This study aims to propose an adaptive fuzzy sliding mode control (AFSMC) method to address the critical issue of enhancing the power extraction of a large-scale permanent magnet synchronous generator (PMSG)-based wind energy conversion system (WECS) in the presence of unknown dynamics, uncertain perturbations and actuator faults. To do this, a modified dynamical model of the PMSG-based WECS is established to capture unsteady dynamics and uncertainties. Next, the novel robust sliding mode reaching law-based speed control is designed to achieve fast convergence and enhance output power by tracking the optimum rotation speed under various wind scenarios. At the same time, the adaptive fuzzy technique is designed to estimate and compensate for unstable dynamics and uncertainties in large-scale WECS, enabling efficient power extraction. Then, the proposed AFSMC stability conditions are derived using suitable Lyapunov functions. Finally, the superiority of the proposed AFSMC scheme is confirmed via simulation using 1.5 MW PMSG-based WECS under diverse wind patterns. Additionally, the applicability of the proposed scheme is validated through experimentation on a prototype of a 5 kW PMSG-based WECS considering actuator fault, unsteady dynamics, and diverse wind speed conditions.
{"title":"Performance enhancement of PMSG-based WECS using robust adaptive fuzzy sliding mode control","authors":"Anto Anbarasu Yesudhas, Kumarasamy Palanimuthu, Seong Ryong Lee, Jae Hoon Jeong, Young Hoon Joo","doi":"10.1016/j.conengprac.2024.106211","DOIUrl":"10.1016/j.conengprac.2024.106211","url":null,"abstract":"<div><div>This study aims to propose an adaptive fuzzy sliding mode control (AFSMC) method to address the critical issue of enhancing the power extraction of a large-scale permanent magnet synchronous generator (PMSG)-based wind energy conversion system (WECS) in the presence of unknown dynamics, uncertain perturbations and actuator faults. To do this, a modified dynamical model of the PMSG-based WECS is established to capture unsteady dynamics and uncertainties. Next, the novel robust sliding mode reaching law-based speed control is designed to achieve fast convergence and enhance output power by tracking the optimum rotation speed under various wind scenarios. At the same time, the adaptive fuzzy technique is designed to estimate and compensate for unstable dynamics and uncertainties in large-scale WECS, enabling efficient power extraction. Then, the proposed AFSMC stability conditions are derived using suitable Lyapunov functions. Finally, the superiority of the proposed AFSMC scheme is confirmed via simulation using 1.5 MW PMSG-based WECS under diverse wind patterns. Additionally, the applicability of the proposed scheme is validated through experimentation on a prototype of a 5 kW PMSG-based WECS considering actuator fault, unsteady dynamics, and diverse wind speed conditions.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"156 ","pages":"Article 106211"},"PeriodicalIF":5.4,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-14DOI: 10.1016/j.conengprac.2024.106202
Yueqiang Li , Zhao Zhang , Lingzhong Kong , Qian Yang , Jing Xu , Zhuliang Chen
This paper addresses the complex constraints and multi-objective requirements faced in the operation process of the Cascade Canal System (CCS), and proposes the Multi-Objective Model Predictive Control (CCS-MOMPC) method that can simultaneously consider the gate control constraints and gate regulation frequency. The proposed method modifies the original predictive control objective function by incorporating a gate regulation penalty term, and directly constrains both the gate deadband and the gate regulation frequency. Furthermore, the multi-objective function is converted into a single-objective function using the weighted method, which is then solved employing the Particle Swarm Optimization (PSO) algorithm. The proposed method is applied in the last eight canal pools of the Middle Route Project of South-to-North Water Diversion (MRP-SNWD). The results show that under the experimental conditions, compared with the traditional method, the proposed method can reduce the maximum water level deviation at control point from 0.15 m to 0.10 m, and decrease the total gate control frequency by 37.1%. In the case of unknown secondary disturbance, the proposed method can reduce the final action time of the gate by 77.5%. The results of this paper show that the improved control method can significantly improve the water level control accuracy and reduce the frequency of gate regulation.
