Zhonglun Cai

Functional electrical stimulation mediated by iterative learning control and 3D robotics reduces motor impairment in chronic stroke

BackgroundNovel stroke rehabilitation techniques that employ electrical stimulation (ES) and robotic technologies are effective in reducing upper limb impairments. ES is most effective when it is applied to support the patients’ voluntary effort; however, current systems fail to fully exploit this connection. This study builds on previous work using advanced ES controllers, and aims to investigate the feasibility of Stimulation Assistance through Iterative Learning (SAIL), a novel upper limb stroke rehabilitation system which utilises robotic support, ES, and voluntary effort.MethodsFive hemiparetic, chronic stroke participants with impaired upper limb function attended 18, 1 hour intervention sessions. Participants completed virtual reality tracking tasks whereby they moved their impaired arm to follow a slowly moving sphere along a specified trajectory. To do this, the participants’ arm was supported by a robot. ES, mediated by advanced iterative learning control (ILC) algorithms, was applied to the triceps and anterior deltoid muscles. Each movement was repeated 6 times and ILC adjusted the amount of stimulation applied on each trial to improve accuracy and maximise voluntary effort. Participants completed clinical assessments (Fugl-Meyer, Action Research Arm Test) at baseline and post-intervention, as well as unassisted tracking tasks at the beginning and end of each intervention session. Data were analysed using t-tests and linear regression.ResultsFrom baseline to post-intervention, Fugl-Meyer scores improved, assisted and unassisted tracking performance improved, and the amount of ES required to assist tracking reduced.ConclusionsThe concept of minimising support from ES using ILC algorithms was demonstrated. The positive results are promising with respect to reducing upper limb impairments following stroke, however, a larger study is required to confirm this.

Iterative Learning Control for Multiple Point-to-Point Tracking Application

This paper considers a general class of linear iterative learning control (ILC) algorithm applied to tracking tasks which require the plant output to reach given points at predetermined time instants, without the specification of intervening reference points. A framework is developed in the frequency-domain in which the reference is updated between trials. 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 output constraints. Experimental results using a non-minimum phase test facility are presented to illustrate the theoretical findings.

Phase-lead iterative learning control algorithms for functional electrical stimulation-based stroke rehabilitation

A control system for stroke rehabilitation is developed which combines electrical stimulation with a robotic support system to provide assistance to stroke patients performing three-dimensional upper-limb reaching tasks in a virtual reality environment. The electrical stimulation is applied to two muscles in the subject’s arm using an iterative learning control scheme which learns from data collected over previous trials of the task in order to achieve accurate movement. The principal components of the system are described and experimental results confirm its feasibility for application to upper-limb stroke rehabilitation.

Output Information based Iterative Learning Control Law Design with Experimental Verification

This paper considers iterative learning control law design using the theory of linear repetitive processes. This setting enables trial-to-trial error convergence and along-the-trial performance to be considered simultaneously in the design. It is also shown that this design extends naturally to include robustness to unmodeled plant dynamics. The results from experimental application of these laws to a gantry robot performing a pick and place operation are given, together with a discussion of the positioning of this approach relative to alternatives and possible further research.

Using 2D systems theory to design output signal based iterative learning control laws with experimental verification

In this paper we use a 2D systems setting to develop new results on iterative learning control for linear plants, where it is well known in the subject area that a trade-off exists between speed of convergence and the response along the trials. Here we give new results by designing the control scheme using a strong form of stability for repetitive processes/2D linear systems known as stability along the pass (or trial). The resulting design computations are in terms of Linear Matrix Inequalities (LMIs) and they are also experimentally validated on a gantry robot. The control laws only use plant output information and hence the use of a state observer is avoided.

Iterative Learning Control Based on Relaxed 2-D Systems Stability Criteria

This brief develops a new algorithm for the design of iterative learning control law algorithms in a 2-D systems setting. This algorithm enables control law design for error convergence and performance, and is actuated by process output information only. Results are also given from the experimental application to a gantry robot.

A 2D Systems Approach to Iterative Learning Control with Experimental Validation

In this paper we use a 2D systems setting to develop new results on iterative learning control for linear plants. It is well known in the subject area that a trade-off exists between speed of convergence and transient response. Here we give new results in this area by designing the control scheme using a strong form of stability for repetitive processes/2D linear systems known as stability along the pass (or trial). The resulting design computations are in terms of Linear Matrix Inequalities (LMIs) and they are also experimentally validated on a gantry robot.

A 2D systems approach to iterative learning control for discrete linear processes with zero Markov parameters

In this article a new approach to iterative learning control for the practically relevant case of deterministic discrete linear plants with uniform rank greater than unity is developed. The analysis is undertaken in a 2D systems setting that, by using a strong form of stability for linear repetitive processes, allows simultaneous consideration of both trial-to-trial error convergence and along the trial performance, resulting in design algorithms that can be computed using linear matrix inequalities (LMIs). Finally, the control laws are experimentally verified on a gantry robot that replicates a pick and place operation commonly found in a number of applications to which iterative learning control is applicable.

Design & control of a 3D stroke rehabilitation platform

An upper limb stroke rehabilitation system is developed which combines electrical stimulation with mechanical arm support, to assist patients performing 3D reaching tasks in a virtual reality environment. The Stimulation Assistance through Iterative Learning (SAIL) platform applies electrical stimulation to two muscles in the arm using model-based control schemes which learn from previous trials of the task. This results in accurate movement which maximises the therapeutic effect of treatment. The principal components of the system are described and experimental results confirm its efficacy for clinical use in upper limb stroke rehabilitation.

Initial Input Selection for Iterative Learning Control

Iterative Learning Control algorithms have been shown to offer a high level of performance both theoretically and in practical applications. However the convergence of the error is generally highly dependent on the initial choice of input applied to the plant. Here techniques are applied which generate an optimal initial input selection, and the effect this has on the error over subsequent trials is examined. The approach is then applied experimentally on a gantry robot test facility.