Electrical and Electronic Engineering - Research Publications
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ItemNo Preview AvailableSHAPELET BASED VISUAL ASSESSMENT OF CLUSTER TENDENCY IN ANALYZING COMPLEX UPPER LIMBMOTION(IEEE, 2021)The evolution of ubiquitous sensors has led to the generation of copious amounts of waveform data. Human motion waveform analysis has found significance in clinical and home-based activity monitoring. Exploration of cluster structure in such waveform data prior to developing learning models is an important pattern recognition problem. A prominent category of algorithms in this direction, known as Visual Assessment of (cluster) Tendency (VAT), employs visual approaches to study cluster evolution through heat maps. This paper proposes shape-iVAT, a new relative of an improved VAT model, that captures local time-series characteristics through representative subsequences, known as shapelets, to identify interesting patterns in motion data. We propose an unsupervised method for shapelet extraction using maximin shape sampling and shape-based distance computation for selecting key shapelets representing characteristic motion patterns. These shapelets are used to transform waveform data into a dissimilarity matrix for VAT evaluation. We demonstrate that the proposed method outperforms standard VAT with global distance measures for identifying complex upper limb motion captured using a camera-based motion sensing device. We also show that our method has significance in efficient and interpretable cluster tendency assessment for anomaly detection and continuous motion monitoring.
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ItemRapid Nonovershooting Control for Simultaneous Infusion of Anesthetics and Analgesics(ELSEVIER, 2021)We propose a rapid nonovershooting tracking controller for the continuous infusion of anesthetics and analgesics to prevent overdosing and other harmful side effects on patients. The controller utilizes a state feedback control design methodology for multi-input multi-output systems to achieve a closed-loop eigenstructure that yields a nonovershooting transient response. The method is combined with a global optimization method to achieve a rapid nonovershooting response. The controller uses an extended Kalman filter to estimate system states from measurable outputs, and integral control is added to achieve robust tracking. The performance of the method is simulated on 20 patient models in two groups, and the results are compared against another recent study from the biomedical control literature.
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ItemPreface(Open Publishing Association, 2021-09-13)
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ItemDirect Predictive Boundary Control of a First-order Quasilinear Hyperbolic PDE(IEEE, 2020-03-12)We present a method for the boundary control of a system governed by one hyperbolic PDE with a non-local coupling term by state feedback. The method is an extension of recently developed controllers for semilinear systems. The design consists of three steps: predicting the states up to the time when they are affected by the delayed input; virtually moving the input to the uncontrolled boundary (which makes characterizing stability trivial); and constructing the inputs by, starting with the desired boundary values at the uncontrolled boundary, solving an ODE governing the dynamics on the system's characteristic lines backwards in time. The controller steers the system to the origin in finite time. A discussion of potential extensions of the presented method is given.
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ItemOptimization based input preview filtering for dynamical systems(IEEE, 2020-03-12)This paper is about filtering uncertain forecast information to update a preview model of inputs to a linear dynamical system, as may be useful in predictive control schemes. A moving horizon optimization approach is proposed, with a view to smoothing abrupt changes in order based forecast information and to manage error, given observations of the dynamics. Numerical examples are used to illustrate a potential application of this approach within the context of processing demand profile requests in a water distribution system.
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ItemMeasures of Bipedal Toe-Ground Clearance Asymmetry to Characterize Gait in Stroke Survivors.(IEEE, 2021-11)Post-stroke hemiparesis often impairs gait and increases the risks of falls. Low and variable Minimum Toe Clearance (MTC) from the ground during the swing phase of the gait cycle has been identified as a major cause of such falls. In this paper, we study MTC characteristics in 30 chronic stroke patients, extracted from gait patterns during treadmill walking, using infrared sensors and motion analysis camera units. We propose objective measures to quantify MTC asymmetry between the paretic and non-paretic limbs using Poincaré analysis. We show that these subject independent Gait Asymmetry Indices (GAIs) represent temporal variations of relative MTC differences between the two limbs and can distinguish between healthy and stroke participants. Compared to traditional measures of cross-correlation between the MTC of the two limbs, these measures are better suited to automate gait monitoring during stroke rehabilitation. Further, we explore possible clusters within the stroke data by analysing temporal dispersion of MTC features, which reveals that the proposed GAIs can also be potentially used to quantify the severity of lower limb hemiparesis in chronic stroke.
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ItemExploiting homogeneity for the optimal control of discrete-time systems: Application to value iteration(IEEE, 2021)To investigate solutions of (near-)optimal control problems, we extend and exploit a notion of homogeneity recently proposed in the literature for discrete-time systems. Assuming the plant dynamics is homogeneous, we first derive a scaling property of its solutions along rays provided the sequence of inputs is suitably modified. We then consider homogeneous cost functions and reveal how the optimal value function scales along rays. This result can be used to construct (near-)optimal inputs on the whole state space by only solving the original problem on a given compact manifold of a smaller dimension. Compared to the related works of the literature, we impose no conditions on the homogeneity degrees. We demonstrate the strength of this new result by presenting a new approximate scheme for value iteration, which is one of the pillars of dynamic programming. The new algorithm provides guaranteed lower and upper estimates of the true value function at any iteration and has several appealing features in terms of reduced computation. A numerical case study is provided to illustrate the proposed algorithm.
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ItemObserving the Slow States of General Singularly Perturbed Systems(IEEE, 2020)This paper studies the behaviour of observers for the slow states of a general singularly perturbed system - that is a singularly perturbed system which has boundary-layer solutions that do not necessarily converge to a slow manifold. The solutions of the boundary-layer system are allowed to exhibit persistent (e.g. oscillatory) steady-state behaviour which are averaged to obtain the dynamics of the approximate slow system. It is shown that if an observer has certain properties such as asymptotic stability of its error dynamics on average, then it is practically asymptotically stable for the original singularly perturbed system.
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ItemAn Approach to Minimum Attention Control by Sparse Derivative(IEEE, 2020)Minimum attention control proposed by Brockett is an important formulation for resource-aware control, while his problem formulation and the underlying optimization problem that he proposed is in general very hard. In this paper, we propose a computationally tractable design method of minimum attention control based on promoting sparsity of the derivative of control. The optimal control problem is formulated as L0 norm minimization of the time derivative of control under the constraint that the derivative is bounded by a fixed value. This is a non-convex problem, and we propose L1 relaxation for linear systems to obtain optimal control by efficient numerical computation. We then show equivalence theorems between the L0 and L1 optimal controls. Also, we present an example of feedback control for the first-order integrator, that illustrates the proposed methodology.
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ItemSampled-data extremum-seeking control for optimization of constrained dynamical systems using barrier function methods(IEEE, 2020-03-12)Most extremum-seeking control approaches focus solely on the problem of finding the extremum of some unknown, steady-state performance map. However, many industrial applications also have to deal with constraints on operating conditions due to, e.g., actuator limitations, limitations on design or tunable system parameters, or constraints on measurable signals. These constraints, which can be unknown a-priori, may conflict with the otherwise optimal operational condition, and should be taken into account in performance optimization. In this work, we propose a sampled-data extremum-seeking approach for optimization of constrained dynamical systems using barrier function methods, where both the objective function and the constraint function are available through measurement only. We show that, under the assumption that initialization does not violate constraints, the interconnection between a constrained dynamical system and optimization algorithms that employ barrier function methods is stable, the constraints are satisfied, and optimization is achieved. We illustrate the results by means of a numerical example.