Article
Automation & Control Systems
Thiago Costa, Rafael R. Sencio, Luis Claudio Oliveira-Lopes, Flavio Silva
Summary: The work introduces a moving horizon virtual actuator (MHVA) as an extension of the classical virtual actuator technique, which can provide practical use of fault-tolerant control in the process industry. Experimental application demonstrates that the proposed technique can help the system recover performance under actuator fault scenarios and maintain the functionality of the nominal controller.
CONTROL ENGINEERING PRACTICE
(2021)
Article
Engineering, Marine
Yuchi Cao, Tieshan Li, Liying Hao
Summary: A comprehensive framework that combines moving horizon estimation (MHE) with model predictive control (MPC) is proposed to address the challenges in controller design for shipboard boom cranes. The framework considers disturbances and noise, and utilizes MHE to accurately estimate velocity information. The estimated information is then used in MPC to derive the optimal control law by solving a constrained optimal problem. The framework is verified through three typical scenarios with different disturbances and/or noises, and comparisons with other control approaches demonstrate its effectiveness.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Interdisciplinary Applications
Benjamin Karg, Sergio Lucia
Summary: Optimization-based methods for output-feedback control can handle multiple-input and multiple-output nonlinear systems with uncertainties and constraints. A combination of moving horizon estimation (MHE) and nonlinear model predictive control (NMPC) is powerful but requires solving two optimization problems at every sampling instant, which can be challenging. The proposed approach using deep neural networks reduces online computations and sensitivity analysis provides an approximate upper-bound for performance deviation due to approximation errors.
COMPUTERS & CHEMICAL ENGINEERING
(2021)
Article
Computer Science, Information Systems
Robab Ebrahimi Bavili, Ardashir Mohammadzadeh, Jafar Tavoosi, Saleh Mobayen, Wudhichai Assawinchaichote, Jihad H. Asad, Amir H. Mosavi
Summary: This study introduces a new approach for active fault-tolerant controller design for constrained nonlinear multi-variable systems, utilizing nonlinear model predictive controller and fault estimation method based on extended kalman filters. The proposed method successfully compensates for system faults and ensures robustness against plant faults and uncertainties.
Article
Engineering, Electrical & Electronic
Abolghasem Sardashti, Amin Ramezani
Summary: This paper discusses the fault tolerant control (FTC) problem in microgrids using online recursive reduced-order model estimation and Klaman Filter (KF) residual generation, as well as a fault-tolerant logic to switch between actual data and the internal model for system stability maintenance. The two-sided cumulative sum (CUSUM) sequential change detection algorithm is employed for fault detection.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2021)
Article
Computer Science, Interdisciplinary Applications
Lu Zhang, Junyao Xie, Stevan Dubljevic
Summary: This manuscript proposes moving horizon control and state/parameter estimation designs for pipeline networks modeled by partial differential equations (PDEs) with boundary actuation. The effectiveness of the proposed controller and estimator designs is demonstrated via numerical examples.
COMPUTERS & CHEMICAL ENGINEERING
(2023)
Article
Engineering, Chemical
Emanuel Bernardi, Eduardo J. Adam
Summary: This paper presents a model-based strategy for improving fault tolerance in non-linear chemical processes, utilizing observer-based fault detection and diagnosis as well as optimization-based model predictive control techniques to compensate for the effects of actuator and sensor faults, thus maintaining system stability in the presence of faults.
JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS
(2021)
Article
Automation & Control Systems
Lifan Li, Lina Yao, Hong Wang
Summary: This article investigates a fault-tolerant tracking control strategy for nonlinear probability density function (PDF) control systems. An adaptive fault diagnosis observer is proposed to estimate fault, disturbance, and state with packet losses. A new active fault-tolerant tracking controller based on model predictive control framework is designed for better adaptive fault-tolerant performance, and its validity is proven through a simulation study.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Wei Wang, Weijie Tian, Zhixiang Lu, Zheng Wang, Wei Hua, Ming Cheng
Summary: In this article, a fault-tolerant predictive control (FTPC) is proposed for the post-fault operation of five-leg dual-mover primary permanent-magnet linear motor drives with open circuit fault. The FTPC method aims to reduce the computation burden and improve the performances by distributing the reference current, determining global optimal mover voltage vectors (MVVs), modifying them as local optimal MVVs, and obtaining synthesized voltage vectors. Experimental results have verified the effectiveness of the proposed FTPC method.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2023)
Article
Construction & Building Technology
Hari S. Ganesh, Kyeongjun Seo, Hagen E. Fritz, Thomas F. Edgar, Atila Novoselac, Michael Baldea
Summary: A novel approach for energy-optimal control of indoor air quality in the presence of system-model mismatch is presented, using a combined MHE and MPC approach for simultaneous control of indoor air pollutants and energy consumption. The impact of model inaccuracies on MPC performance is addressed by predicting model parameters at each time instant based on past measurements. The control performance of the proposed framework is demonstrated through a case study, considering the impact of location and seasonality.
