Control Performance Assessment Research Articles

Development of ITER first wall heat load feedback control

Real-time monitoring and control of thermal loads on tungsten plasma-facing components is mandatory for ITER to avoid their overheating during plasma discharges. For the Start of Research Operations (SRO) phase, dedicated First Wall Heat Load Control (FWHLC) functions are included in the ITER Plasma Control System (PCS) to prevent the development of excessive plasma heat loads on the inertially cooled first wall (FW). In this work, we propose a FWHLC design with a model-based approach, which features a simplified thermal model for the FW armour. This control-oriented model simulates the thermal response of ITER FW to slow changes in plasma equilibrium and energy, which during ITER operation will be monitored by the real-time wide angle infrared camera system. This allows computationally efficient runs for rapid controller performance assessment but can also assist operation scenario design by pre-empting potential heat load issues. FWHLC is designed to react to measured and predicted FW temperatures overcoming predesigned limits with real-time variations of the plasma shape or auxiliary power references, aiming to reduce thermal loads before a premature plasma termination is required to protect the FW. The new scheme is tested in closed loop simulations, where perturbations to nominal ITER low current scenarios are introduced in the wall thermal model to trigger the response of FWHLC, supporting the effectiveness of the proposed policy.

Robust Model Predictive Control for an Ion Beam Shepherd in a large-debris removal mission

The increasing accumulation of space debris poses significant risks to spacecraft, making the development of effective debris mitigation technologies necessary. This paper explores the Ion Beam Shepherd (IBS) method as a potential contactless solution for deorbiting large debris objects. The IBS system concept involves a spacecraft equipped with an ion thruster to direct a controlled ion beam at the debris, generating a small force that gradually lowers its orbit. A proposed configuration of the chaser’s actuator system integrates radial and out-of-plane cold-gas thrusters along with in-track ion thrusters to enhance control and safety while maintaining low mission costs. A robust Model Predictive Control (MPC) strategy is implemented, using the theory of MPC for Tracking to ensure accurate positioning and effective deorbiting. This theoretical approach addresses uncertainties and perturbations to robustly guarantee safe distances between the chaser and the debris. Additionally, a new ray-marching-based algorithm is introduced to estimate the force and torque exerted by the ion beam on the target, considered as a 6 degrees of freedom object, improving simulation accuracy and control performance assessment. A comprehensive simulation of the deorbit of a large debris object is performed, demonstrating the potential of the IBS technology for future large-debris removal missions. This research advances the conceptual framework and control techniques for the IBS technology, advancing towards its future implementation in space debris mitigation.

A novel class of non-Gaussian system performance assessment and controller parameter tuning methods

Traditional variance-based control performance assessment (CPA) and controller parameter tuning (CPT) methods tend to ignore non-Gaussian external disturbances. To address this limitation, this study proposes a novel class of CPA and CPT methods for non-Gaussian single-input single-output systems, denoted as data Gaussianization (inverse) transformation methods. The idea of quantile transformation is used to transform the non-Gaussian data with the goal of maximizing mutual information into virtual Gaussian data. In addition, optimal system data for the virtual loop are mapped back to the actual non-Gaussian system using quantile inverse transformation. Furthermore, a CARMA model-based recursive extended least square algorithm and a CARMA model-based least absolute deviation iterative algorithm are used to identify virtual Gaussian and non-Gaussian system process models, respectively, while implementing the CPT. Finally, a unified framework is proposed for the CPA and CPT of a non-Gaussian control system. The simulation results demonstrate that the proposed strategy can provide a consistent benchmark judgment criterion (threshold) for different non-Gaussian noises, and the tuned controller parameters have good performance.

PID Control Assessment Using L-Moment Ratio Diagrams

This paper presents an application of L-moments and respective L-moment ratio diagrams (LMRD) to the task of control performance assessment (CPA). An L-moment ratio diagram is a graphical approach to the visualization of statistical properties for a given time series. Moreover, it enables comparing various data, showing their similarities and homogeneity. Simultaneously, CPA aims at measuring the control loop quality, supporting decision-making about their tuning and maintenance. This paper shows that control system quality can be efficiently visualized using LMRDs. The method was analyzed using simulations and further validated at a real chemical engineering industrial ammonia synthesis plant.

