5 results for Alam, F

  • Gaussian Process Model Predictive Control of an Unmanned Quadrotor

    Cao, G; Lai, E; Alam, F

    Journal article
    Auckland University of Technology

    The Model Predictive Control (MPC) trajectory tracking problem of an unmanned quadrotor with input and output constraints is addressed. In this article, the dynamic models of the quadrotor are obtained purely from operational data in the form of probabilistic Gaussian Process (GP) models. This is different from conventional models obtained through Newtonian analysis. A hierarchical control scheme is used to handle the trajectory tracking problem with the translational subsystem in the outer loop and the rotational subsystem in the inner loop. Constrained GP based MPC are formulated separately for both subsystems. The resulting MPC problems are typically nonlinear and non-convex. We derived a GP based local dynamical model that allows these optimization problems to be relaxed to convex ones which can be efficiently solved with a simple active-set algorithm. The performance of the proposed approach is compared with an existing unconstrained Nonlinear Model Predictive Control (NMPC) algorithm and an existing constrained nonlinear GP based MPC algorithm. In the first comparison, simulation results show that the two approaches exhibit similar trajectory tracking performance. However, our approach has the advantage of incorporating constraints on the control inputs. In the second comparison, simulation results demonstrate that our approach only requires 20% of the computational time for the existing nonlinear GP based MPC.

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  • Blind source separation for adaptive speech control

    Moir, T; Alam, F; Harris, J (2011-09-06)

    Conference item
    Auckland University of Technology

    Crosstalk resistant adaptive noise cancellation (CTRANC) is a method of separat-ing convolutively mixed sources where little a priori information is known about the system. Possible areas of application for such an algorithm include speech sig-nal processing, in telecommunications, and in the biomedical industry. In this pa-per we propose a novel adaptation to the traditional CTRANC which increases computational efficiency when the number of sources fits the requirement of where and . Preliminary results also show a modest im-provement in separation performance when comparing it to the multiple-input multiple-output method proposed by Mei and Yin (2004).

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  • Gaussian Process Model Predictive Control of Unknown Nonlinear Systems

    Cao, G; Lai, E; Alam, F

    Journal article
    Auckland University of Technology

    Model predictive control (MPC) of an unknown system that is modelled by Gaussian process (GP) techniques is studied. Using GP, the variances computed during the modelling and inference processes allow us to take model uncertainty into account. The main issue in using MPC to control systems modelled by GP is the propagation of such uncertainties within the control horizon. In this study, two approaches to solve this problem, called GPMPC1 and GPMPC2, are proposed. With GPMPC1, the original stochastic model predictive control (SMPC) problem is relaxed to a deterministic non-linear MPC based on a basic linearised GP local model. The resulting optimisation problem, though non-convex, can be solved by the sequential quadratic programming. By incorporating the model variance into the state vector, an extended local model is derived. This model allows us to relax the non-convex MPC problem to a convex one which can be solved by an active-set method efficiently. The performance of both approaches is demonstrated by applying them to two trajectory tracking problems. Results show that both GPMPC1 and GPMPC2 produce effective controls but GPMPC2 is much more efficient computationally.

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  • Integrated fuzzy signal and ramp-metering at a diamond interchange

    Pham, VC; Alam, F; Potgieter, J; Fang, FC; Xu, Peter (2013-06)

    Journal article
    The University of Auckland Library

    We propose a fuzzy logic control for the integrated signal operation of a diamond interchange and its ramp meter, to improve traffic flows on surface streets and motorway. This fuzzy logic diamond interchange (FLDI) comprises of three modules: fuzzy phase timing (FPT) module that controls the green time extension of the current phase, phase logic selection (PLS) module that decides the next phase based on the pre-defined phase sequence or phase logic and, fuzzy ramp-metering (FRM) module that determines the cycle time of the ramp meter based on current traffic volumes and conditions of the surface streets and the motorways. The FLDI is implemented in Advanced Interactive Microscopic Simulator for Urban and Non-Urban Network Version 6 (AIMSUN 6), and compared with the traffic actuated signal control. Simulation results show that the FLDI outperforms the traffic-actuated models with lower system total travel time, average delay, and improvements in downstream average speed and average delay.

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  • Optimal Coordination of Ramp Metering Via Iterative Dynamic Programming

    Yu, XF; Xu, Peter; Alam, F; Fang, CF (2014)

    Journal article
    The University of Auckland Library

    This paper presents an online approach for the optimal coordination and control of ramp metering. The ramp metering problem is formulated in a decision network and subsequently solved by iterative dynamic programming (IDP), where a near optimal ramp metering policy is obtained by minimizing the total travel time spent (TTS). The optimal metering policy is implemented under the framework of receding horizon control. In this study, by incorporating METANET for traffic flow model, a study location is simulated and the performance of the proposed algorithm is measured and compared to those without ramp metering and with a local uncoordinated ramp metering. Results show that the proposed method is able to improve traffic conditions and prevent recurrent congestion under certain ramp queue constrains.

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