliant environment, the presence of feedback cannot be ignored...

A multivariable control strategy based on model predictive control techniques for the control of variable-speed variable pitch wind energy conversion systems (WECSs) in the above-rated wind speed zone is proposed. Pitch angle and generator torque are controlled simultaneously to provide optimal regulation of the generated power and the generator sp...

The Hammerstein identification problem is studied using a prediction error method in a separable least squares framework. Thus, the identification is recast as an optimization over the parameters used to describe the nonlinearity. Under certain conditions the identification problem is quasiconvex. First, the identification problem is shown to be qu...

Nonlinear system identification has a long history in several disciplines related to biomedical engineering. Much of this can be credited to the seminal textbook written by Marmarelis and Marmarelis [25], which popularised the use of the cross-correlation method for estimating the Wiener kernels of a system driven by a white Gaussian noise input [2...

A multivariable control strategy based on model predictive control techniques for the control of variable speed variable pitch wind turbines is proposed. The proposed control strategy is described for the whole operating region of the wind turbine (both partial and full load regimes). Pitch angle and generator torque are controlled simultaneously t...

Joint stiffness is often represented by a parallel cascade model. The present study proposes a new approach to identify the parameters of such model structures from nonlinear frequency response functions. At first, a harmonic probing technique is used to derive the linear and higher-order frequency response functions (called the generalized frequen...

When approximating dynamic systems with Laguerre Basis Functions it is important to tune the Laguerre pole such that the expansion is both parsimonious and accurate. The sum of squared error (SSE) objective function has many local minima and therefore cannot be optimized directly. Two alternative objective functions have been proposed in the litera...

Subset selection and shrinkage methods locate and remove insignificant terms from identified models. The least absolute shrinkage and selection operator (Lasso) is a term selection method that shrinks some coefficients and sets others to zero. In this paper, the incorporation of constraints (such as Lasso) into the linear and/or nonlinear parts of...

The present study proposes a new approach to identify the parameters of both continuous and discrete time Hammerstein systems in frequency domain. A harmonic probing technique is used to derive the linear and higher-order frequency response functions (called the generalized frequency response functions (GFRF)) of both discrete and continuous-time H...

In this work, an algorithm that identifies Hammerstein models with support vector machine nonlinearities and output-error linear dynamics is proposed. This algorithm is used to identify a Hammerstein model of stretch reflex EMG dynamics from experimental data.

Methods for the identification of Hammerstein models consisting of a Support Vector Machine nonlinearity followed by an ARX linear system are developed. The models are identified by minimizing epsilon insensitive cost functions based on either the sum of absolute residuals or the sum of squared residuals. Large scale implementations of these techni...

An identification algorithm for a power system load model is proposed in this paper. The overall non-convex identification problem is separated into convex and non-convex subproblems, allowing for a global optimum to be found.Numerical experiments using data from both simulated and physical systems illustrate the accuracy of the proposed algorithm....

This paper presents a simple near constant bandwidth amplifier constructed from two operational amplifiers. The near constant bandwidth is obtained by reducing the normally high input impedance of the opamp via local and overall feedback. Experimental results obtained using identical opamps and different opamps verify the expected theoretical resul...

Radar-based microwave imaging techniques have been proposed for early stage breast cancer detection. A considerable challenge for the successful implementation of these techniques is the reduction of clutter, or components of the signal originating from objects other than the tumor. In particular, the reduction of clutter from the late-time scatter...

This work uses historical data from existing wind generation facilities as a sample of a larger future population of wind generation to create realistic long term time series of future wind generation scenarios. Models are created that reasonably capture both the deterministic and random characteristics of wind generation that are locally unique. O...

Heffron-Phillips model of a synchronous machine is commonly used in small signal stability analysis and for off-line design of power system stabilisers. The data used to determine the parameters of this model are either hard to measure or require the machine to be taken off-line to take the measurements which, in general, is inconvenient. identifyi...

This paper presents a new algorithm for identification of NARX Hammerstein systems using support vector machines (SVMs) to model the static nonlinear elements. The SVM is fitted by minimizing an ε-insensitive, L-1 cost function which is robust in the presence of outliers. Another advantage of this algorithm is that the value of the uncertainty leve...

This paper presents a new algorithm for identification of NARX Hammerstein systems using support vector machines (SVMs) to model the static nonlinear elements. The SVM is fitted by minimizing an e-insensitive, L-1 cost function which is robust in the presence of outliers. Another advantage of this algorithm is that the value of the uncertainty leve...