JOURNAL OF BUILDING ENGINEERING
(2021)
Article
Automation & Control Systems
Owais Khan, Ghulam Mustafa, Abdul Qayyum Khan, Muhammad Abid, Muhammad Ali
Summary: This article presents a fault-tolerant robust model-predictive control design for industrial processes, addressing issues such as time delays, model uncertainties, faults, and disturbances. It uses a parameter-dependent Lyapunov-Krasovskii functional to design state-feedback control, providing increased degrees of freedom for control design and ensuring robust stability and tracking performance.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Engineering, Multidisciplinary
Navid Vafamand, Mohammad Mehdi Arefi, Mohammad Hassan Asemani, Mohammad Sadegh Javadi, Fei Wang, Joao P. S. Catalao
Summary: This article investigates the issue of a state estimation-based fault-tolerant controller for direct current (dc) microgrids. A novel dual-Extended Kalman filter is proposed to simultaneously estimate the system states and faults, which are then deployed in a nonlinear Takagi-Sugeno fuzzy predictive controller. Experimental results demonstrate that the proposed method outperforms existing techniques in terms of fault tolerance and robustness.
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
(2022)
Article
Automation & Control Systems
Julian D. Schiller, Matthias A. Mueller
Summary: In this article, a suboptimal moving horizon estimator for nonlinear systems is proposed. The feasibility-implies-stability/robustness paradigm is transferred from model predictive control to moving horizon estimation, ensuring robust stability of the estimator. The design allows for the choice between a standard least squares approach and a time-discounted modification for improved theoretical guarantees. The proposed estimator is applied to a nonlinear chemical reactor process, showing significant improvement in estimation results with just a few iterations of the optimizer. Different solvers are employed to illustrate the flexibility of the design, and performance is compared with state-of-the-art fast moving horizon estimation schemes.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Agriculture, Multidisciplinary
Sara Kamali, Valerie C. A. Ward, Luis Ricardez-Sandoval
Summary: A nonlinear model predictive controller integrated with moving horizon estimation was implemented to maintain water quality parameters in recirculating aquaculture systems. The proposed control scheme showed acceptable performance in keeping the water quality parameters close to their setpoints under various scenarios.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2023)
Article
Engineering, Electrical & Electronic
Xueqing Wang, Zheng Wang, Zhixian Xu, Wei Wang, Bo Wang, Zhixiang Zou
Summary: This article proposes and develops a fault-tolerant scheme based on deadbeat predictive current control for dual three-phase permanent-magnet synchronous motor drives. By minimizing stator copper loss and maximizing the utilization of the remaining healthy switch, the proposed fault-tolerant control reduces additional copper loss caused by open-switch faults, resulting in a 20% increase in ultimate torque output.
IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS
(2021)
Article
Automation & Control Systems
Marcelo M. Morato, Igor M. L. Pataro, Marcus V. Americano da Costa, Julio E. Normey-Rico
Summary: This study proposes a Nonlinear Model Predictive Control (NMPC) scheme to plan social distancing measures and relaxations in order to mitigate the impact of COVID-19. The proposed scheme is based on an adapted data-driven SIRD model, which adequately represents the contagion curves and is validated with real data and simulation results.
Article
Automation & Control Systems
Tito L. M. Santos, Julio E. Normey-Rico
Summary: This paper presents a Generalised Dynamic Matrix Control (GDMC) algorithm for controlling open-loop unstable processes. Unlike Dynamic Matrix Control (DMC), GDMC can provide internally stable predictions through a generalised filtered approach. The paper shows the conditions for achieving internal stability and proposes a new data-driven filter design procedure. Two simulation case studies are presented to demonstrate the usefulness of GDMC.
