Transactions of the Institute of Measurement and Control

The concentration of organic acids in anaerobic digesters is one of the most critical parameters for monitoring and advanced control of anaerobic digestion processes. Thus, a reliable online-measurement system is absolutely necessary. A novel approach to obtaining these measurements indirectly and online using UV/vis spectroscopic probes, in conjunction with powerful pattern recognition methods, is presented in this paper. An UV/vis spectroscopic probe from S::CAN is used in combination with a custom-built dilution system to monitor the absorption of fully fermented sludge at a spectrum from 200 to 750 nm. Advanced pattern recognition methods are then used to map the non-linear relationship between measured absorption spectra to laboratory measurements of organic acid concentrations. Linear discriminant analysis, generalized discriminant analysis (GerDA), support vector machines (SVM), relevance vector machines, random forest and neural networks are investigated for this purpose and their performance compared. To validate the approach, online measurements have been taken at a full-scale 1.3-MW industrial biogas plant. Results show that whereas some of the methods considered do not yield satisfactory results, accurate prediction of organic acid concentration ranges can be obtained with both GerDA and SVM-based classifiers, with classification rates in excess of 87% achieved on test data.

For the purpose of measuring the haemoglobin non-invasively, the dynamic spectrum (DS) method, which can minimize and hopefully eliminate the discrepancies among the individuals and the complicated conditions during measurement by near-infrared spectroscopy, was applied. DS is more accurate than the traditional method in haemoglobin non-invasive measurement, which was proved by theoretical derivation. In vivo measurements were carried out in 110 healthy volunteers and a criterion named the number of stable wavelengths was mentioned to evaluate the data’s quality. A back propagation neural network was used to establish the calibration model of haemoglobin concentration against DS data, which were pre-processed by some special algorithms. The correlation coefficient of the predicted values and the true values was 0.907, which demonstrates proof-of-concept for the ability of the DS method to monitor haemoglobin concentration changes non-invasively.

The conventional radial basis function (RBF) network optimization methods, such as orthogonal least squares or the two-stage selection, can produce a sparse network with satisfactory generalization capability. However, the RBF width, as a nonlinear parameter in the network, is not easy to determine. In the aforementioned methods, the width is always pre-determined, either by trial-and-error, or generated randomly. Furthermore, all hidden nodes share the same RBF width. This will inevitably reduce the network performance, and more RBF centres may then be needed to meet a desired modelling specification. In this paper we investigate a new two-stage construction algorithm for RBF networks. It utilizes the particle swarm optimization method to search for the optimal RBF centres and their associated widths. Although the new method needs more computation than conventional approaches, it can greatly reduce the model size and improve model generalization performance. The effectiveness of the proposed technique is confirmed by two numerical simulation examples.

In this paper we study parameter estimation problems for the output error moving average systems. In order to reduce calculation loads of the existing identification methods, an interactive stochastic gradient (ISG) algorithm is presented to estimate the parameters of the system model and the noise model, respectively, based on the interactive estimation theory. Since the ISG algorithm possesses a slow convergence rate and poor estimation accuracy, interactive gradient-based and interactive least-squares-based iterative algorithms are derived to enhance the parameter estimation performances of the ISG algorithm. The simulation results illustrate the effectiveness of the proposed algorithms.

This paper develops a co-ordinated electricity and heat dispatching model for a Microgrid under a day-ahead environment. In addition to operational constraints, network loss and physical limits are addressed in this model, which were always ignored in previous work. As an important component of the Microgrid, a detailed combined heat and power (CHP) model is developed. The part load performance of CHP is modelled by a curve fitting method. Furthermore, an electric heater is introduced into the model to improve the economy of the Microgrid operation and enhance the flexibility of the Microgrid by electricity–heat conversion. Particle swarm optimization is employed to solve this model for the operation schedule to minimize the total operational cost of the Microgrid by co-ordinating the CHP, electric heater, boiler and heat storage. The efficacy of the model and methodology is verified with different operation scenarios.

This paper investigates the probabilistic load flow (PLF) calculation with Latin hypercube sampling (LHS) technique for grid-connected induction wind power system. Considering the uncertainties of both loads and wind power outputs, firstly, probabilistic models of main components in wind power generation system are introduced. A combined iterative method for deterministic load flow is then extended to the PLF calculation for grid-connected induction wind power system, which facilitates simultaneous correction for the slip of induction generator and the nodal voltages during all iterations. Furthermore, to overcome the drawback of simple random sampling like excessive time consumption, LHS is combined with Monte Carlo simulation to execute the PLF. Finally, the proposed method is verified by an IEEE 14-bus system modified to include 20 wind turbines. Simulation results confirm the efficiency of the proposed method and reveal the impact of wind farm capacity on PLF results.

Traditional networked control systems (NCSs) analysis and design have been based on the single closed-loop configuration. This paper studies the modelling and stability of multi-input multi-output (MIMO) networked control systems (NCSs) with multiple channels. Unlike the NCSs based on the single close-loop configuration, there exist data packet dropout, data packet out-of-order and network-induced delay in every channel, which make multi-channel MIMO NCSs more complex. In order to solve these network-related non-deterministic issues, a general switched system model with unknown switched sequence for multi-channel MIMO NCSs is first proposed, which can not only describe the MIMO NCSs where the controller communicates with sensors and actuators through distinct channels, but also can describe the NCSs based on the single closed-loop configuration. Based on Lyapunov stability theory combined with linear matrix inequalities (LMIs) techniques, a sufficient condition is then derived for multi-channel MIMO NCSs to be asymptotical stable in term of a set of bilinear matrix inequalities. Furthermore, the proposed results are easily extended to the uncertain MIMO NCSs. Finally, simulation results confirm the feasibility and effectiveness of the proposed method.

This paper considers the mean square stabilization for a kind of network control system (NCS) in which there exist network-induced delay and stochastic packet dropout between sensor and controller. This kind of NCS is modelled as a Markovian switched system with two subsystems. Using the average dwell time method, we design a state feedback controller to guarantee that NCS be mean square exponentially stable. An illustrative example is provided to demonstrate the effectiveness of the proposed results.

In this paper we address the stabilization problem of a networked control system with signal-to-noise ratios (SNRs) constrained channels. The minimal SNRs required for stabilizability are obtained by a novel linear matrix inequality approach. Similar results are provided under circumstances of a constant delay and random packet loss. The effectiveness of the proposed approach is demonstrated with some numerical examples.

Considering the potentials of iterative learning control as a framework for industrial batch process control and optimization, a novel dynamic parameters-based quadratic criterion-iterative learning control (Q-ILC) is proposed in this paper. Firstly, Q-ILC with dynamic parameter is used to improve the performance of ILC. As a result, the proposed method can avoid the problem of initialization of the optimization controller parameters, in which a trial and error procedure is usually resorted to in the existing iterative algorithms used for the optimization of the batch process. Next, we make the first attempt to provide a rigorous description and proof to verify that the changes of the ILC policy converges with respect to the batch index number, which are normally validated only on the basis of the simulation results in the previous works. Lastly, an example is used to illustrate the performance and applicability of the proposed method.