Closed-loop Data Research Articles

Plasma-insulin-cognizant adaptive model predictive control for artificial pancreas systems

An adaptive model predictive control (MPC) algorithm with dynamic adjustments of constraints and objective function weights based on estimates of the plasma insulin concentration (PIC) is proposed for artificial pancreas (AP) systems. A personalized compartment model that translates the infused insulin into estimates of PIC is integrated with a recursive subspace-based system identification to characterize the transient dynamics of glycemic measurements. The system identification approach is able to identify stable, reliable linear time-varying models from closed-loop data. An MPC algorithm using the adaptive models is designed to compute the optimal exogenous insulin delivery for AP systems without requiring any manually-entered meal information. A dynamic safety constraint derived from the estimation of PIC is incorporated in the adaptive MPC to improve the efficacy of the AP and prevent insulin overdosing. Simulation case studies demonstrate the performance of the proposed adaptive MPC algorithm.

Research on Closed-Loop Identification of Time-Delay Systems Based on High-Frequency Test Signals

To get the accurate identification of time delay in closed-loop control system widely existing in industry processes, a method is presented by using the auto regressive exogenous (ARX) model with high frequency test signals. The mean filtering is applied on the sampled data to eliminate the effects of the colored noise existed in the data. Simulation results show that the proposed method can obtain the system time delay effectively. Furthermore, to evaluate the performance of the proposed method in physical systems, a closed-loop control system of single-tank with obvious time-delay is constructed. The influences of white noise test signals with different frequencies on the identifiability of closed-loop systems are discussed. The results on experimental data show that the proposed method can effectively identify the time delay based on closed-loop data.

Recovering Markov models from closed-loop data

Situations in which recommender systems are used to augment decision making are becoming prevalent in many application domains. Almost always, these prediction tools (recommenders) are created with a view to affecting behavioural change. Clearly, successful applications actuating behavioural change, affect the original model underpinning the predictor, leading to an inconsistency. This feedback loop is often not considered in standard machine learning techniques which rely upon machine learning/statistical learning machinery. The objective of this paper is to develop tools that recover unbiased user models in the presence of recommenders. More specifically, we assume that we observe a time series which is a trajectory of a Markov chain R modulated by another Markov chain S, i.e. the transition matrix of R is unknown and depends on the current state of S. The transition matrix of the latter is also unknown. In other words, at each time instant, S selects a transition matrix for R within a given set which consists of known and unknown matrices. The state of S, in turn, depends on the current state of R thus introducing a feedback loop. We propose an Expectation–Maximisation (EM) type algorithm, which estimates the transition matrices of S and R. Experimental results are given to demonstrate the efficacy of the approach.

Estimating models with high-order noise dynamics using semi-parametric weighted null-space fitting

Standard system identification methods often provide inconsistent estimates with closed-loop data. With the prediction error method (PEM), this issue is solved by using a noise model that is flexible enough to capture the noise spectrum. However, a too flexible noise model (i.e., too many parameters) increases the model complexity, which can cause additional numerical problems for PEM. In this paper, we consider the weighted null-space fitting (WNSF) method. With this method, the system is first modeled using a non-parametric ARX model, which is then reduced to a parametric model of interest using weighted least squares. In the reduction step, a parametric noise model does not need to be estimated if it is not of interest. Because the flexibility of the noise model is increased with the sample size, this will still provide consistent estimates in closed loop and asymptotically efficient estimates in open loop. In this paper, we prove these results, and we derive the asymptotic covariance for the estimation error obtained in closed loop, which is optimal for an infinite-order noise model. For this purpose, we also derive a new technical result for geometric variance analysis, instrumental to our end. Finally, we perform a simulation study to illustrate the benefits of the method when the noise model cannot be parametrized by a low-order model.

A New Approach of Data-Driven Controller Tuning Method By Using Virtual IMC Structure—Virtual Internal Model Tuning—

In this paper, we propose a new method for updating controllers by using closed loop data without a mathematical model of plant. The proposed method here requires only one-shot output data obtained in the closed loop experiment together with the feedback controller used in this initial experiment and the desired tracking transfer function. This method is derived based on the idea of the data-driven prediction and the concept of the internal model control (IMC) structure that only appears in the virtual closed loop system. Finally, the validity and the effectiveness of the proposed method are verified through experimental examples.