A Qualitative and Comparative Performance Assessment of Logically Centralized SDN Controllers via Mininet Emulator

An alternative networking approach called Software Defined Networking (SDN) enables dynamic, programmatically efficient network construction, hence enhancing network performance. It splits a traditional network into a centralized control plane and a configurable data plane. Because the core component overseeing every data plane action is the controller in the control plane, which may contain one or more controllers and is thought of as the brains of the SDN network, controller functionality and performance are crucial to achieve optimal performances. There is much controller research available in the existing literature. Nevertheless, no qualitative comparison study of OpenFlow-enabled distributed but logically centralized controllers exists. This paper includes a quantitative investigation of the performance of several distributed but logically centralized SDN controllers in custom network scenarios using Mininet, as well as a thorough qualitative comparison of them. More precisely, we give a qualitative evaluation of their attributes and classify and categorize 13 distributed but logically centralized SDN controllers according to their capabilities. Additionally, we offer a comprehensive SDN emulation tool, called Mininet-based SDN controller performance assessment, in this study. Using six performance metrics—bandwidth, round-trip time, delay, jitter, packet loss, and throughput—this work also assesses five distributed but logically centralized controllers within two custom network scenarios (uniform and non-uniform host distribution). Our analysis reveals that the Ryu controller outperforms the OpenDayLight controller in terms of latency, packet loss, and round-trip time, while the OpenDayLight controller performs well in terms of throughput, bandwidth, and jitter. Throughout the entire experiment, the HyperFlow and ONOS controllers performed worst in all performance metrics. Finally, we discuss detailed research findings on performance. These experimental results provide decision-making guidelines when selecting a controller.

Data‐driven plant‐model mismatch quantification in closed‐loop system based on output predictions

AbstractThe assessment and diagnosis of controller performance for model‐based closed‐loop control systems has received considerable attention in recent years. A recognized factor dilapidating the controller performance is mismatching the true dynamical model of the plant and the mathematic model employed in the controller. In order to reduce the heavy effort to re‐identify the entire model, a large amount of recent works have been focusing on locating the mismatch. To further improve the mathematic model as well as controller performance, in this article, we provide a novel prediction‐based plant‐model mismatch quantification approach, which belongs to the class of moment match methodology. In particular, two cases of output prediction are considered: one‐step ahead prediction and multistep ahead prediction. Compared with existing efforts along the same line of mismatch quantification, when using the one‐step ahead prediction, our method shows an advantage of light computational complexity. On the other hand, when utilizing multistep predictions, it shows better convergence and robustness than the former, especially in the case of structural mismatches, and the long‐term prediction capability of the model is employed. With both predictions and consideration of the existence of unknown noise models in practice, we show that the plant‐model mismatch and unknown parameters of noise models can be quantified as the solution of a minimization issue that penalizes the discrepancy between the sample of plant outputs and the predictions calculated by considering the plant‐model mismatch and noise model parameters. Several examples are provided to demonstrate the effectiveness of the proposed methods, respectively.

Performance Assessment of a MPID controller in Microgrid Systems for Load Frequency Control

This study provides a novel methodology to design an A.C. off-grid multi-microgrid (MMG) system and suggests an analytical method for load frequency management utilizing a multistage PID (MPID) controller based on the sine cosine algorithm. The unique benefits of an MMG system are similar to those of renewable energy sources (RES), such as solar system models, wind turbine models, etc. There is a conflict between the MMG system's source and the load requirement because of the erratic properties of renewable energy sources. The irregular nature of renewable energy sources has an impact on the frequency of the system. Therefore, it is very difficult these days to check the frequency in a MMG system. This article uses the SCA-based MPID to tune the controller parameter for secondary frequency control, despite all of the complexity. In the power system, LFC (load frequency control ) is achieved by regulating active power. The dynamic response of the conventional PI-PID controller has been used to assess the effectiveness of the proposed MPID controller. Using the sine and cosine approaches, the parameters are optimized.

