Electrical and Electronic Engineering - Research Publications

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Start date: 2012 × Type: Conference Paper × Author: Manzie, Christopher ×

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Now showing 1 - 10 of 31
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    Economic Model Predictive Control of Water Distribution Systems with Solar Energy and Batteries
    Zheng, X ; Wang, Y ; Weyer, E ; Manzie, C (IEEE, 2023-01-01)
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    Feasibility detection for nested codesign of hypersonic vehicles
    van der Heide, C ; Cudmore, P ; Jahn, I ; Bone, V ; Dower, PM ; Manzie, C (IEEE, 2023)
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    Incentivizing Local Controllability in Optimal Trajectory Planning
    Skoraczynski, AZ ; Manzie, C ; Dower, PM (IEEE, 2023-01-01)
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    Constraint Handling of an Airbreathing Hypersonic Vehicle via Predictive Reference Management
    Liu, V ; Manzie, C ; Dower, PM (IEEE, 2022-01-01)
    In this paper we consider the problem of constraint handling for an airbreathing hypersonic vehicle (HSV) through a hierarchical control architecture. A reference manager is incorporated as an intermediate control loop whose role is to modify an offline generated reference trajectory, without knowledge of disturbances, to enforce state and input constraints. Compared with traditional constraint handling approaches in HSV literature, this proposed approach allows for the deployment of controllers that are not typically formulated to handle constraints. We provide a computation time and constraint management comparison between a scheme that directly utilizes the nonlinear vehicle model and one that performs online linearization of the model.
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    Co-design of Control Strategy and Storage Size for a Water Distribution System
    Wang, Y ; Weyer, E ; Manzie, C ; Simpson, AR (IEEE, 2022-01-01)
    The design and operation of water distribution systems (WDSs) are two interrelated tasks that both impact the overall cost of the systems. The traditional approach is to first design the system and then develop a control strategy for the specified infrastructure. However, this is suboptimal in that the controlled system may hit operating constraints arising from inadequate design, or the capital cost may be excessive due to conservative design processes. The challenge of designing both the infrastructure and control strategy simultaneously is amplified by the demand profiles and energy prices being stochastic. In this paper, we investigate stochastic co-design optimization problems for simultaneously optimizing the tank size and parameters of a pumping strategy. We employ Markov chain theory to establish tractable co-design optimization problems. We show several simulation results to demonstrate the efficacy of the proposed approach.
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    Robust Tracking Model Predictive Control with Koopman Operators
    Wang, Y ; Yang, Y ; Pu, Y ; Manzie, C (IEEE, 2022-01-01)
    Koopman operators can be used to lift nonlinear dynamics into a higher dimensional space to obtain a linear model with nonlinear basis functions. They have proven particularly attractive when combined with data-driven techniques to identify the basis function coefficients. The resulting higherorder linear model is subsequently a good candidate for MPC application, as convex solvers may be applied in the lifted space. Nonetheless, the modeling errors between the original nonlinear system and the approximated Koopman linear model must be taken into account in the MPC design such that the closed-loop properties such as recursive feasibility and convergence can be guaranteed. In this paper, we use a robust constraint tightening approach to address this issue. To demonstrate the approach, we apply the proposed robust Koopman tracking MPC (KTMPC) to a continuous stirred tank reactor case study to show its efficacy.
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    A UNIFYING FRAMEWORK FOR ANALYSIS AND DESIGN OF EXTREMUM SEEKING CONTROLLERS
    Nesic, D ; Tan, Y ; Manzie, C ; Mohammadi, A ; Moase, W (IEEE, 2012-01-01)
    We summarize a unifying design approach to continuous-time extremum seeking that was recently reported by the authors. This approach is based on a feedback control paradigm that was to the best of our knowledge explicitly summarized for the first time in this form in our recent work. This paradigm covers some existing extremum seeking schemes, provides a direct link to off-line optimization and can be used as a unifying framework for design of novel extremum seeking schemes. Moreover, we show that other extremum seeking problem formulations can be interpreted using this unifying viewpoint. We believe that this unifying view will be invaluable to systematically design and analyze extremum seeking controllers in various settings.
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    Trajectory-based proofs for sampled-data extremum seeking control
    KHONG, S ; Nesic, D ; Tan, Y ; Manzie, CG (IEEE, 2013)
    Extremum seeking of nonlinear systems based on a sampled-data control law is revisited. It is established that under some generic assumptions, semi-global practical asymptotically stable convergence to an extremum can be achieved. To this end, trajectory-based arguments are employed, by contrast with Lyapunov-function-type approaches in the existing literature. The proof is simpler and more straightforward; it is based on assumptions that are in general easier to verify. The proposed extremum seeking framework may encompass more general optimisation algorithms, such as those which do not admit a state-update realisation and/or Lyapunov functions. Multi-unit extremum seeking is also investigated within the context of accelerating the speed of convergence.
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    On a Shubert Algorithm-Based Global Extremum Seeking Scheme
    Nesic, D ; Nguyen, T ; Tan, Y ; Manzie, C (IEEE, 2012)
    This paper adapts the so-called Shubert algorithm for Extremum Seeking Control (ESC) to seek the global extremum (in presence of local extrema) of general dynamic plants. Different from derivative based methods that are widely used in ESC, the Shubert algorithm is a good representative of sampling optimization methods. With knowledge of the Lipschitz constant of an unknown static mapping, this deterministic algorithm seeks the global extremum. By introducing “waiting time” the proposed Shubert algorithm-based global extremum seeking guarantees the semi-global practical convergence (in the initial states) to the global extremum if compact sets of inputs are considered. Several numerical examples demonstrate how proposed method may be successfully deployed.
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    On sampled-data extremum seeking control via stochastic approximation methods
    Khong, SZ ; Tan, Y ; Nesic, D ; Manzie, C (IEEE, 2013-01-01)
    This note establishes a link between stochastic approximation and extremum seeking of dynamical nonlinear systems. In particular, it is shown that by applying classes of stochastic approximation methods to dynamical systems via periodic sampled-data control, convergence analysis can be performed using standard tools in stochastic approximation. A tuning parameter within this framework is the period of the synchronised sampler and hold device, which is also the waiting time during which the system dynamics settle to within a controllable neighbourhood of the steady-state input-output behaviour. Semiglobal convergence with probability one is demonstrated for three basic classes of stochastic approximation methods: finite-difference, random directions, and simultaneous perturbation. The tradeoff between the speed of convergence and accuracy is also discussed within the context of asymptotic normality of the outputs of these optimisation algorithms.