{"title":"Multi-objective Predictive Control for cascade canal system considering constraints and objectives related to gate regulation","authors":"Yueqiang Li , Zhao Zhang , Lingzhong Kong , Qian Yang , Jing Xu , Zhuliang Chen","doi":"10.1016/j.conengprac.2024.106202","DOIUrl":"10.1016/j.conengprac.2024.106202","url":null,"abstract":"<div><div>This paper addresses the complex constraints and multi-objective requirements faced in the operation process of the Cascade Canal System (CCS), and proposes the Multi-Objective Model Predictive Control (CCS-MOMPC) method that can simultaneously consider the gate control constraints and gate regulation frequency. The proposed method modifies the original predictive control objective function by incorporating a gate regulation penalty term, and directly constrains both the gate deadband and the gate regulation frequency. Furthermore, the multi-objective function is converted into a single-objective function using the weighted method, which is then solved employing the Particle Swarm Optimization (PSO) algorithm. The proposed method is applied in the last eight canal pools of the Middle Route Project of South-to-North Water Diversion (MRP-SNWD). The results show that under the experimental conditions, compared with the traditional method, the proposed method can reduce the maximum water level deviation at control point from 0.15 m to 0.10 m, and decrease the total gate control frequency by 37.1%. In the case of unknown secondary disturbance, the proposed method can reduce the final action time of the gate by 77.5%. The results of this paper show that the improved control method can significantly improve the water level control accuracy and reduce the frequency of gate regulation.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"156 ","pages":"Article 106202"},"PeriodicalIF":5.4,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-13DOI: 10.1016/j.conengprac.2024.106199
Siqi Wang , Yan Liu , Fuli Wang , Zhe Ma
Effective fault diagnosis techniques are important to ensure the normal operation of hydrometallurgical process. Generalized zero-shot fault diagnosis (GZSFD) technology can effectively diagnosis both seen and unseen faults in actual production, so as to improve production efficiency and reduce losses. However, traditional GZSFD methods have the domain shift problem (DSP) and over-rely on deep knowledge to establish the relationship between semantics/attributes and faults. This deep knowledge is difficult to acquire without sufficient understanding of the production process. In this study, a GZSFD method based on fault similarity (GZSFDFS) for hydrometallurgical process is proposed to overcome the limitations of traditional GZSFD. The core of GZSFDFS method is to build a fault similarity matrix (FSM) between seen and unseen faults using the superficial knowledge of whether state and operational variables will change as a fault occurs. Firstly, in order to extract representative features from the original data, a proper supervised learning method is used to establish feature extraction model, and the extracted features are used to establish a recognition model. Next, the prediction results for the test samples with respect to each seen fault can be obtained by utilizing the fault recognition model, and the appropriate threshold is selected to distinguish the unseen faults from the seen faults. For the unseen faults, the predicted results for the test samples with respect to each unseen fault are constructed based on the FSM. Then, reasonable discriminant rules are designed to determine the fault classes of the test samples. Finally, based on numerical examples and hydrometallurgical processes, the effectiveness and superiority of the proposed method are verified.
{"title":"Generalized zero-shot fault diagnosis based on fault similarity for hydrometallurgical process","authors":"Siqi Wang , Yan Liu , Fuli Wang , Zhe Ma","doi":"10.1016/j.conengprac.2024.106199","DOIUrl":"10.1016/j.conengprac.2024.106199","url":null,"abstract":"<div><div>Effective fault diagnosis techniques are important to ensure the normal operation of hydrometallurgical process. Generalized zero-shot fault diagnosis (GZSFD) technology can effectively diagnosis both seen and unseen faults in actual production, so as to improve production efficiency and reduce losses. However, traditional GZSFD methods have the domain shift problem (DSP) and over-rely on deep knowledge to establish the relationship between semantics/attributes and faults. This deep knowledge is difficult to acquire without sufficient understanding of the production process. In this study, a GZSFD method based on fault similarity (GZSFDFS) for hydrometallurgical process is proposed to overcome the limitations of traditional GZSFD. The core of GZSFDFS method is to build a fault similarity matrix (FSM) between seen and unseen faults using the superficial knowledge of whether state and operational variables will change as a fault occurs. Firstly, in order to extract representative features from the original data, a proper supervised learning method is used to establish feature extraction model, and the extracted features are used to establish a recognition model. Next, the prediction results for the test samples with respect to each seen fault can be obtained by utilizing the fault recognition model, and the appropriate threshold is selected to distinguish the unseen faults from the seen faults. For the unseen faults, the predicted results for the test samples with respect to each unseen fault are constructed based on the FSM. Then, reasonable discriminant rules are designed to determine the fault classes of the test samples. Finally, based on numerical examples and hydrometallurgical processes, the effectiveness and superiority of the proposed method are verified.