Iterative learning control: algorithm development and experimental benchmarking

This thesis concerns the general area of experimental benchmarking of Iterative Learning Control (ILC) algorithms using two experimental facilities. ILC is an approach which is suitable for applications where the same task is executed repeatedly over the necessarily finite time duration, known as the trial length. The process is reset prior to the commencement of each execution. The basic idea of ILC is to use information from previously executed trials to update the control input to be applied during the next one. The first experimental facility is a non-minimum phase electro-mechanical system and the other is a gantry robot whose basic task is to pick and place objects on a moving conveyor under synchronization and in a fixed finite time duration that replicates many tasks encountered in the process industries. Novel contributions are made in both the development of new algorithms and, especially, in the analysis of experimental results both of a single algorithm alone and also in the comparison of the relative performance of different algorithms. In the case of non-minimum phase systems, a new algorithm, named Reference Shift ILC (RSILC) is developed that is of a two loop structure. One learning loop addresses the system lag and another tackles the possibility of a large initial plant input commonly encountered when using basic iterative learning control algorithms. After basic algorithm development and simulation studies, experimental results are given to conclude that performance improvement over previously reported algorithms is reasonable. The gantry robot has been previously used to experimentally benchmark a range of simple structure ILC algorithms, such as those based on the ILC versions of the classical proportional plus derivative error actuated controllers, and some state-space based optimal ILC algorithms. Here these results are extended by the first ever detailed experimental study of the performance of stochastic ILC algorithms together with some modifications necessary to their configuration in order to increase performance. The majority of the currently reported ILC algorithms mainly focus on reducing the trial-to-trial error but it is known that this may come at the cost of poor or unacceptable performance along the trial dynamics. Control theory for discrete linear repetitive processes is used to design ILC control laws that enable the control of both trial-to-trial error convergence and along the trial dynamics. These algorithms can be computed using Linear Matrix Inequalities (LMIs) and again the results of experimental implementation on the gantry robot are given. These results are the first ever in this key area and represent a benchmark against which alternatives can be compared. In the concluding chapter, a critical overview of the results presented is given together with areas for both short and medium term further researc

Distributed human computation framework for linked data co-reference resolution

Distributed Human Computation (DHC) is a technique used to solve computational problems by incorporating the collaborative effort of a large number of humans. It is also a solution to AI-complete problems such as natural language processing. The Semantic Web with its root in AI is envisioned to be a decentralised world-wide information space for sharing machine-readable data with minimal integration costs. There are many research problems in the Semantic Web that are considered as AI-complete problems. An example is co-reference resolution, which involves determining whether different URIs refer to the same entity. This is considered to be a significant hurdle to overcome in the realisation of large-scale Semantic Web applications. In this paper, we propose a framework for building a DHC system on top of the Linked Data Cloud to solve various computational problems. To demonstrate the concept, we are focusing on handling the co-reference resolution in the Semantic Web when integrating distributed datasets. The traditional way to solve this problem is to design machine-learning algorithms. However, they are often computationally expensive, error-prone and do not scale. We designed a DHC system named iamResearcher, which solves the scientific publication author identity co-reference problem when integrating distributed bibliographic datasets. In our system, we aggregated 6 million bibliographic data from various publication repositories. Users can sign up to the system to audit and align their own publications, thus solving the co-reference problem in a distributed manner. The aggregated results are published to the Linked Data Cloud

Iterative learning control for multiple point-to-point tracking application

A framework is developed which enables a general class of linear Iterative Learning Control (ILC) algorithms to be applied to tracking tasks which require the plant output to reach given points at predetermined time instants, without the need for intervening reference points to be stipulated. It is shown that superior convergence and robustness properties are obtained compared with those associated with using the original class of ILC algorithm to track a prescribed arbitrary reference trajectory satisfying the point-to-point position constraints. Experimental results using a non-minimum phase test facility are presented to confirm the theoretical findings

Experimentally supported 2D systems based iterative learning control law design for error convergence and performance

This paper considers iterative learning control law design for both trial-to-trial error convergence and along the trial performance. It is shown how a class of control laws can be designed using the theory of linear repetitive processes for this problem where the computations are in terms of Linear Matrix Inequalities (LMIs). It is also shown how this setting extends to allow the design of robust control laws in the presence of uncertainty in the along the trial dynamics. Results from the experimental application of these laws on a gantry robot performing a pick and place operation are also given

3D stroke rehabilitation using electrical stimulation and robotics

Stroke is the third leading cause of death and foremost cause of adult disability in the UK. A third of the surviving patients suffer from some degree of motor disability and depend on others to undertake daily activities. Conventional rehabilitation can mitigate this disability, but only 5% of the severely paralysed patients regain full upper limb function. Past studies have shown evidence of more effective technologies such as rehabilitation robotics and functional electrical stimulation (FES). Previous collaborative research at the University of Southampton developed a system that pioneered the use of FES with rehabilitation robots to assist planar upper limb stroke rehabilitation. Results from a clinical trial in 2008 have shown significant improvements in 5 stroke patients across a range of clinical outcome measures. Stimulation Assistance through iterative learning (SAIL) is a novel system that builds on this work, combining robotic therapy with the use of FES. This research has been possible through collaboration between engineers, physiotherapists and psychologists. The SAIL platform assists patients in performing 3D arm movements that are presented in a virtual reality setting and resemble daily activities. This assistance is provided through a supportive robotic system, together with FES applied to two muscles in the arm. Iterative learning control (ILC) schemes have been designed for the delivery of precisely-controlled FES to maximise the therapeutic effect of training. Preliminary tests with 9 unimpaired patients and 2 stroke patients have confirmed the control system’s high level of performance in assisting movement. These encouraging results are expected to transfer into effective treatment on 5 stroke patients who have recently started a clinical trial comprising 18 treatment sessions. A range of clinical measures are employed to assess the performance of patients pre- and post-treatment. In addition to the assessment of motor function, the Behavioural Inattention Test is being used to assess level and changes in visual neglect

Objective-Driven ILC for Point-to-Point Movement Tasks

A framework is developed which allows a general class of ILC algorithm to be applied to tasks which require the plant output to reach a given point in a set time. It is shown that superior convergence and robustness properties are obtained compared with those associated with using the original ILC algorithm to track an arbitrary reference trajectory satisfying the end-point conditions. Experimental results are presented to confirm the theoretical findings, and verify the favourable qualities of this novel approach to point-to-point movement control