Browsing by Author "Barber, Phil"

Now showing 1 - 3 of 3
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    Controllability, observability in networked control
    (Elsevier, 2009-06-18) Longo, Stefano; Herrmann, Guido; Barber, Phil
    We reconsider and advance the analysis of structural properties (controllability and observability) of a class of linear Networked Control Systems (NCSs). We model the NCS as a periodic system with limited communication where the non updated signals can either be held constant (the zero-order-hold case) or reset to zero. Periodicity is dealt using the lifting technique. We prove that a communication sequence that avoids particularly defined pathological sampling rates and updates each actuator signal only once is sufficient to preserve controllability (and observability for the dual problem of sensor scheduling). These sequences can be shorter than previously established and we set a tight lower bound to them.
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    Optimization approaches for controller and schedule codesign in networked control
    (Elsevier, 2009-06-17) Longo, Stefano; Herrmann, Guido; Barber, Phil
    We consider the offline optimization of a sequence for communication scheduling in networked control systems. Given a continuous-time Linear Quadratic Regulator (LQR) problem we design a sampled-data periodic controller based on the continuous time LQR controller that takes into account the limited communication medium and inter-sampling behavior. To allow for a Riccati equation approach, singularities in the weighting matrices and time-variance are accounted for using a lifting approach. Optimal scheduling can be obtained by solving a complex combinatorial optimization problem. Two stochastic algorithms will be proposed to find a (sub)optimal sequence and the associated optimal controller which is the result of a discrete algebraic Riccati equation for the given optimal sequence.
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    Road vehicle state estimation using low-cost GPS/INS
    (Elsevier Science B.V., Amsterdam, 2011-08-01T00:00:00Z) Tin Leung, King; Whidborne, James F.; Purdy, David J.; Barber, Phil
    Assuming known vehicle parameters, this paper proposes an innovative integrated Kalman filter (IKF) scheme to estimate vehicle dynamics, in particular the sideslip, the heading and the longitudinal velocity. The IKF is compared with the 2DoF linear bicycle model, the triple Kalman filter (KF) and a model-based KF (MKF) in a simulation environment. Simulation results show that the proposed IKF is superior to other KF designs (both Kinematic KF and MKF) on state estimation when tyre characteristics are within the linear region (i.e. manoeuvres below 55 kph).