Article
Operations Research & Management Science
Alireza Olama, Eduardo Camponogara, Paulo R. C. Mendes
Summary: This paper presents the distributed primal outer approximation (DiPOA) algorithm for efficiently solving sparse convex programming (SCP) problems with separable structures in a decentralized manner. The algorithm combines the relaxed hybrid alternating direction method of multipliers (RH-ADMM) algorithm with the outer approximation (OA) algorithm, and proposes improvements to control the quality and quantity of cutting planes that approximate nonlinear functions. DiPOA takes advantage of modern processors' multi-core architecture to accelerate optimization algorithms, and provides a practical solution for SCP in learning and control problems. The paper concludes with a performance analysis and numerical comparison with state-of-the-art solvers for distributed sparse logistic regression and quadratically constrained optimization problems.
JOURNAL OF GLOBAL OPTIMIZATION
(2023)
Article
Energy & Fuels
Diogo Ortiz Machado, William D. Chicaiza, Juan M. Escano, Antonio J. Gallego, Gustavo A. de Andrade, Julio E. Normey-Rico, Carlos Bordons, Eduardo F. Camacho
Summary: This work develops digital models of a commercial Fresnel Solar Collector (FSC) in an absorption cooling plant. Two modeling approaches are employed and their twinning/adaptation time and performance validation are evaluated. The results show that both models perform well and are suitable for control and optimization.
Article
Automation & Control Systems
Marcelo Menezes Morato, Julio Elias Normey-Rico, Olivier Sename
Summary: This article proposes an extrapolation algorithm based on recursive calculation using simple Taylor expansions to estimate the future values of qLPV scheduling parameters for a fixed prediction horizon. Sufficient conditions for convergent extrapolation are presented, and benchmark examples are used to illustrate the effectiveness of the algorithm, which is also compared to state-of-the-art techniques.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Paulo H. F. Biazetto, Gustavo A. de Andrade, Julio E. Normey-Rico
Summary: Concentrating solar power (CSP) plants with thermal energy storage (TES) systems are a sustainable technology to meet global energy consumption and reduce greenhouse gas emissions. An optimal control strategy is proposed to regulate solar energy capture and manage TES systems to maximize revenue. Simulation results show potential gains of up to 13.5% in annual revenue compared to a classic control strategy.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2023)
Article
Automation & Control Systems
Daniel Martins Lima, Bruno Martins Lima, Julio Elias Normey-Rico
Summary: This paper proposes a Modified Kalman Predictor (MKP) for linear multivariable square systems with multiple dead-time. The MKP uses a specific state-space representation of the process, making its implementation more straightforward compared to other methods. It affects disturbance rejection but not closed-loop stability in the nominal case, and can improve closed-loop robustness in the uncertain case.
JOURNAL OF PROCESS CONTROL
(2023)
Article
Green & Sustainable Science & Technology
Diogo Ortiz Machado, William D. Chicaiza, Juan M. Escano, Antonio J. Gallego, Gustavo A. de Andrade, Julio E. Normey-Rico, Carlos Bordons, Eduardo F. Camacho
Summary: The aim of this study is to create a digital twin of a commercial absorption chiller using Adaptive Neuro-fuzzy Inference System (ANFIS) for control and optimization purposes. The ANFIS models show good accuracy and precision, outperforming literature models in terms of Mean Absolute Percentage Error (MAPE). The resulting digital twin is suitable for Model Predictive Control applications and fast what-if analysis and optimization.
Article
Computer Science, Interdisciplinary Applications
Joao Bernardo Aranha Ribeiro, Jose Dolores Vergara Dietrich, Julio Elias Normey-Rico
Summary: We propose a simplified economic model predictive controller (EMPC) for supervisory control of an offshore petroleum production network. The controller mimics a nonlinear EMPC while solving quadratic programming and enhances feasibility using an estimator and slack variables. We provide tips for design improvement and compare our method with other controllers in various scenarios, showing its economic optimality and robustness.