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"156 ","pages":"Article 106199"},"PeriodicalIF":5.4,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The gradual deactivation of SCR catalysts at high temperatures and under steam conditions leads to a reduction in their NOx reduction efficiency. In order to mitigate the adverse effects of hydrothermal aging on the SCR catalyst and ensure compliance with Euro VII diesel exhaust standards. Firstly, the concept of SCR hydrothermal aging and its corresponding model are constructed. The model is then transformed using the variable substitution method and Method of Lines, optimized by the Levenberg–Marquardt method, and solved with the backward differentiation formula method. Secondly, this paper designs the ATS-EKF hydrothermal aging factor observer, informed by the model’s solution characteristics, NH concentration, NOx concentration, and ammonia coverage within the SCR catalyst. Based on the observer’s monitoring, open-loop feedforward and closed-loop feedback control methods are formulated. The closed-loop feedback utilizes the effective set algorithm to address the SCR hydrothermal aging system’s nonlinearity, uncertainty, and time-varying nature. Finally, the SCR model is validated through testing cycles at the WHTC. Validation results reveal a high calculation accuracy. Observations of the ATS-EKF hydrothermal aging factor, in conjunction with control of upstream NH concentration, indicate mean downstream NH concentrations of 8.6 ppm, 8.18 ppm, and 7.87 ppm and NOx concentrations of 11.03 ppm, 10.59 ppm, and 10.14 ppm for hydrothermal aging factors of 1, 0.8, and 0.6, respectively, all meeting the Euro VII emission standards. The methodology in this paper accurately responds to the hydrothermal ageing of the SCR catalyst and controls the NH and NOx concentrations using the ATS-EKF monitoring and control strategy to ensure Euro VII compliant emissions with greater accuracy and stability than existing systems.
{"title":"Hydrothermal ageing control of diesel engine urea selective catalytic reduction (SCR) based on adaptive time-step extended Kalman filter (ATS-EKF) and combination of open-loop feedforward and closed-loop feedback","authors":"Wenlong Liu, Ying Gao, Yuelin You, Changwen Jiang, Taoyi Hu, Bocong Xia","doi":"10.1016/j.conengprac.2024.106201","DOIUrl":"10.1016/j.conengprac.2024.106201","url":null,"abstract":"<div><div>The gradual deactivation of SCR catalysts at high temperatures and under steam conditions leads to a reduction in their NOx reduction efficiency. In order to mitigate the adverse effects of hydrothermal aging on the SCR catalyst and ensure compliance with Euro VII diesel exhaust standards. Firstly, the concept of SCR hydrothermal aging and its corresponding model are constructed. The model is then transformed using the variable substitution method and Method of Lines, optimized by the Levenberg–Marquardt method, and solved with the backward differentiation formula method. Secondly, this paper designs the ATS-EKF hydrothermal aging factor observer, informed by the model’s solution characteristics, NH<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span> concentration, NOx concentration, and ammonia coverage within the SCR catalyst. Based on the observer’s monitoring, open-loop feedforward and closed-loop feedback control methods are formulated. The closed-loop feedback utilizes the effective set algorithm to address the SCR hydrothermal aging system’s nonlinearity, uncertainty, and time-varying nature. Finally, the SCR model is validated through testing cycles at the WHTC. Validation results reveal a high calculation accuracy. Observations of the ATS-EKF hydrothermal aging factor, in conjunction with control of upstream NH<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span> concentration, indicate mean downstream NH<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span> concentrations of 8.6 ppm, 8.18 ppm, and 7.87 ppm and NOx concentrations of 11.03 ppm, 10.59 ppm, and 10.14 ppm for hydrothermal aging factors of 1, 0.8, and 0.6, respectively, all meeting the Euro VII emission standards. The methodology in this paper accurately responds to the hydrothermal ageing of the SCR catalyst and controls the NH<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span> and NOx concentrations using the ATS-EKF monitoring and control strategy to ensure Euro VII compliant emissions with greater accuracy and stability than existing systems.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"156 ","pages":"Article 106201"},"PeriodicalIF":5.4,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study is concerned with the development of reduced order machine learning (ML) and non-ML model representations of experimental and simulated bioprocesses and their implementation in model predictive control (MPC) strategies to quantify performance accuracy and computational efficiency compared with the original models. Results showed that ML models such as Long Short-Term Memory (LSTM) networks and Artificial Neural Networks (ANNs) outperformed other reduced order models such as Kriging, Multiple Linear Regression (MLR) and Random Forest (RF) in terms of performance metrics such as R2 and RMSE for both experimental and simulated data. Experimental data were obtained from a fed-batch and perfusion-based bioprocess and an LSTM model was developed and implemented in an MPC open-loop optimal control strategy to determine optimal input trajectories to maximize key performance metrics such as product titer. For the 2 by 3 ODE simulation, results showed that an autoregressive ANN was the most accurate in terms of replicating the plant model dynamics under MPC conditions followed by the LSTM and transfer function (TF) representations, with the feedforward ANN not being able to fully capture the salient dynamics. For the 4 by 5 ODE simulation, the TF representation outperformed the feedforward ANN model which in turn was more accurate than the LSTM model. In terms of computational time, the plant model simulation time for an MPC solution is intractable for larger input-output sizes compared with the ML models. Overall, it can be seen the ML models such as ANNs and LSTMs, provide the best balance between accuracy and computational efficiency as they can capture the inherent nonlinearities of the plant model but also are not computationally intensive compared to the full plant model which are often represented by ODE and/or PDE-based differential equations. ML models such as those developed in this study demonstrate practical methods of implementing advanced process control in highly nonlinear chemical/biological processes as part of the smart manufacturing/Industry 4.0 paradigm.
