Comparison of stochastic methods for control in air traffic management

This paper provides a direct comparison of two stochastic optimisation techniques (Markov Chain Monte Carlo and Sequential Monte Carlo) when applied to the problem of conflict resolution and aircraft trajectory control in air traffic management. The two methods are then also compared to another existing technique of Mixed-Integer Linear Programming which is also popular in distributed control.Work supported by EPSRC (Engineering and Physical Sciences Research Council - UK) Grant No. EP/G066477/1International Federation of Automatic Control World Congress, 201

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

This paper presents the parallelisation of a Sequential 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.This work was supported by EPSRC (Engineering and Physical Sciences Research Council - UK) Grant No. EP/G066477/1In proceedings of the IEEE Conference on Decision and Control 201

Control of aircraft in the terminal manoeuvring area using parallelised sequential Monte Carlo

This paper reports on the use of a parallelised Model Predictive Control, Sequential Monte Carlo algorithm for solving the problem of conflict resolution and aircraft trajectory control in air traffic management specifically around the terminal manoeuvring area of an airport. The target problem is nonlinear, highly constrained, non-convex and uses a single decision-maker with multiple aircraft. The implementation includes a spatio-temporal wind model and rolling window simulations for realistic ongoing scenarios. The method is capable of handling arriving and departing aircraft simultaneously including some with very low fuel remaining. A novel flow field is proposed to smooth the approach trajectories for arriving aircraft and all trajectories are planned in three dimensions. Massive parallelisation of the algorithm allows solution speeds to approach those required for real-time use.This work was supported by EPSRC (Engineering and Physical Sciences Research Council - UK) Grant No. EP/G066477/1AIAA Conference on Guidance, Navigation and Control 201

Mapping adaptive particle filters to heterogeneous reconfigurable systems

This article presents an approach for mapping real-time applications based on particle filters (PFs) to heterogeneous reconfigurable systems, which typically consist of multiple FPGAs and CPUs. A method is proposed to adapt the number of particles dynamically and to utilise runtime reconfigurability of FPGAs for reduced power and energy consumption. A data compression scheme is employed to reduce communication overhead between FPGAs and CPUs. A mobile robot localisation and tracking application is developed to illustrate our approach. Experimental results show that the proposed adaptive PF can reduce up to 99% of computation time. Using runtime reconfiguration, we achieve a 25% to 34% reduction in idle power. A 1U system with four FPGAs is up to 169 times faster than a single-core CPU and 41 times faster than a 1U CPU server with 12 cores. It is also estimated to be 3 times faster than a system with four GPUs

Guidelines for CPD accreditation of the South African Society of Medical Oncology

There do not appear to be guidelines in use for accreditation of continuing professional development (CPD) activities in South Africa, or indeed in many other parts of the world. The South African Society of Medical Oncology (SASMO) has adopted the guidelines below, based in part on the guidelines of the Canadian Medical Association for the interaction between industry and doctors

Real-time optimisation-based planning and scheduling of vehicle trajectories

Optimal planning and scheduling of trajectories for vehicles such as aircraft, road vehicles, or trains, generally involves non-convex optimization. Such problems are frequently regarded as intractable. But we show that it is effective to tackle such problems using stochastic optimization methods, even for real-time use, as in model predictive control. We use Sequential Monte Carlo (particle filter) methods, implemented on Graphical Processor Units which allow massive parallelization. We describe the application of these methods to the problem of air-traffic management in a high-density vicinity of an airport (the terminal maneouvering area). We briefly discuss the applicability of the approach to other transport applications. © 2014 IEEE

Comparison of stochastic methods for control in air traffic management

This paper provides a direct comparison of two stochastic optimisation techniques (Markov Chain Monte Carlo and Sequential Monte Carlo) when applied to the problem of conflict resolution and aircraft trajectory control in air traffic management. The two methods are then also compared to another existing technique of Mixed-Integer Linear Programming which is also popular in distributed control. © 2011 IFAC