Evolutionary Design of a PID Controller for Two-Inertia Systems

In the vehicle development, it is important to shorten the development period of the powertrain components such as transmission, torque converter. In order to simulate real road loads of the powertrain components, it is necessary that the dynamometer of the drivetrain bench emulates the moment of inertia of a tire. To emulate the desired moment of inertia, controller parameters are calculated based on system identification in conventional scheme. In the aforementioned control scheme, high accuracy of system identification is required. This paper proposes a data-oriented control system whose controller parameters are directly calculated without system identification. In the proposed scheme, the fictitious exogenous signal is calculated using closed-loop data, and controller parameters and system parameters are simultaneously obtained. Finally, the effectiveness of the proposed scheme is verified by using a numerical example.

Parametric Identification Using Weighted Null-Space Fitting

In identification of dynamical systems, the prediction error method with a quadratic cost function provides asymptotically efficient estimates under Gaussian noise, but in general it requires solving a nonconvex optimization problem, which may imply convergence to nonglobal minima. An alternative class of methods uses a nonparametric model as intermediate step to obtain the model of interest. Weighted null-space fitting (WNSF) belongs to this class, starting with the estimate of a nonparametric ARX model with least squares. Then, the reduction to a parametric model is a multistep procedure where each step consists of the solution of a quadratic optimization problem, which can be obtained with weighted least squares. The method is suitable for both open- and closed-loop data, and can be applied to many common parametric model structures, including output-error, ARMAX, and Box–Jenkins. The price to pay is the increase of dimensionality in the nonparametric model, which needs to tend to infinity as function of the sample size for certain asymptotic statistical properties to hold. In this paper, we conduct a rigorous analysis of these properties: namely, consistency, and asymptotic efficiency. Also, we perform a simulation study illustrating the performance of WNSF and identify scenarios where it can be particularly advantageous compared with state-of-the-art methods.

Performance Analysis of Dynamic PCA for Closed-Loop Process Monitoring and Its Improvement by Output Oversampling Scheme

The performance of dynamic principal component analysis (DPCA)-based fault detection and diagnosis in a closed-loop system is studied and its improvement by the output oversampling scheme is proposed in this paper. By the subspace decomposition technique, DPCA with the closed-loop data for fault detection does not perform better than DPCA with the open-loop data. Moreover, using fault reconstruction based on DPCA to determine the root cause would also become invalid in the closed loop. To eliminate the adverse effect of feedback control on the performance of the DPCA model, a new algorithm that directly constructs DPCA based on the closed-loop data is investigated using the output oversampled data without excitations in the reference signals. The associated enhanced characteristics of the sampled data in the output oversampling scheme are analyzed. A simulated continuous stirred tank heater illustrates that the proposed algorithm can significantly improve the DPCA performance of process monitoring and fault reconstruction in closed-loop systems.

Creation of a micro cutting machine tool digital-twin using a cloud-based model-based PLM Platform: first results

The current work presents the first results when aiming to create a digital twin of a single edge micro cutting machine tool in a collaborative cloud-based PLM platform. As starting point, this work takes the models created during the development and design phase of the machine tool, which date back to ongoing works that started three years ago. In order to mirror the physical device in a unified platform, the different models, created using different computational applications, have to be imported into a single collaborative PLM platform (3D Experience from Dassault Systems). The goal of integrating these models into a unified cloud-based platform is to achieve an interoperable model-based digital twin. The digital twin should allow to estimate and simulate the behavior of the machine under different cutting process conditions. Ultimately, it should also allow to estimate how the machined part will be. The PLM platform is the kernel to implement a closed-loop data flow between the digital and the physical domains.

