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A control theory approach to the management of blood clotting speed using the anticoagulant warfarin is investigated. Proportional Integral (PI) and Proportional-Integral-Plus (PIP) controllers are developed for models identified from patient data. These are used to estimate treatment decisions subject to stochastic disturbances, model uncertainty and missed observations, the latter representing missed clinic appointments. The focus is on the relative performance of various integral-of-error (IOE) forms, which are used to track the target International Normalised Ratio (INR) at steady state. These are adapted in novel ways to handle the missed observations. Preliminary Monte Carlo simulations suggest that forward difference, mean and trapezoidal IOE forms with modified pole assignment, could lead to more desirable outcomes than the standard case, but the differences are relatively small and more research is required into scenarios leading to set point deviations.

Accurate characterization of radiation hotspots is a critical requirement for monitoring and decommissioning operations in the nuclear industry, particularly where the arrangement of contamination is complex, and the availability of ground-truth data is limited. This article develops a novel stochastic modelling approach that alleviates challenges often present in such operations. Initially, the experimentally derived angular responses of a collimated single detector apparatus at different energy regions (counts over radiation footprints) are expressed by two functions: the Fourier transform of a rectangular pulse (approximated by a sinc function) and a Moffat function. Subsequently, these are both framed within a Dynamic Linear Regression (DLR) model. The resulting Moffat/sinc-DLR models enhance the quality of the fit to experimental data, and improve the accuracy and resolution of radiation localization, thus showcasing the value of such methods for radiation characterization tasks.

An algorithm to investigate nonlinear systems with time delay variability is proposed. It automatically cycles through segments of an open loop experiment, capturing the directional change in the output via a pair of regression equations, and estimates the time delay accordingly. The method is a black-box, statistical approach that does not require a dynamic model nor any knowledge of the systematic causes of the time delay variability. It is designed for systems with approximately integrating behaviour, hence it is used in this article for the analysis of hydraulically actuated robotic manipulators with this characteristic and time-varying delays. In the context of control, the new algorithm provides insight into the variable time delay behaviour and hence can guide control design decisions e.g. by uncovering state dependencies.

Systems with time-varying time delays present a particularly challenging control problem. They have been observed across a wide array of domains, from hydraulic actuators to insulin delivery control systems. Control systems that address system time-delays, nonlinearities and uncertainty are the subject of much research but, whilst the specific concept of varying time delays is sometimes acknowledged (for example in the control of hydraulic manipulators), this appears to be less widely investigated than some other types of nonlinearity. In part motivated by recent research into internal multi-model control, as similarly applied to systems with unknown time-varying delays, the present work utilises a Gaussian radial basis function to switch between two or more partial controllers. Each partial controller is based on a linear model with a (time-invariant) time delay. The new algorithm is developed and evaluated via simulation using a non-minimal state space (NMSS) framework, with pole assignment as the design criterion. Simulation results suggest that it yields improved performance in comparison to a simpler switching approach and the equivalent linear control system. However, laboratory examples and further research into robustness and stability is required in the next step.

Experimental measurements to explore the effect of detector voltage, pulse-shape discrimination (PSD) threshold and detector shielding on time-of-flight measurements of the 252Cf neutron spectrum made with organic scintillation detectors are described. It is found that detector voltage has a major effect, whilst changing the PSD threshold and shielding the detectors to optimize sensitivity to the desired gamma-neutron correlation results in a small effect.

In Smart Grids (SG), Electricity Theft Detection (ETD) is of great importance because it makes the SG cost-efficient. Existing methods for ETD cannot efficiently handle data imbalance, missing values, variance and non-linear data problems in the smart meter data. Therefore, an effective integrated strategy is required to address underlying issues and accurately detect electricity theft using big data. In this work, a simple yet effective approach is proposed by integrating two different modules, such as data pre-processing and classification, in a single framework. The first module involves data imputation, outliers handling, standardization and class balancing steps to generate quality data for classifier training. The second module classifies honest and dishonest users with a Support Vector Machine (SVM) classifier. To improve the classifier’s learning trend and accuracy, a Bayesian optimization algorithm is used to tune SVM’s hyperparameters. Simulation results confirm that the proposed framework for ETD significantly outperforms previous machine learning approaches such as random forest, logistic regression and SVM in terms of accuracy.

Uncertain time-delays can reduce the performance of hydraulic manipulator control systems. Variations in the time delay between, for example, an applied voltage and the associated manipulator movement, may be caused by the internal dynamics of the system and other nonlinear characteristics, such as fluid compressibility, dead-band of the pump, valve flow properties and friction characteristics. Robot control that addresses system time-delays, nonlinearities and uncertainty is the subject of much research but, whilst the specific concept of varying time delays in hydraulic systems is sometimes acknowledged, it appears to be less widely investigated than other types of uncertainty. The present article discuses some of the issues involved, exemplified by a dual manipulator device used in the laboratory for research into nuclear decommissioning, and presents a review of the relevant control literature in this area.