Performance-Driven Safe Bayesian Optimization for Intelligent Tuning of High-Order Cascade Systems

Automatic parameter tuning of high-order cascade controllers suffers from sampling inefficiency and strong couplings. This work presents a performance-driven, systematic, and safe intelligent parameter-tuning framework for high-order cascade systems. To achieve data-efficient and noise-robust hyper-parameter calibration, an intelligent tuning framework based on Bayesian optimization is proposed to calibrate the control parameters from the buffer of performance-metric measurements. Furthermore, we improve the Bayesian-optimization-based framework in three aspects, involving effectiveness, security, and sampling efficiency. Firstly, a comprehensive control performance assessment combining the error-integral and statistical performance criteria is designed to evaluate the cost of a sampling point in terms of final precision, response rapidity, and vibration. Meanwhile, the security of sampling exploration is heightened by imposing composite hard parametric and soft performance-metric (maximum input, overshoot, etc.) constraints on the acquisition points. Additionally, a hierarchical optimization strategy is proposed to further boost sampling exploitation by alternatively tuning the control parameters of subsystems and refining the constraint on the sampling space. The proposed framework is applied to automatic parameter tuning of high-order dynamic-surface-based backstepping control combined with anti-disturbance rejection control. The comparative simulation results demonstrate that the proposed intelligent tuning possesses superiorities in sampling efficiency and security for parameter calibration problems.

Assessment of Cyber-Physical Inverter-Based Microgrid Control Performance under Communication Delay and Cyber-Attacks

The integration of communication infrastructures into traditional power systems, transforming them into cyber-physical power systems (CPPS), accentuates the significance of communication in influencing system performance and sustainability. This paper presents a versatile, innovative cyber-physical co-simulation framework that integrates the physical power system and communication networks, uniting OPAL-RT, a network simulator (ns3), and Docker containers into a sophisticated platform, facilitating intensive studies into CPPS dynamics. The proposed experimental study provides an innovative way to assess the frequency control response of a cyber-physical inverter-based microgrid (MG), addressing the MG sustainability challenges. We consider diverse real-world scenarios, focusing on communication delays and distributed denial of service (DDoS) attacks within the communication channels. We propose a precise ns3-based communication model that bridges the MG’s primary and secondary control layers, an aspect often overlooked in previous studies; this is a noteworthy contribution to elucidating the adverse impacts of communication latency on MG frequency performance. The experimental results demonstrate the effectiveness of the centralized secondary controller in eliminating the frequency deviations. Furthermore, the findings offer insights into stable and unstable regions, revealing how the communication delay value affects the frequency stability under different operating conditions. In addition, the developed real-time DDoS attacks model within the proposed communication surface unveils crucial insights into the MG’s resilience to cyber threats. This work’s revelations offer a foundational awareness of MG vulnerabilities, paving the way for designing robust and resilient communication networks and control strategies within the cyber-physical inverter-based microgrids.

Multi-objective Machine Learning for control performance assessment in PID control loops

Control Performance Assessment (CPA) is a critical endeavor in industrial processes, ensuring optimal functioning of control systems. Traditionally, CPA has been addressed through solutions using some control performance indicators. Nowadays, the integration of data science and machine learning has emerged as a viable alternative, particularly in classification tasks related to CPA. That is, in a binary classification scheme, the goal is to predict whether incoming data from the control loop belongs to class 0 or 1, representing the absence or presence of an anomaly (performance degradation). In such a case, a trade-of between false positives and false negatives should be obtained, via the training phase of a given supervised machine learning structure for example. Usually, this is a conflicting trade-of, where multi-objective optimization techniques in the training phase of such learners could bring interesting results. In this paper, we explore the usability of multi-objective optimization training in machine learning, for control performance assessment classification. A database describing 30 control performance indicators (features) in a PID control loop is used. The obtained results indicate that the proposed approach could bring interesting applications to improve the performance of CPA classification systems.

Fractional order for Multi-criteria Control Performance Assessment

This paper presents an alternative way to do control performance assessment (CPA). The task to measure controller quality has to meet contradictory goals. Generally, the tuning of any controller means reaching a compromise between the accuracy and speed. The demanded ratio between them depends on the process requirements, technological limitations and just the engineering skills. Two fundamental indexes, like the overshoot and settling time fit perfectly into the picture. Considered investigations follow this research path, but with the use of modern measures: L-moments, tail index and ARFIMA filter fractional order estimator. The assessment uses two dimensional Index Ratio Diagrams (IRD), which allow to present and compare contradictory measures in a single diagram. They also allow to define new multi-criteria index able to compare different loops. The validation is compared against commonly used integral measures: means square and absolute errors.

