This paper develops an observer-based predictive repetitive control system to track periodic reference signals or reject disturbances with bandlimited frequency content. The new design complements ...existing approaches to predictive control, where a model of the periodic disturbance is embedded in the controller. In particular, the new design, based on a novel combination of repetitive control and a disturbance observer, results in a significant improvement in design transparency and implementation simplicity. Although the design is undertaken using a state-space-model, frequency response analysis based on the sensitivity and complementary functions is used to demonstrate the characteristics of the repetitive control system for disturbance rejection, reference following and measurement noise attenuation. Moreover, operational constraints can be included in the design for applications where this feature is required. Simulation studies are given to highlight the closed-loop performance achievable in the presence of constraints. Experimental validation results from application to a two-joint robotic arm are also given and discussed.
Iterative learning control is a methodology applicable to systems which repeatedly track a specified reference trajectory defined over a finite time duration. Here the methodology is instead applied ...to the point-to-point motion control problem in which the output is only specified at a subset of time instants. The iterative learning framework is expanded to address this case, and conditions for convergence to zero point-to-point tracking error are derived. It is shown how the extra design freedom the point-to-point set-up brings allows additional input, output and state constraints to be simultaneously addressed, hence providing a powerful design framework of wide practical utility. Experimental results confirm the performance and accuracy that can be achieved, and the improvements gained over the standard ILC framework.
► Iterative learning control algorithms are developed for point-to-point motion control. ► These allow objectives involving the input, output and states to be simultaneously addressed. ► This results in a powerful framework of wide-ranging utility. ► Performance is evaluated using a non-minimum phase test facility and is shown to improve on the standard ILC set-up.
Drop-foot is characterised by an inability to lift the foot, and affects an estimated 3 million people worldwide. Current treatment methods include rigid splints, electromechanical systems, and ...functional electrical stimulation (FES). However, these all have limitations, with electromechanical systems being bulky and FES leading to muscle fatigue.
This paper addresses the limitations with current treatments by developing a novel orthosis combining FES with a pneumatic artificial muscle (PAM). It is the first system to combine FES and soft robotics for application to the lower limb, as well as the first to employ a model of their interaction within the control scheme. The system embeds a hybrid controller based on model predictive control (MPC), which combines FES and PAM components to optimally balance gait cycle tracking, fatigue reduction and pressure demands. Model parameters are found using a clinically feasible model identification procedure. Experimental evaluation using the system with three healthy subjects demonstrated a reduction in fatigue compared with the case of only using FES, which is supported by numerical simulation results.
•A drop foot device is developed that combines a pneumatic artificial muscle and functional electrical stimulation (FES) for the first time.•The combined system improves gait tracking compared to either approach used individually.•Combining soft robotics and FES reduces muscle fatigue when compared to purely FES.•Model predictive control can be used to optimize control parameters.
Electrode arrays are gaining increasing popularity within the rehabilitation and assistive technology communities, due to their potential to deliver selective electrical stimulation to underlying ...muscles. This paper develops the first model-based control strategy in this area, unlocking the potential for faster, more accurate postural control. Due to time-varying nonlinear musculoskeletal dynamics, the approach fuses model identification with iterative learning control (ILC), and employs a restricted input subspace comprising only those inputs deemed critical to task completion. The subspace selection embeds past experience and/or structural knowledge, with a dimension chosen to affect a trade-off between the test time and overall accuracy. Experimental results using a 40 element surface electrode array confirm accurate tracking of three reference hand postures.
This brief develops a disturbance observer-based repetitive control system using a non-minimal state-space (NMSS) realization where all state variables correspond to the system's input and output ...variables and past values. Tracking a periodic reference signal or rejection of a periodic disturbance signal is achieved by including a disturbance observer to estimate an input disturbance containing the same frequency characteristics. This new approach differs from previously published designs because it separates the design procedure into two tasks: first, stabilization via state feedback control; second, independent incorporation of the periodic modes via estimation of the disturbance. Moreover, the new design naturally contains an anti-windup mechanism when the control signal reaches its maximum or minimum value. Results from the experimental evaluation are given, including a comparison against a design that constructs a minimal state controller using an observer. These results demonstrate that the new method can deliver significant performance improvement, with excellent disturbance rejection and reference tracking.
