Error minimising gradients for improving cerebellar model articulation controller performance

In motion control applications where the desired trajectory velocity exceeds an actuator’s maximum velocity limitations, large position errors will occur between the desired and actual trajectory responses. In these situations standard control approaches cannot predict the output saturation of the actuator and thus the associated error summation cannot be minimised.An adaptive feedforward control solution such as the Cerebellar Model Articulation Controller (CMAC) is able to provide an inherent level of prediction for these situations, moving the system output in the direction of the excessive desired velocity before actuator saturation occurs. However the pre-empting level of a CMAC is not adaptive, and thus the optimal point in time to start moving the system output in the direction of the excessive desired velocity remains unsolved. While the CMAC can adaptively minimise an actuator’s position error, the minimisation of the summation of error over time created by the divergence of the desired and actual trajectory responses requires an additional adaptive level of control.This thesis presents an improved method of training CMACs to minimise the summation of error over time created when the desired trajectory velocity exceeds the actuator’s maximum velocity limitations. This improved method called the Error Minimising Gradient Controller (EMGC) is able to adaptively modify a CMAC’s training signal so that the CMAC will start to move the output of the system in the direction of the excessive desired velocity with an optimised pre-empting level.The EMGC was originally created to minimise the loss of linguistic information conveyed through an actuated series of concatenated hand sign gestures reproducing deafblind sign language. The EMGC concept however is able to be implemented on any system where the error summation associated with excessive desired velocities needs to be minimised, with the EMGC producing an improved output approximation over using a CMAC alone.In this thesis, the EMGC was tested and benchmarked against a feedforward / feedback combined controller using a CMAC and PID controller. The EMGC was tested on an air-muscle actuator for a variety of situations comprising of a position discontinuity in a continuous desired trajectory. Tested situations included various discontinuity magnitudes together with varying approach and departure gradient profiles.Testing demonstrated that the addition of an EMGC can reduce a situation’s error summation magnitude if the base CMAC controller has not already provided a prior enough pre-empting output in the direction of the situation. The addition of an EMGC to a CMAC produces an improved approximation of reproduced motion trajectories, not only minimising position error for a single sampling instance, but also over time for periodic signals

The challenges and opportunities of human-centred AI for trustworthy robots and autonomous systems

The trustworthiness of robots and autonomous systems (RAS) has taken a prominent position on the way towards full autonomy. This work is the first to systematically explore the key facets of human-centred AI for trustworthy RAS. We identified five key properties of a trustworthy RAS, i.e., RAS must be (i) safe in any uncertain and dynamic environment; (ii) secure, i.e., protect itself from cyber threats; (iii) healthy and fault-tolerant; (iv) trusted and easy to use to enable effective human-machine interaction (HMI); (v) compliant with the law and ethical expectations. While the applications of RAS have mainly focused on performance and productivity, not enough scientific attention has been paid to the risks posed by advanced AI in RAS. We analytically examine the challenges of implementing trustworthy RAS with respect to the five key properties and explore the role and roadmap of AI technologies in ensuring the trustworthiness of RAS in respect of safety, security, health, HMI, and ethics. A new acceptance model of RAS is provided as a framework for human-centric AI requirements and for implementing trustworthy RAS by design. This approach promotes human-level intelligence to augment human capabilities and focuses on contribution to humanity