Data-Based Control Synthesis and Performance Assessment for Moored Wave Energy Conversion Systems: The PeWEC Case

With a model-based control strategy, the effectiveness of the associated control action depends on the availability of a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">representative</i> control-oriented model. In the case of floating offshore wave energy converters (WECs), the device response depends upon the interaction between mooring system, any mechanical parts, and the hydrodynamics of the floating body. This study proposes an approach to synthesise WEC controllers under the effect of mooring forces building a representative data-based linear model able to include any relevant dynamics. Moreover, the procedure is tested on the moored pendulum wave energy converter (PeWEC) by means of a high-fidelity mooring solver, OrcaFlex (OF). In particular, the control action is computed with and without knowledge of the mooring influence, in order to analyse and elucidate the effect of the station-keeping system on the harvested energy. The performance assessment of the device is achieved by evaluating device power on the resource scatter characterising Pantelleria, Italy. The results show the relevance of the mooring dynamics on the device response and final set of control parameters and, hence, a significant influence of the station-keeping system on control synthesis and extracted mechanical power.

Comprehensive Exploration and Performance Assessment of SDN Controller

Comprehensive Exploration and Performance Assessment of SDN Controller

Study on levitation control performance assessment of maglev train based on minimum entropy

In order to meet the strict requirements of the reliability and safety of the commercial operation of the maglev train, it is necessary to carry out the control performance evaluation of the suspension system. However, at present, variance is often used as the evaluation index of control system. This method cannot get accurate performance evaluation results when the system is affected by non-Gaussian disturbance. Therefore, aiming at the non-Gaussian control performance evaluation of maglev train suspension system, a control performance evaluation method of maglev train suspension system based on minimum entropy is proposed in this paper. In view of the non-Gaussian characteristics in the operation data of maglev train suspension system, this paper introduces the feedback invariant entropy of non-Gaussian system, and then realizes the evaluation and analysis of the control performance of maglev train suspension system based on the minimum entropy criterion. Finally, combined with the evaluation and analysis of the operation data of Changsha medium and low-speed maglev train, this paper verifies the effectiveness and accuracy of the proposed performance evaluation method for the control performance evaluation of maglev train suspension system.

Stochastic stability analysis of legged locomotion using unscented transformation

In this manuscript, we present a novel method for estimating the stochastic stability characteristics of metastable legged systems using the unscented transformation. Prior methods for stability analysis in such systems often required high-dimensional state space discretization and a broad set of initial conditions, resulting in significant computational complexity. Our approach aims to alleviate this issue by reducing the dimensionality of the system and utilizing the unscented transformation to estimate the output distribution. This technique allows us to account for multiple sources of uncertainty and high-dimensional system dynamics, while leveraging prior knowledge of noise statistics to inform the selection of initial conditions for experiments. As a result, our method enables the efficient assessment of controller performance and analysis of parametric dependencies with fewer experiments. To demonstrate the efficacy of our proposed method, we apply it to the analysis of a one-dimensional hopper and an underactuated bipedal walking simulation with a hybrid zero dynamics controller.

Practical implementation of computationally-efficient machine learning-based control performance assessment system for a class of closed loop systems

This paper presents the concept of the computationally-efficient reduced order control performance assessment (RO-CPA) system for automatic detection of industrial single closed loop control systems whose performance could be not acceptable due to a poor tuning of the controller. The proposed machine learning (ML) based classification system is derived for a broad class of industrial control closed loops based on the proportional–integral–derivative (PID) controller. Apart from a high accuracy of the classification of the closed loop performance, the proposed RO-CPA system is derived to meet the requirements of low computational complexity and low memory usage. These requirements allow for practical implementation of RO-CPA in devices with low computational and memory resources, such as industrial programmable logic controllers (PLC) operating in manufacturing systems or embedded autonomous systems with dedicated PID-based control loops. This paper shows the rigorous ML-based design of the proposed RO-CPA system and the results of the validation of its classification accuracy. Additionally, an example of a practical implementation within a PLC in the form of a general purpose library function block is presented and the final experimental validation is made using a part of laboratory heat exchange and distribution setup.

Effect of a visual dual task on postural stability-A comparative study using linear and nonlinear methods.

