Neurobiologically-Inspired Soft Switching Control of Autonomous Vehicles

Yang, Erfu; Hussain, Amir; Gurney, Kevin

doi:10.1007/978-3-642-31561-9_9

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…The intuitive explanation for this improvement is the integration of controllers, (shown in Eq. (13)), in a biologically-inspired fashion augmented with the BG process that facilitates the natural selection of actions, hence result in less 'bumping' at switching time [64]. Moreover, the computational model of [58,59] in particular is successfully validated to avoid oscillation [62].…”

Section: Oscillatory Behaviourmentioning

confidence: 99%

Design and evaluation of a biologically-inspired cloud elasticity framework

Ullah

Hussain

2020

Cluster Comput

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The elasticity in cloud is essential to the effective management of computational resources as it enables readjustment at runtime to meet application demands. Over the years, researchers and practitioners have proposed many auto-scaling solutions using versatile techniques ranging from simple if-then-else based rules to sophisticated optimisation, control theory and machine learning based methods. However, despite an extensive range of existing elasticity research, the aim of implementing an efficient scaling technique that satisfies the actual demands is still a challenge to achieve. The existing methods suffer from issues like: (1) the lack of adaptability and static scaling behaviour whilst considering completely fixed approaches; (2) the burden of additional computational overhead, the inability to cope with the sudden changes in the workload behaviour and the preference of adaptability over reliability at runtime whilst considering the fully dynamic approaches; and (3) the lack of considering uncertainty aspects while designing auto-scaling solutions. In this paper, we aim to address these issues using a holistic biologically-inspired feedback switch controller. This method utilises multiple controllers and a switching mechanism, implemented using fuzzy system, that realises the selection of suitable controller at runtime. The fuzzy system also facilitates the design of qualitative elasticity rules. Furthermore, to improve the possibility of avoiding the oscillatory behaviour (a problem commonly associated with switch methodologies), this paper integrates a biologically-inspired computational model of action selection. Lastly, we identify seven different kinds of real workload patterns and utilise them to evaluate the performance of the proposed method against the state-of-the-art approaches. The obtained computational results demonstrate that the proposed method results in achieving better performance without incurring any additional cost in comparison to the state-of-the-art approaches.

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Section: Oscillatory Behaviourmentioning

confidence: 99%

Design and evaluation of a biologically-inspired cloud elasticity framework

Ullah

Hussain

2020

Cluster Comput

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“…Note that the work reported here is part of the preliminary study, and thus we have not carried out a further theoretical stability analysis. However, an intuitive explanation is that the mixture of all controllers is done (in Equation (3)) in a bio-inspired way augmented by the BG process, which facilitates a natural selection of actions that results in less 'bumping' at the switching time [58]. Moreover, the computational model of [21,22] in particular is proved to successfully avoid the oscillation and keep the energy efficiency in various action selection problems [17].…”

Section: Experimental Set-upmentioning

confidence: 99%

Towards a Biologically Inspired Soft Switching Approach for Cloud Resource Provisioning

Ullah

Hussain

et al. 2016

Cogn Comput

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Cloud elasticity augments applications to dynamically adapt to changes in demand by acquiring or releasing computational resources on the fly. In the past, we developed a framework for cloud elasticity utilizing multiple feedback controllers simultaneously. Each controller determines the scaling action with different intensity, whereby the selection of a suitable controller is realized with a fuzzy inference system. In this paper, we aim to identify the similarities between cloud elasticity and action selection mechanism in animal's brain. We treat each controller in our previous framework as an action and propose a novel bio-inspired, soft switching approach. This approach integrates a basal ganglia computational model as an action selection mechanism. Initial experimental results demonstrate that the basal ganglia based approach has higher potential to improve the overall system performance and stability.

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