COMPUTERS & CHEMICAL ENGINEERING
(2023)
Article
Automation & Control Systems
Marcelo M. Morato, Julio E. Normey-Rico, Olivier Sename
Summary: This paper evaluates the robustness qualities of Model Predictive Control (MPC) algorithms applied to Linear Parameter Varying (LPV) systems. The paper proposes a parameter-dependent Karush-Kuhn-Tucker (KKT) inequality to describe the existence and feasibility of the LPV MPC control inputs. It also models the uncertainties arising from the unavailability of the scheduling trajectory as a bounded interconnection in the form of a Linear Fractional Transformation (LFT). The paper uses dissipativity arguments to compute robust induced gains of the closed-loop system, considering MPC prediction uncertainties. A benchmark example is provided to illustrate the analysis procedure.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Marcelo M. Morato
Summary: In this paper, a new robust Model Predictive Control (MPC) algorithm for Linear Parameter Varying (LPV) systems in Input-Output (IO) form is introduced. The algorithm incorporates integral action and ensures output reference tracking for piece-wise constant signals. It is based on the online extrapolation of the LPV scheduling parameters, generated recursively using a simple Taylor expansion argument. The proposed method is demonstrated to have closed-loop asymptotic stability, recursive feasibility of online optimization, as well as robustness towards disturbances and scheduling parameter prediction uncertainties. Two nonlinear multi-input multi-output benchmarks are used to illustrate the effectiveness of the algorithm: a simulation example for comparison with state-of-the-art techniques, and a twin rotor system for further validation. The real-time capabilities of the method are highlighted as it only requires one Quadratic Program evaluation per discrete-time sample during implementation.
JOURNAL OF PROCESS CONTROL
(2023)
Proceedings Paper
Automation & Control Systems
M. M. Morato, J. J. Marquez, A. Zafra-Cabeza, C. Bordons, J. E. Normey-Rico
Summary: This paper formalizes a fault-tolerant control and mitigation strategy for the energy management of renewable microgrids. It introduces a Model Predictive Control (MPC) algorithm based on a Linear Parameter Varying (LPV) model, which utilizes fault diagnosis information to update the microgrid model and adapt process constraints according to fault level and location. Nonlinear simulation results demonstrate the effectiveness of the approach.
Proceedings Paper
Automation & Control Systems
Marcelo M. Morato, Julio E. Normey-Rico, Olivier Sename
Summary: This paper presents a novel Model Predictive Control (MPC) algorithm for Linear Parameter Varying (LPV) systems represented in the Input-Output (IO) form. The proposed MPC is derived using estimates for the future scheduling parameter trajectory, made viable through a recursive Taylor-based extrapolation law. The method also includes explicit integral action, which, coupled with quadratic terminal ingredients, enables offset-free reference tracking and asymptotic IO stability. A numeric benchmark example is used to illustrate the advantages of the proposed method, as well as its real-time capabilities.
Article
Engineering, Electrical & Electronic
Wandry R. Faria, Gregorio Munoz-Delgado, Javier Contreras, Benvindo R. Pereira Jr
Summary: This paper proposes a new bilevel mathematical model for competitive electricity markets, taking into account the participation of distribution systems operators. A new pricing method is introduced as an alternative to the inaccessible dual variables of the transmission system.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Chao Zhang, Liwei Zhang, Dong Wang, Kaiyuan Lu
Summary: The load disturbance rejection ability of electrical machine systems is crucial in many applications. Existing studies mainly focus on improving disturbance observers, but the speed response control during the transient also plays a significant role. This paper proposes a sliding mode disturbance observer-based load disturbance rejection control with an adaptive filter and a Smith predictor-based speed filter delay compensator to enhance the transient speed response.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Arif Hussain, Arif Mehdi, Chul-Hwan Kim
Summary: The proposed scheme in this research paper is a communication-less islanding detection system based on recurrent neural network (RNN) for hybrid distributed generator (DG) systems. The scheme demonstrates good performance in feature extraction, feature selection, and islanding detection, and it also performs effectively in noisy environments.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Zonghui Sun, Xizheng Guo, Shinan Wang, Xiaojie You
Summary: This paper presents a status pre-matching method (SPM) that eliminates the iterative calculations for resistance switch model, and simulates all operation modes of PECs through a more convenient approach. Furthermore, a FPGA implementation scheme is proposed to fully utilize the multiplier units of FPGA.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Rui Zhou, Shuheng Chen, Yang Han, Qunying Liu, Zhe Chen, Weihao Hu
