Particle predictive control

Villiers, J. P. de; Godsill, Simon; Singh, Sumeetpal S.

doi:10.1016/j.jspi.2010.11.025

Cited by 13 publications

(5 citation statements)

References 24 publications

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“…The use of stochastic optimisation, and specifically SMC algorithms, in the context of MPC and with application to planning trajectories for aircraft and UAV's has previously been proposed in [13,6,31].…”

Section: Trajectory Optimisation In a Tmamentioning

confidence: 99%

“…L needs to be chosen sufficiently large for the approximate distribution of ξ k to be sufficiently concentrated about the global optimum (or set of global optima) to be useful -that is, that any sample drawn from this distribution is very likely to be very close to a global optimiser. See [31] for a precise account of how an augmented statistical problem can be set up such that an optimisation problem is re-cast as an inference problem, in a context very similar to the one considered in this report.…”

Section: Trajectory Optimisation In a Tmamentioning

confidence: 99%

“…Previous work by the authors [11] compared two stochastic methods along with a standard linearised method to give an indication of the quality of the stochastic solutions when given a limited computational timespan. The work of [10]- [12] includes examples of SMC applied to MPC of multiple vehicles considering two and three dimensional trajectories under the predicted effects of wind and additional uncertainty. These works also observed that the time required to solve such problems was currently prohibitive despite the positive features of the solutions and indicated that potential time savings could be achieved through parallelisation.…”

Section: Introductionmentioning

confidence: 99%

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Parallelisation of Sequential Monte Carlo for real-time control in air traffic management

Eele

Maciejowski

Chau

et al. 2013

52nd IEEE Conference on Decision and Control

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This paper presents the parallelisation of a Se quential Monte Carlo algorithm, and the associated changes required when applied to the problem of conflict resolution and aircraft trajectory control in air traffic management. The target problem is non-linear constrained, non-convex and multi-agent. The new method is ' shown to have a 98.5% computational time saving over that of a previous sequential implementation, with no degradation in path quality. The computation saving is enough to allow real-time implementation.

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Section: Trajectory Optimisation In a Tmamentioning

confidence: 99%

Section: Trajectory Optimisation In a Tmamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Parallelisation of Sequential Monte Carlo for real-time control in air traffic management

Eele

Maciejowski

Chau

et al. 2013

52nd IEEE Conference on Decision and Control

View full text Add to dashboard Cite

show abstract

“…The optimization problem is solved using mixed-integer linear programming techniques. De Villiers et al (2011) used a Markov chain Monte Carlo (MCMC) algorithm to approximate the maximum a posteriori estimation of the control signal. In that study, PF has been used in the control computation step as a slight aid for the estimation of acceptance probability of an MCMC algorithm.…”

Section: Introductionmentioning

confidence: 99%

A non-linear estimation and model predictive control algorithm based on ant colony optimization

Nobahari

Nasrollahi

2019

Transactions of the Institute of Measurement and Control

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A new heuristic controller, called the continuous ant colony controller, is proposed for non-linear stochastic Gaussian/non-Gaussian systems. The new controller formulates the state estimation and the model predictive control problems as a single stochastic dynamic optimization problem, and utilizes a colony of virtual ants to find and track the best estimated state and the best control signal. For this purpose, an augmented state space is defined. An integrated cost function is also defined to evaluate the points of the augmented state space, explored by the ants. This function minimizes simultaneously the state estimation error, tracking error, control effort and control smoothness. Ants search the augmented state space dynamically in a similar scheme to the optimization algorithm, known as the continuous ant colony system. Performance of the new model predictive controller is evaluated for three non-linear problems. The problems are a non-linear continuous stirred tank reactor, a non-linear cart and spring system, and the attitude control of a non-linear quadrotor. The results verify successful performance of the proposed algorithm from both estimation and control points of view.

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“…In the study of the trajectory and offtracking of the vehicle can be used various methods of field tests [4,5,7]. In source [28], the experimental procedure involves the installation of two nozzles on a road train, which spray paint at pressure and, as a result of processing individual points, get a trajectory of movement.…”

Section: Introductionmentioning

confidence: 99%

Testing trajectory of road trains with program complexes

Nurmuhumatovich

Mikušová

2019

The Archives of Automotive Engineering – Archiwum Motoryzacji

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The article provides information about using software for determination off tracking and trajectory movement of trucks. It describes experiment of determination of trajectory movement of trucks, using GPS system, to assess the compliance of similar models of the movement of road trains in computer-aided road design systems. Experimental study was conducted using truck KAMAZ 54115 and semi-trailer M 9397 in real road conditions. There are presented and compared results of the study of movement trajectories of road trains with results obtained of the Auto TURN software package application. Discrepancy between the curves is explained. As the difference in AutoTURN data in comparison with experimental values does not exceed 4.07 %, which, given the very similar nature of the curves, can be considered valid. As a conclusion the use of AutoTURN software package is validated for determining dynamic dimension of cars and for modelling trajectory of movement, not only for road trains, but also for all types of vehicles in the area of their design.

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Particle predictive control

Cited by 13 publications

References 24 publications

Parallelisation of Sequential Monte Carlo for real-time control in air traffic management

Parallelisation of Sequential Monte Carlo for real-time control in air traffic management

A non-linear estimation and model predictive control algorithm based on ant colony optimization

Testing trajectory of road trains with program complexes

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