Autonomic management of virtualized resources in cloud computing

The last five years have witnessed a rapid growth of cloud computing in business, governmental and educational IT deployment. The success of cloud services depends critically on the effective management of virtualized resources. A key requirement of cloud management is the ability to dynamically match resource allocations to actual demands, To this end, we aim to design and implement a cloud resource management mechanism that manages underlying complexity, automates resource provisioning and controls client-perceived quality of service (QoS) while still achieving resource efficiency. The design of an automatic resource management centers on two questions: when to adjust resource allocations and how much to adjust. In a cloud, applications have different definitions on capacity and cloud dynamics makes it difficult to determine a static resource to performance relationship. In this dissertation, we have proposed a generic metric that measures application capacity, designed model-independent and adaptive approaches to manage resources and built a cloud management system scalable to a cluster of machines. To understand web system capacity, we propose to use a metric of productivity index (PI), which is defined as the ratio of yield to cost, to measure the system processing capability online. PI is a generic concept that can be applied to different levels to monitor system progress in order to identify if more capacity is needed. We applied the concept of PI to the problem of overload prevention in multi-tier websites. The overload predictor built on the PI metric shows more accurate and responsive overload prevention compared to conventional approaches. To address the issue of the lack of accurate server model, we propose a model-independent fuzzy control based approach for CPU allocation. For adaptive and stable control performance, we embed the controller with self-tuning output amplification and flexible rule selection. Finally, we build a QoS provisioning framework that supports multi-objective QoS control and service differentiation. Experiments on a virtual cluster with two service classes show the effectiveness of our approach in both performance and power control. To address the problems of complex interplay between resources and process delays in fine-grained multi-resource allocation, we consider capacity management as a decision-making problem and employ reinforcement learning (RL) to optimize the process. The optimization depends on the trial-and-error interactions with the cloud system. In order to improve the initial management performance, we propose a model-based RL algorithm. The neural network based environment model, which is learned from previous management history, generates simulated resource allocations for the RL agent. Experiment results on heterogeneous applications show that our approach makes efficient use of limited interactions and find near optimal resource configurations within 7 steps. Finally, we present a distributed reinforcement learning approach to the cluster-wide cloud resource management. We decompose the cluster-wide resource allocation problem into sub-problems concerning individual VM resource configurations. The cluster-wide allocation is optimized if individual VMs meet their SLA with a high resource utilization. For scalability, we develop an efficient reinforcement learning approach with continuous state space. For adaptability, we use VM low-level runtime statistics to accommodate workload dynamics. Prototyped in a iBalloon system, the distributed learning approach successfully manages 128 VMs on a 16-node close correlated cluster

Proceedings of The Multi-Agent Logics, Languages, and Organisations Federated Workshops (MALLOW 2010)

http://ceur-ws.org/Vol-627/allproceedings.pdfInternational audienceMALLOW-2010 is a third edition of a series initiated in 2007 in Durham, and pursued in 2009 in Turin. The objective, as initially stated, is to "provide a venue where: the cost of participation was minimum; participants were able to attend various workshops, so fostering collaboration and cross-fertilization; there was a friendly atmosphere and plenty of time for networking, by maximizing the time participants spent together"

Intelligent control of industrial processes

A detailed survey of the field of intelligent control is presented. Current practices are reviewed and the need for a unifying framework to identify and strengthen the underlying core principles is postulated. Intelligent control is redefined to make explicit use of human systems in control as a reference model. Psychological theories of intelligent behaviour reveal certain basic attributes. From these a set of necessary and sufficient conditions for intelligent control are derived. Learning ability is identified as a crucial element. Necessary attributes for learning are prediction capabilities, internal world model, estimation of the model parameters, and active probing to reduce uncertainties. This framewoik is used to define a Learning Based Predictive Control (LBPC) strategy. LBPC is derived from Predictive Functional Control techniques with an adaptive layer implemented by recursive least squares. Improved performance above conventional adaptive control is demonstrated. Distributed parameter systems are identified as a suitable application area requiring an intelligent control approach. Such systems are invariably complex, ill-defined, and nonlinear. Plasticating extrusion processes are considered in particular. LBPC is applied to control of the primary loop to regulate melt temperature and pressure at the die. A novel control technique is proposed for dynamic profile control of extruder barrel wall temperature. This is a two-level hierarchical scheme combining the benefits of LBPC control blocks at the lowest level with decision logic operating at the higher level as a supervisor. This Logic Based Strategy allows multivariable control of non-square systems with more outputs than inputs. The application of LBS to an extruder is demonstrated