A novel, semi-autonomous radiological scanning system for inspecting uncharacterised objects is presented. The research utilises commercially available off-the-shelf (COTS) electronic components, intended for use in relatively low radioactive dose environments. To illustrate the concept, a Microsoft Kinect, a Universal Robots UR3 (6 degree of freedom) manipulator and a Kromek RadAngel gamma radiation spectrometer are utilised. The new control software allows the remote operator to select the required object, before the robot arm autonomously scans it, enabling the generation of various radiological spectra shapes. These data inform the operator of any likely radioisotopes present and where they are located in the object.

Experimental measurements to explore the effect of detector voltage, pulse-shape discrimination (PSD) threshold and detector shielding on time-of-flight measurements of the 252Cf neutron spectrum made with organic scintillation detectors are described. It is found that detector voltage has a major effect, whilst changing the PSD threshold and shielding the detectors to optimize sensitivity to the desired gamma-neutron correlation results in a small effect.

Uncertain time-delays can reduce the performance of hydraulic manipulator control systems. Variations in the time delay between, for example, an applied voltage and the associated manipulator movement, may be caused by the internal dynamics of the system and other nonlinear characteristics, such as fluid compressibility, dead-band of the pump, valve flow properties and friction characteristics. Robot control that addresses system time-delays, nonlinearities and uncertainty is the subject of much research but, whilst the specific concept of varying time delays in hydraulic systems is sometimes acknowledged, it appears to be less widely investigated than other types of uncertainty. The present article discuses some of the issues involved, exemplified by a dual manipulator device used in the laboratory for research into nuclear decommissioning, and presents a review of the relevant control literature in this area.

Sliding Mode Control (SMC) systems are developed for a hydraulic manipulator. The control model is obtained via state-dependent parameter (SDP) system identification. In contrast to previous research using discrete-time SDP models, in which the model coefficients are functions of the sampling interval, the present work develops a new continuous-time approach. It is well known that for conventional SMC there is a trade-off between chattering and robust performance. Hence, a recently developed approach to address this problem is investigated, in which the controller is designed via a fractional exponent of the sliding surface. The approach is developed for both conventional and Nonsingular Terminal SMC (NTSMC). The new continuous version of the NTSMC algorithm successfully reduces chattering and provides the best overall performance of various SMC designs. However, for the preliminary experiments reported in this article, a PID lead-lag controller yields the lowest absolute errors, albeit at the cost of a higher control effort. Hence, given that dead-zone and other uncertainties provide the main motivation for use of SMC in this application, further research into the robustness of the new algorithm is required.

This work focuses on implementing the optimal power flow (OPF) problem, considering wind, solar and hydropower generation in the system. The stochastic nature of renewable energy sources (RES) is modelled using Weibull, Lognormal and Gumbel probability density functions. The system-wide economic aspect is examined with additional cost functions such as penalty and reserve costs for under and overestimating the imbalance of RES power outputs. Also, a carbon tax is imposed on carbon emissions as a separate objective function to enhance the contribution of green energy. For solving the optimization problem, a simple and efficient augmentation to the basic grey wolf optimization (GWO) algorithm is proposed, in order to enhance the algorithm's exploration capabilities. The performance of the new augmented GWO (AGWO) approach, in terms of robustness and scalability, is confirmed on IEEE-30, 57 and 118 bus systems. The obtained results of the AGWO algorithm are compared with modern heuristic techniques for a case of OPF incorporating RES. Numerical simulations indicate that the proposed method has better exploration and exploitation capabilities to reduce operational costs and carbon emissions.

Electricity load forecasting has always been a significant part of the smart grid. It ensures sustainability and helps utilities to take cost-efficient measures for power system planning and operation. Conventional methods for load forecasting cannot handle huge data that has a nonlinear relationship with load power. Hence an integrated approach is needed that adopts a coordinating procedure between different modules of electricity load forecasting. We develop a novel electricity load forecasting architecture that integrates three modules, namely data selection, extraction, and classification into a single model. First, essential features are selected with the help of random forest and recursive feature elimination methods. This helps reduce feature redundancy and hence computational overhead for the next two modules. Second, dimensionality reduction is realized with the help of a t-stochastic neighbourhood embedding algorithm for the best feature extraction. Finally, the electricity load is forecasted with the help of a deep neural network (DNN). To improve the learning trend and computational efficiency, we employ a grid search algorithm for tuning the critical parameters of the DNN. Simulation results confirm that the proposed model achieves higher accuracy when compared to the standard DNN.

This article concerns a previously developed, Fractional-order, Generalised Predictive Control (FGPC) approach to control system design. Here, fractional order concepts are utilised to extend the design flexibility of the FGPC algorithm, in comparison to conventional Generalised Predictive Control (GPC). The performance of FGPC is investigated via Monte Carlo simulation. With a focus on plots of the closed-loop eigenvalues, these results are utilised to develop recommendations for how to optimise the extra design coefficients introduced in the fractional order case. One of the simulation examples involves a hydraulically actuated robotic manipulator. Finally, FGPC methods are applied to control airflow in a laboratory 1 m by 2 m by 2 m forced ventilation environmental test chamber.