Price coordination in closed-loop data supply chain

Price coordination in closed-loop data supply chain

Price Coordination in Closed-loop Data Supply Chain

By focusing on new features of data products and, based on game theoretical models, we study three pricing mechanisms' performance and their effects on the participants in the data industry from the data supply chain perspective. A win-win pricing strategy for the players in the data supply chain is proposed. We obtain analytical solutions in each pricing mechanism, including the decentralised and centralised pricing, Nash bargaining pricing, and revenue sharing mechanism. Our findings show that: 1) the decentralised pricing has the lowest performance; 2) although Nash bargaining pricing can achieve the centralised channel performance, the upstream data provider and downstream application provider can only equally divide the total channel profit; 3) revenue sharing mechanism, in which the data provider subsidises the application provider, can achieve the first best performance and divide the maximum profit arbitrarily. Accordingly, end-users benefit mostly from the bargaining pricing and revenue sharing.

Design and implementation of a hierarchical clustering-based CMAC-PID controller using closed-loop data

Design and implementation of a hierarchical clustering-based CMAC-PID controller using closed-loop data

Data-driven Minimum Variance Control Using Regulatory Closed-loop Data Based on the FRIT Method

The paper proposes data-driven minimum variance regulatory control using a Fictitious Reference Iterative Tuning (FRIT) method. The present work uses the inverse of a linearly parametrized controller. Thus, a general parametrization is realized, and the integrator is straightforwardly introduced. A numerical example shows an effectiveness of the proposed method.

PRM226 - DEVELOPMENT OF A METHOD FOR A STAR-SHAPED NETWORK META-ANALYSIS UNDER UNIDENTIFIABLE CONSISTENCY ASSUMPTION

In a star-shaped network, comparative evaluations among treatments using only indirect evidences were based on an unidentifiable consistency assumption, limiting the reliability of the results. We suggest a data imputation method as a sensitivity analysis to assess robustness of the results depending on an unknown degree of consistency. We attempted to fill data for missing randomized controlled trials (RCTs) that may potentially be linked into the unclosed loop in a star-shaped network. A number of effect sizes and their standard errors were generated from a potential distribution of the unknown direct effect size. Starting from an assumption that the unknown direct results would be perfectly consistent with the existing indirect results, we compared deviance information criterions (DIC) in fitted consistency and inconsistency models. The imputation was finalized when the DIC from the consistency model is close enough to that from the inconsistency model, suggesting a marginally acceptable level of overall inconsistency. We examined how the network meta-analysis (NMA) results after imputation could differ from that from the unclosed loop. For illustration, we took a closed loop data of streptokinase (SK), alteplase (tPA), and anistreptilase (ASPAC) from an existing network comparing thrombolytic drugs, and then eliminated data between tPA and ASPAC. The intentional missing data were re-imputed using the suggested method and the results were compared. The best drug to reduce mortality based on the original data where RCTs were present in all contrasts was ASPAC, followed by tPA and SK. When one contrast became disconnected, the ranking has changed to tPA, ASPAC, and SK. After the data imputation for the missing link, the results were coherent with that from the original closed loop where the result of inconsistency test was not significant (p-value≈0.43). The data imputation method can be useful to help assessing reliability of results from a star-shaped NMA.

Detection and diagnosis of model-plant mismatch in multivariable model-based control schemes

The extent of approximation in modelling a given process, characterized by the model-plant mismatch (MPM), amongst other factors, critically determines the performance of a model-based control scheme. It is necessary therefore to carry out model maintenance and correction on a regular basis. However, a complete re-identification is usually a costly exercise. Therefore, it is highly desirable to precisely determine the specific elements that are in mismatch and re-identify only those parameters. In the recent times, the plant-model ratio (PMR) was proposed as an effective metric for diagnosing MPM in single-input single-output (SISO) systems from closed loop data. The PMR facilitates unique detection of mismatch in gain, dynamics and delay. A straightforward application of PMR to multivariable closed-loop systems is challenging primarily due to the confounding effects of other inputs and loop-to-loop interactions under closed-loop conditions. Furthermore, the metric requires high-frequency excitation for identification of delay mismatch. In this work, we first present a method to overcome the latter requirement using Hilbert transform relation and partial cross-spectral densities. Subsequently, we present the key contribution of this work, that of generalizing the PMR approach to multivariable control systems. Two threshold-based hypothesis tests are presented for diagnosing mismatch in gain and dynamics. Three simulation case studies are presented to demonstrate the efficacy of the proposed method.