The dual-task experimental paradigm is used to study the attentional demands of postural control. Postural control is impaired in poststroke patients, and dual-task balance studies address the visual needs of postural control in stroke patients. A nonlinear approach can help us understand the overall behavior of the dynamic system. A total of 20 chronic stroke patients and 20 healthy subjects with similar age, height, and weight participated in this study. The stability and complexity of postural control were assessed using linear and nonlinear methods. All data and parameters (center of pressure [COP] velocity, anteroposterior and mediolateral directions displacement, length of COP path, and phase plane) were analyzed using the Kolmogorov-Smirnov test. When postural control was examined based on linear analysis, the results showed that the main effect of the group was not significant, but the main impact of position was significant for all parameters of the COP variation (p < 0.05). Examination of postural control based on nonlinear analysis also showed that the main effect of the group was not significant, and the main effect of status was significant only for the parameters of approximate entropy in both directions and short-term Lyapunov view in the anterior-posterior direction (p < 0.05). According to the results of this study, the assessment of postural control and gait performance in poststroke patients, as well as the dual tasks they have to perform in daily life, is crucial for their independence in activities of daily living.

Data driven performance monitoring and retuning using PID controllers

This paper presents a joint methodology for performance assessment and retuning of PID controllers. The methods are data-driven and based on the set-point response collected during operation or tests, using as reference input steps, ramps, and other signals. For each set-point response, the IAE (Integral of Absolute Error) is computed and a statistical test is evaluated to check if performance is below a given threshold. A data-driven design method is applied if new controller gains are required to resume the performance, using closed-loop data and a reference model obtained also from data under operation with good performance. Both monitoring and design require only closed-loop data, and statistical tests are used to provide evidence that monitoring and retuning were successful. Three applications for two different pilot plants are presented, evaluating: (i) flow, level, and pressure loops, (ii) outer and inner loops, (iii) validation of closed-loop data, (iv) performance recovery after introducing faults that reduce performance, (v) performance monitoring and retuning for three levels of performance. As illustrated, the proposed methodology can be applied directly to closed loops even in the presence of very noisy signals, with no assumptions about the design method used for the controller design.

Application of Machine Learning to Performance Assessment for a Class of PID-Based Control Systems

In this paper, a novel machine learning derived control performance assessment (CPA) classification system is proposed. It is dedicated for a wide class of PID-based control industrial loops with processes exhibiting dynamical properties close to second order plus delay time (SOPDT). The proposed concept is very general and easy to configure to distinguish between acceptable and poor closed loop performance. This approach allows for determining the best (but also robust and practically achievable) closed loop performance based on very popular and intuitive closed loop quality factors. Training set can be automatically derived off-line using a number of different, diverse control performance indices (CPIs) used as discriminative features of the assessed control system. The proposed extended set of CPIs is discussed with comprehensive performance assessment of different machine learning based classification methods and practical application of the suggested solution. As a result, a general-purpose CPA system is derived that can be immediately applied in practice without any preliminary or additional learning stage during normal closed loop operation. It is verified by practical application to assess the control system for a laboratory heat exchange and distribution setup.

Contribution to strengthening Bus voltage stability and power exchange balance of a decentralized DC-multi-microgrids: Performance assessment of classical, optimal, and nonlinear controllers for hybridized energy storage systems control

Stability is the primary goal in standalone power systems due to the high penetration and uncertainties of renewable energy resources, so the system reliability is affected, and the hybrid energy storage system (HESS) and diesel generator (DG) compensate for renewables' failures. This study contributes to managing and controlling DC-coupled multi-Microgrids fed by Photovoltaic, HESS, and DG resources and subjected to pulsed loading and uncertain PV outputs, besides battery SOCs and DG fuel constraints. The Dual-loop control structure compares classical Proportional Integral (CPI), Super-Twisting-Sliding-Mode-Control (ST-SMC), and Linear Quadratic Regulator with Integral Action (LQR-I) to evaluate the robustness and control performance of the HESS. For the supercapacitor (SC), a hysteresis current control (HCC) tracks its setpoint currents using a frequency decoupling-based power-split control. Matlab simulations confirmed system reliability by combining controllers with different Key Performance Indicators, where adopting the LQR-I for bus voltage regulation and the ST-SMC for HESS current control outperformed the other combinations and revealed superior system performance, improving Bus voltage regulation, DG current tracking, overall system efficiency, and loading convergence by 67.63(%), 83.63(%), 77.92(%), and 2.72(%), respectively. Instead, the basic CPI enhanced battery and SC current control with 51.55 and 63.94 (%) compared to the winner scenario.