Summary: In power system scheduling with variable renewable energy sources, considering both spatial and temporal correlations is a challenging task due to the complex intertwining of spatiotemporal characteristics and computational complexity caused by high dimensionality. This paper proposes a novel probabilistic spatiotemporal scenario generation (PSTSG) method that generates probabilistic scenarios accounting for spatial and temporal correlations simultaneously. The method incorporates Latin hypercube sampling, copula-importance sampling theory, and probability-based scenario reduction technique to efficiently capture the spatial and temporal correlation in the dynamic optimal power flow problem. Numerical simulations demonstrate the superiority of the proposed approach in terms of computational efficiency and accuracy compared to existing methods.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Juan Manuel Mauricio, J. Carlos Olives-Camps, Jose Maria Maza-Ortega, Antonio Gomez-Exposito
Summary: This paper proposes a simplified thermal model of VSC, which can produce accurate results at a low computational cost. The model consists of a simple first-order thermal dynamics system and two quadratic equations to model power losses. A methodology is also provided to derive the model parameters from manufacturer data.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Jae-Kyeong Kim, Kyeon Hur
Summary: This paper investigates the relationship between the accuracy of finite difference-based trajectory sensitivity (FDTS) analysis and the perturbation size in non-smooth systems. The study reveals that the approximation accuracy is significantly influenced by the perturbation size, and linear approximation is the most suitable method for practical applications.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Yuan Si, Amjad Anvari-Moghaddam
Summary: This paper investigates the impact of geomagnetic disturbances on small signal stability in power systems and proposes the installation of blocking devices to mitigate the negative effects. Quantitative evaluation reveals that intense geomagnetic disturbances significantly increase the risk of small signal instability. Optimal placement of blocking devices based on sensitivity scenarios results in a significant reduction in the risk index compared to constant and varying induced geoelectric fields scenarios.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Xuejian Zhang, Wenxin Kong, Nian Yu, Huang Chen, Tianyang Li, Enci Wang
Summary: The intensity estimation of geomagnetically induced currents (GICs) varies depending on the method used. The estimation using field magnetotelluric (MT) data provides the highest accuracy, followed by the estimation using 3D conductivity models and the estimation using a 1D conductivity model. The GICs in the North China 1000-kV power grid have reached a very high-risk level, with C3 and C4 having a significant impact on the geoelectric field and GICs.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Yue Pan, Shunjiang Lin, Weikun Liang, Xiangyong Feng, Xuan Sheng, Mingbo Liu
Summary: This paper introduces the concept and model of offshore-onshore regional integrated energy system, and proposes a stochastic optimal dispatch model and an improved state-space approximate dynamic programming algorithm to solve the model. The case study demonstrates the effectiveness and high efficiency of the proposed method in improving economic and environmental benefits.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Mohammad Eydi, Reza Ghazi, Majid Oloomi Buygi
Summary: Proportional current sharing, voltage restoration, and SOCs balancing in DC microgrid control algorithms are the leading challenges. This paper proposes a novel communication-less control method using a capacitor and a DC/DC converter to stabilize the system and restore the DC bus voltage. The method includes injecting an AC signal into the DC bus, setting the current of energy storage units based on frequency and SOC, and incorporating droop control for system stability. Stability analysis and simulation results validate the effectiveness of the proposed method.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Xiangjian Meng, Xinyu Shi, Weiqi Wang, Yumin Zhang, Feng Gao
Summary: With the increasing penetration of photovoltaic power generation, regional power forecasting becomes critical for stable and economical operation of power systems. This paper proposes a minute-level regional PV power forecasting scheme using selected reference PV plants. The challenges include the lack of complete historical power data and the heavy computation burden. The proposed method incorporates a novel reference PV plant selection method and a flexible approach to decrease the accumulated error of rolling forecasting.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Huabo Shi, Yuhong Wang, Xinwei Sun, Gang Chen, Lijie Ding, Pengyu Pan, Qi Zeng
Summary: This article investigates the dynamic stability characteristics of the full size converter variable speed pumped storage unit and proposes improvements for the control strategy. The research is important for ensuring the safe and efficient operation of the unit.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Firmansyah Nur Budiman, Makbul A. M. Ramli, Houssem R. E. H. Bouchekara, Ahmad H. Milyani
Summary: This paper proposes an optimal harmonic power flow framework for the daily scheduling of a grid-connected microgrid, which addresses power quality issues and ensures effective control through demand side management.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Cong Zeng, Ziyu Chen, Jizhong Zhu, Fellew Ieee
Summary: This paper introduces a distributed solution method for the multi-objective OPF problem, using a coevolutionary multi-objective evolutionary algorithm and the idea of decomposition. The problem is alleviated by decomposing decision variables and objective functions, and a new distributed fitness evaluation method is proposed. The experimental results demonstrate the effectiveness of the method and its excellence in large-scale systems.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