Price Coordination in Closed-Loop Data Supply Chain

In this paper, we study pricing mechanisms’ performance and their effects on the participants in the data industry. We obtain analytical solutions in three pricing mechanisms, including the decentralized and centralized pricing, Nash Bargaining pricing, and revenue sharing mechanism. Our first result show that the decentralized pricing has the lowest performance. We also find that revenue sharing mechanism can achieve the first best performance and divide the maximum profit arbitrarily. Nash Bargaining pricing can achieve the centralized channel performance, the upstream data provider and downstream application provider can only equally divide the total channel profit.

Hybrid algorithm for accumulated error suppression in open‐loop Doppler receiver

A hybrid algorithm is proposed to decrease the integration phase measurement error accumulation in open-loop Doppler receiver which is designed for space orbit determination and positioning applications when cooperated closed-loop system is unavailable. Firstly, adaptive-neuro-fuzzy inference system (ANFIS) module α is implemented for the interrupted closed-loop measurement data prediction. Then to improve the prediction accuracy of the ANFIS module α, an adaptive Kalman filter is employed for data fusion with predicted data from ANFIS module α and open-loop measurement data for complementation and corrections. Meanwhile, ANFIS module β is embedded in the Kalman filter for adaptive error compensation to optimise the filter performance. Finally, the time costly ANFIS computations are accelerated by reconfigurable software–hardware co-design module implemented in the receiver system to improve the computing capability and efficiency of system. Experiment results are analysed to demonstrate the effectiveness of proposed hybrid algorithm. It suppresses error accumulation in open-loop receiver phase measurement by 85.89% compared to the directly integration results when closed-loop system is off-line and has 61.02% enhancement compared to the algorithm without adaptive error compensations. Thus, the whole combinatory system accuracy is improved.

Fault diagnosis for processes with feedback control loops by shifted output sampling approach

Data-driven fault diagnosis of closed loop processes has been a challenge in the process control community. The issue of the interaction between the process model and the controller model exists in models directly identified from closed loop data, because for all the measured process outputs, no matter whether they are normal or faulty, they are fed back into the controllers so that the reconstruction-based contribution (RBC) as the fault diagnosis method has a severe fault smearing effect. This article proposes a novel sampling scheme which can significantly eliminate the adverse effect of modeling issues in feedback control. The identifiability condition of model parameters is satisfied in the new sampling framework so that the RBC recovers its efficiency even though the process runs under feedback control. Two benchmarks, a continuous stirred-tank heater process and the Tennessee Eastman challenge problem, are used to test the efficiency of the proposed method.

Plant–Model Mismatch Estimation from Closed-Loop Data for State-Space Model Predictive Control

The area of controller performance monitoring, assessment, and diagnosis for model predictive control (MPC) has seen an increase in interest in recent years. A frequently identified cause of degraded performance is mismatch between the plant model used in the controller and the true dynamics of the plant. Most recent research focuses on locating plant–model mismatch in order to reduce the considerable effort required to re-identify the model. In this paper, we present a novel autocovariance-based plant–model mismatch estimation approach for unconstrained MPC based on linear state space models. We show that (additive) plant–model mismatch can be quantified as the solution of an optimization problem which minimizes the discrepancy between the sample autocovariance of plant outputs and the corresponding value obtained from theoretical predictions. We illustrate our theoretical results with two simulation case studies, demonstrating good performance in estimating parametric mismatch.

Research on a reduced order closed-loop identification algorithms based on AIC for process with time delay

In view of the limitations of traditional closed-loop identification methods, and considering process with time delay, a new closed-loop identification method based on Akaike’s information criterion (AIC) was proposed.it is possible to identify process model using closed-loop data under the reference signal was being excited. Firstly, the time delay term is approximated by first-order Pade and the equivalent closed-loop transfer function of closed-loop system is calculated. Secondly, the high-order discrete model which is determined by proposed method based on AIC can be obtained by the identification of the real closed-loop system. The terminal identification model which is gained from after reduction of the high-order discrete model has the same model structures as the equivalent closed-loop transfer function. Finally, solving methods of process parameters could be deduced by coefficient of same power in the two typical time delay process. Feasibility, universality, high identification accuracy and practicability of the proposed method were verified by a simulation test. It achieves satisfying identification effect in the closed-loop system identification of temperature and liquid level in distillation column reactor.