Tremor is a rapid involuntary movement often seen in patients with neurological conditions such as multiple sclerosis and Parkinson's disease. This debilitating oscillation can be suppressed by ...applying functional electrical stimulation (FES) within a closed-loop control system. However, conventional implementations use classical control methods and have proved capable of only limited performance. This paper establishes the feasibility of embedding repetitive control (RC) action to exploit the capability of learning from experience to completely suppress tremor at the wrist via FES regulated co-contraction of wrist extensors/flexors. A nonlinear model structure and associated identification procedure are first proposed to guarantee stability and performance of the RC system. Then a linearizing control approach is developed to facilitate transparent RC design, together with a mechanism to preserve patients' voluntary intention. Experimental evaluation is performed with both unimpaired and neurologically impaired participants using a validated wrist-rig. For the former group, a novel electromechanical system is employed to induce tremor artificially. Results are bench-marked against a well-known classical filtering technique to establish the efficacy of the RC approach. These confirm that the proposed control system with the developed model identification procedure can increase tremor suppression by 43.3% compared with conventional filtering. In addition, the mechanism reduces the interference of RC action with voluntary motion by 20.2% compared with conventional filtering.
There is considerable interest in understanding the biological mechanisms that regulate carbon exchanges between the land and atmosphere, and how these exchanges respond to climate change. An ...understanding of soil microbial ecology is central to our ability to assess terrestrial carbon cycle-climate feedbacks, but the complexity of the soil microbial community and the many ways that it can be affected by climate and other global changes hampers our ability to draw firm conclusions on this topic. In this paper, we argue that to understand the potential negative and positive contributions of soil microbes to land-atmosphere carbon exchange and global warming requires explicit consideration of both direct and indirect impacts of climate change on microorganisms. Moreover, we argue that this requires consideration of complex interactions and feedbacks that occur between microbes, plants and their physical environment in the context of climate change, and the influence of other global changes which have the capacity to amplify climate-driven effects on soil microbes. Overall, we emphasize the urgent need for greater understanding of how soil microbial ecology contributes to land-atmosphere carbon exchange in the context of climate change, and identify some challenges for the future. In particular, we highlight the need for a multifactor experimental approach to understand how soil microbes and their activities respond to climate change and consequences for carbon cycle feedbacks.
Tropical peatlands contain one of the largest pools of terrestrial organic carbon, amounting to about 89,000 teragrams (1 Tg is a billion kilograms). Approximately 65 per cent of this carbon store is ...in Indonesia, where extensive anthropogenic degradation in the form of deforestation, drainage and fire are converting it into a globally significant source of atmospheric carbon dioxide. Here we quantify the annual export of fluvial organic carbon from both intact peat swamp forest and peat swamp forest subject to past anthropogenic disturbance. We find that the total fluvial organic carbon flux from disturbed peat swamp forest is about 50 per cent larger than that from intact peat swamp forest. By carbon-14 dating of dissolved organic carbon (which makes up over 91 per cent of total organic carbon), we find that leaching of dissolved organic carbon from intact peat swamp forest is derived mainly from recent primary production (plant growth). In contrast, dissolved organic carbon from disturbed peat swamp forest consists mostly of much older (centuries to millennia) carbon from deep within the peat column. When we include the fluvial carbon loss term, which is often ignored, in the peatland carbon budget, we find that it increases the estimate of total carbon lost from the disturbed peatlands in our study by 22 per cent. We further estimate that since 1990 peatland disturbance has resulted in a 32 per cent increase in fluvial organic carbon flux from southeast Asia--an increase that is more than half of the entire annual fluvial organic carbon flux from all European peatlands. Our findings emphasize the need to quantify fluvial carbon losses in order to improve estimates of the impact of deforestation and drainage on tropical peatland carbon balances.
Iterative learning control (ILC) is a well-established methodology which has proved successful in achieving accurate tracking control for repeated tasks. However, the majority of ILC algorithms ...require a nominal plant model and are sensitive to modelling mismatch. This paper focuses on the class of gradient-based ILC algorithms and proposes a data-driven ILC implementation applicable to a general class of nonlinear systems, in which an explicit model of the plant dynamics is not required. The update of the control signal is generated by an additional experiment executed between ILC trials. The framework is further extended by allowing only specific reference points to be tracked, thereby enabling faster convergence. Necessary convergence conditions and corresponding convergence rates for both approaches are derived theoretically. The proposed data-driven approaches are demonstrated through application to a stroke rehabilitation problem requiring accurate control of nonlinear artificially stimulated muscle dynamics.
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