Electrical and Electronic Engineering - Research Publications
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ItemNo Preview AvailableEvent-Triggered Boundary Control of 2 x 2 Semilinear Hyperbolic Systems(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2024-01)
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ItemNo Preview AvailableOn Receding-Horizon Approximation in Time-Varying Optimal Control(IEEE, 2023-01-01)
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ItemNo Preview AvailableSecond-Order Properties of Noisy Distributed Gradient Descent(IEEE, 2023-01-01)
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ItemNo Preview AvailableBoundary Feedback Control of 2x2 Quasilinear Hyperbolic Systems: Predictive Synthesis and Robustness Analysis(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2022-03)
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ItemNo Preview AvailableModeling and Control of Pipeline Networks Supplied by Automated Irrigation Channels(American Society of Civil Engineers, 2022-06-01)A model-based approach to control system design is developed for regulating the discharge flows at the outlets of a pipeline network supplied by an irrigation channel. The open channel is also controlled automatically to regulate the supply-point water level. The hydraulic pressure at the source of the network is therefore dynamic when flow load varies. Regulation of the piped discharge flows is achieved by adjusting outlet control valves on the basis of the specified flow demand and sensor measurements. A blend of feedforward and feedback control is proposed. The steady-state behaviour of a nonlinear distributed-parameter model of the network is used to determine the feedforward control action. The feedback control action is used to compensate for modeling error. The design of the feedback controller is based on a frequency-domain transfer function model of the system dynamics, and classical loop-shaping ideas. This model is obtained via the admittance matrix of linearized network equations. The synthesis of both centralized and decentralized feedback controller configurations is considered. Simulations that involve a nonlinear distributed-parameter model of the network dynamics are used to illustrate controller performance.
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ItemNo Preview AvailablePredictive feedback boundary control of semilinear and quasilinear 2 × 2 hyperbolic PDE–ODE systems(Elsevier BV, 2022-06-01)We present a control design for semilinear and quasilinear 2 × 2 hyperbolic partial differential equations with the control input at one boundary and a nonlinear ordinary differential equation coupled to the other. The controller can be designed to asymptotically stabilize the system at an equilibrium or relative to a reference signal. Two related but different controllers for semilinear and general quasilinear systems are presented and the additional challenges in quasilinear systems are discussed. Moreover, we present an observer that estimates the distributed PDE state and the unmeasured ODE state from measurements at the actuated boundary only, which can be used to also solve the output feedback control problem.
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ItemMoving Horizon Estimation for Linear Cascade Systems(IEEE, 2018-01-01)A structured approach to the problem of state estimation for cascaded linear sub-systems is studied in terms of minimizing a measure of the error relative to a model over a moving horizon of past system input and output observations. A quadratic programming formulation of this optimization problem is considered and two approaches are explored. One approach involves solving the Karush-Kuhn-Tucker conditions directly, and the other is based on the alternating direction method of multipliers. In both cases, the problem structure can be exploited to yield distributed computations in the following sense: Construction of the estimate for each sub-system component of the state involves information pertaining to the two immediate neighbours only. Numerical simulations based on model data from an automated irrigation channel are used to investigate and compare the computational burden of the two approaches.
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ItemStructured moving horizon estimation for linear system chains(IEEE, 2019-06-01)Computational aspects of moving horizon state estimation are studied for a class of chain networks with bidirectional coupling in the linear state dynamics, and measured outputs. Moving horizon estimation involves solving a quadratic program to minimize the estimation error relative to a model over a fixed window of past input-output observations. By exploiting the spatial structure of a chain, two algorithms for solving this quadratic program are considered. Both algorithms can be distributed in the sense that the computations associated with each sub-system component of the state depend only on information associated with the immediate neighbours. The algorithms differ in the way that the linear Karush-Kuhn-Tucker conditions for optimality are solved. Computational and information dependency overheads are analyzed. Numerical results are presented for a 1-D mass-spring-damper chain.
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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.