Fig 8 - uploaded by Kevin Gurney
Content may be subject to copyright.
Source publication
This paper describes emergent neurobiological characteristics of an intelligent multiple-controller that has been developed
for controlling the throttle, brake and steering subsystems of a validated vehicle model. Simulation results demonstrate the
effectiveness of the proposed approach. Importantly, the controller exhibits discrete behaviours, gov...
Context in source publication
Context 1
... enable gaze strategies driven by complex visual features and task demands. Note that this kind of 'subsumption architecture' also figures in an influential strand of contemporary robotics [16]. Integrating the general idea of a layered scheme with the hypothesis that the basal ganglia act as a central switch yields the architecture shown in Fig. 8 [15]. Each level of competence has its own competition between multiple action requests mediated by the BG, and may also take control of the motor plant. Further, action representations in higher layers can influence those in lower layers thereby exerting 'top-down' ...
Similar publications
This paper deals with the problem of automatic path-following control for a class of autonomous vehicle systems with parametric uncertainties and external disturbances in cross-country conditions. In the unstructured environments, the unevenness, the discontinuity and the variability of the terrain greatly increase the parametric uncertainties and...
We review the enabling theory for the decentralized and cooperative control of formations of unmanned, autonomous vehicles. The decentralized and cooperative formation control approach combines recent results from dynamical system theory, control theory, and algebraic graph theory. The stability of vehicle formations is discussed, and the applicabi...
Citations
... The basal ganglia are the source of inspiration for a number of works. Hussain, Gurney, Abdullah, and Chambers (2008) refer specifically to the cortico-basal ganglia loops, which play a fundamental role in reward and action selection (Haber, 2011), to implement a vehicle controller. The controller is loosely based on an earlier computational model of action selection in the basal ganglia (Gurney, Prescott, & Redgrave, 2001). ...
... We then go on to deploy these ideas in the context of an architecture for autonomous vehicle control which uses multiple sub-controllers [4]. This builds on a general consideration of the links between biology and the vehicle controller dealt with previously [5]). Thus, we suggest how the architecture may incorporate 'automatised' processing to complement its current 'executive' control of sub-controller selection. ...
... Recently, Abdulah et al [4] have formulated one instantiation of AVC using such a multi-controller scheme. We have previously described similarities between this AVC architecture and biological solutions to action selection [5], but here, we focus on the issues of automatic and controlled behaviour. ...
There are two modes of control recognised in the cognitive psychological literature. Controlled processing is slow, requires serial attention to sub-tasks, and requires effortful memory retrieval and decision making.
In contrast automatic control is less effortful, less prone to interference from simultaneous tasks, and is driven largely by the current stimulus.
Neurobiological analogues of these are goal-directed and habit-based behaviour respectively. Here, we suggest how these control modes might be deployed in an engineering solution to Automatic
Vehicle Control. We present pilot data on a first step towards instantiating automatised control in the architecture, and
suggest a synergy between the engineering and biological investigation of this dual-process approach.
This paper presents an intelligent multiple-controller framework for the integrated control of throttle, brake and steering subsystems of realistic validated nonlinear autonomous vehicles. In the developed multiple-controller framework, a fuzzy logic-based switching and tuning supervisor operates at the highest level of the system and makes a switching decision on the basis of the required performance measure, between an arbitrary number of adaptive controllers: in the current case, between a conventional Proportional-Integral-Derivative (PID) controller and a PID structure-based pole-zero placement controller. The fuzzy supervisor is also able to adaptively tune the parameters of the multiple controllers. Sample simulation results using a realistic autonomous vehicle model demonstrate the ability of the intelligent controller to both simultaneously track the desired throttle, braking force, and steering changes, whilst penalising excessive control actions - with significant potential implications for both fuel and emission economy. We conclude by demonstrating how this work has laid the foundation for ongoing neuro-biologically motivated algorithmic development of a more cognitively inspired multiple-controller framework.
A novel soft switching control approach is presented in this paper for autonomous vehicles by using a new functional model for Basal Ganglia (BG). In the proposed approach, a family of fundamental controllers is treated as each of a set of basic controllers are thought of as an ‘action’ which may be selected by the BG in a soft switching regime for real-time control of autonomous vehicle systems. Three controllers, i.e., conventional Proportional-Integral-Derivative (PID) controller, a PID structure-based pole-zero placement controller, and a pole only placement controller are used in this paper to support the proposed soft switching control strategy. To demonstrate the effectiveness of the proposed soft switching approach for nonlinear autonomous vehicle control (AVC), the throttle, brake and steering subsystems are focused on in this paper because they are three key subsystems in the whole AVC system. Simulation results are provided to illustrate the performance and effectiveness of the proposed soft switching control approach by applying it to the abovementioned subsystems.
Cognitive computation is an emerging discipline linking together neurobiology, cognitive psychology and artificial intelligence. Springer Neuroscience has launched a journal in this exciting multidisciplinary topic, which seeks to publish biologically inspired theoretical, computational, experimental and integrative accounts of all aspects of natural and artificial cognitive systems. In this keynote, we outline and build on some of the pioneering work of the late Professor John Taylor, who was also founding Advisory Board Chair of Cognitive Computation, specifically his proposal on how to create a cognitive machine equipped with multi-modal cognitive capabilities. In this context, we first present a novel modular cognitive control framework for autonomous systems that could potentially realize the required cognitive action-selection and learning capabilities in Professor Taylor's envisaged cognitive machine. An ongoing case study in autonomous vehicle control is described, as a benchmark problem, with encouraging preliminary results in a range of realistic driving scenarios — and with significant potential fuel and emission economy implications, compared to conventional control systems. Finally, possible future avenues are explored, including our ongoing work aimed at developing a general modular cognitive framework incorporating multiple modalities, including vision, motor action, language and emotion, required for enabling multi-modal social cognitive and affective behavioral capabilities in future autonomous agents.
Cognition and control processes share many similar characteristics, and decisionmaking and learning under the paradigm of
multiple models has increasingly gained attention in both fields. The controlled finite Markov chain (CFMC) approach enables
to deal with a large variety of signals and systems with multivariable, nonlinear, and stochastic nature. In this article,
adaptive control based on multiple models is considered. For a set of candidate plant models, CFMC models (and controllers)
are constructed off-line. The outcomes of the CFMC models are compared with frequentist information obtained from on-line
data. The best model (and controller) is chosen based on the Kullback–Leibler information. This approach to adaptive control
emphasizes the use of physical models as the basis of reliable plant identification. Three series of simulations are conducted:
to examine the performace of the developed Matlab-tools; to illustrate the approach in the control of a nonlinear nonminimum
phase van der Vusse CSTR plant; and to examine the suggested model selection method for the adaptive control.
How are we to go about understanding the computations that underpin cognition? Here we set out a methodological framework
that helps understand different approaches to solving this problem. We argue that a very powerful stratagem is to attempt
to ‘reverse engineer’ the brain and that computational neuroscience plays a pivotal role in this programme. En passant, we
also tackle the oft-asked and prior question of why we should build computational models of any kind. Our framework uses four
levels of conceptual analysis: computation, algorithm, mechanism and biological substrate. As such it enables us to understand
how (algorithmic) AI and connectionism may be recruited to help propel the reverse-engineering programme forward. The framework
also incorporates the notion of different levels of structural description of the brain, and analysis of this issue gives
rise to a novel proposal for capturing computations at multiple levels of description in a single model.