The well-known electrical analogy for thermal modelling is based on the observation that Fourier's equation for one dimensional heat transfer takes the same form as Ohm's law. This provides a system for creating and resolving complex heat transfer problems using an established set of physically-based laws. The present article illustrates the concept for adjacent rooms in a modern university building, and investigates some of the modelling issues involved. The electrical analogy is chosen so that the models can be extended and used for future research into demand-side control of multiple buildings on the university network, requiring a fast computation time. For illustrative purposes, the present article is limited to a relatively straightforward two-room system, for which the modelling equations are conveniently represented and solved using MATLAB-SIMULINK. The coefficients of this model are estimated from data using standard nonlinear optimisation tools. For comparison, the article also develops an equivalent multiple-input Transfer Function form of the model. Finally, suggestions are made for the inclusion of occupancy estimates in the model.

Smart grid provides an opportunity for customers as well as for utility companies to reduce electricity costs and regulate generation capacity. The success of scheduling algorithms mainly depends upon accurate information exchange between main grids and smart meters. On the other hand, customers are required to schedule loads, respond to energy demand signals, participate in energy bidding and actively monitor energy prices generated by the utility company. Strengthening communication infrastructure between the utility company and consumers can serve the purpose of consumer satisfaction. We propose a heuristic demand side management model for scheduling smart home appliances in an automated manner, to maximise the satisfaction of the consumers associated with it. Simulation results confirm that the proposed hybrid approach has the ability to reduce the peak-to-average ratio of the total energy demand and reduce the total cost of the energy without compromising user comfort.

This paper shows how a standard proportional-integral-plus controller, based on a non-minimal state space (NMSS) design, can be extended to reduce the effects of measurement noise and so yield smoother control inputs for automated drug delivery control applications. Use of a NMSS model for state variable feedback control design, in which all the states are based on the directly measured input and output variables, removes the need for state estimation. Nonetheless, a stochastic NMSS form, with a Kalman filter, can optionally be introduced to reduce the effect of measurement noise and so yield smoother control inputs. In this case, the Kalman filter attenuates measurement noise but does not address state disturbances. In this article, we propose a modification to the stochastic NMSS control system to enable the elimination of such state disturbances. This involves further extending the non-minimal state vector to include additional terms based on the innovations. We compare the deterministic, stochastic and extended stochastic NMSS controllers via a simulation of the control of anaesthesia using propofol.

A control theory approach to the management of the blood clotting speed using the anticoagulant Warfarin is investigated. Controllers are developed and analysed using hospital data from patients with chronic conditions under Warfarin anticoagulation treatment. Proportional Integral (PI) and Model Predictive (MPC) controllers are used to estimate treatment decisions. These controllers are adapted in a novel manner, to enable their use with missing or irregularly sampled data. The performance of the controllers is evaluated both using a simulation of the system and by retrospectively comparing actual decisions in the data to those suggested by the control algorithms. It is shown that when the blood clotting speed is within a target range, the decisions suggested by the control algorithms are similar to those actually made (by medical staff), so would likely have led to similar desirable outcomes. When the blood clotting speed is outside the desirable range and too high or too low, the control algorithms on average suggest lower, or higher inputs respectively. These suggestions are likely to lead to improved outcomes.

The robotic platform in this study is being used for research into assisted tele-operation for common nuclear decommissioning tasks, such as remote pipe cutting. It has dual, seven-function, hydraulically actuated manipulators mounted on a mobile base unit. For the new visual servoing system, the user selects an object from an on-screen image, whilst the computer control system determines the required position and orientation of the manipulators; and controls the joint angles for one of these to grasp the pipe and the second to position for a cut. Preliminary testing shows that the new system reduces task completion time for both inexperienced and experienced operators, in comparison to tele-operation. In a second contribution, a novel state-dependent parameter (SDP) control system is developed, for improved resolved motion of the manipulators. Compared to earlier SDP analysis of the same device, which used a rather ad hoc scaling method to address the dead-zone, a state-dependent gain is used to implement inverse dead-zone control. The new approach integrates input signal calibration, system identification and nonlinear control design, allowing for straightforward recalibration when the dynamic characteristics have changed or the actuators have deteriorated due to age.

A mobile camera is used to support an assisted teleoperation pipe–cutting system for nuclear decommissioning. The base system consists of dual–manipulators with a single mounted Kinect camera. The user selects the object from an on–screen image, whilst the computer control system automatically grasps the pipe with one end–effector and positions the second for cutting. However, the system fails in some cases because of data limitations, for example a partially obscured pipe in a challenging decommissioning scenario (simulated in the laboratory). Hence, the present article develops a new method to increase the use case scenarios via the introduction of mobile cameras e.g. for mounting on a drone. This is a non-trivial problem, with SLAM and ArUco fiducials introduced to locate the cameras, and a novel error correction method proposed for finding the ArUco markers. Preliminary results demonstrate the validity of the approach but improvements will be required for robust autonomous cutting. Hence, to reduce the pipe position estimation errors, suggestions are made for various algorithmic and hardware refinements.