{"title":"Machine learning model-based design and model predictive control of a bioreactor for the improved production of mammalian cell-based bio-therapeutics","authors":"Ashley Dan , Bochi Liu , Urjit Patil , Bhavani Nandhini Mummidi Manuraj , Ronit Gandhi , Justin Buchel , Shishir P.S. Chundawat , Weihong Guo , Rohit Ramachandran","doi":"10.1016/j.conengprac.2024.106198","DOIUrl":"10.1016/j.conengprac.2024.106198","url":null,"abstract":"<div><div>This study is concerned with the development of reduced order machine learning (ML) and non-ML model representations of experimental and simulated bioprocesses and their implementation in model predictive control (MPC) strategies to quantify performance accuracy and computational efficiency compared with the original models. Results showed that ML models such as Long Short-Term Memory (LSTM) networks and Artificial Neural Networks (ANNs) outperformed other reduced order models such as Kriging, Multiple Linear Regression (MLR) and Random Forest (RF) in terms of performance metrics such as R<sup>2</sup> and RMSE for both experimental and simulated data. Experimental data were obtained from a fed-batch and perfusion-based bioprocess and an LSTM model was developed and implemented in an MPC open-loop optimal control strategy to determine optimal input trajectories to maximize key performance metrics such as product titer. For the 2 by 3 ODE simulation, results showed that an autoregressive ANN was the most accurate in terms of replicating the plant model dynamics under MPC conditions followed by the LSTM and transfer function (TF) representations, with the feedforward ANN not being able to fully capture the salient dynamics. For the 4 by 5 ODE simulation, the TF representation outperformed the feedforward ANN model which in turn was more accurate than the LSTM model. In terms of computational time, the plant model simulation time for an MPC solution is intractable for larger input-output sizes compared with the ML models. Overall, it can be seen the ML models such as ANNs and LSTMs, provide the best balance between accuracy and computational efficiency as they can capture the inherent nonlinearities of the plant model but also are not computationally intensive compared to the full plant model which are often represented by ODE and/or PDE-based differential equations. ML models such as those developed in this study demonstrate practical methods of implementing advanced process control in highly nonlinear chemical/biological processes as part of the smart manufacturing/Industry 4.0 paradigm.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"156 ","pages":"Article 106198"},"PeriodicalIF":5.4,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-06DOI: 10.1016/j.conengprac.2024.106197
Alaref Elhaj, Mohamad Alzayed, Hicham Chaoui
This manuscript proposes an efficient, straightforward, direct voltage maximum torque per ampere (MTPA) control scheme for an interior permanent magnet synchronous motor (IPMSM) propelling an electric vehicle (EV). The main feature of the traction control scheme is that the MTPA is attained by directly varying the amplitude and angle of the voltage vector, eliminating the need for current control loops and associated regulators. Instead, a single-speed controller is adopted. Furthermore, an analytical formulation based on the motor voltage model is developed to extract the desired voltage’s magnitude and angle to run the motor within the MTPA operating points, disregarding numerical solutions, control law approximation, long-winded iterative calculations, or approximate representation of the IPMSM. Such a methodology significantly reduces control scheme complexity, enhances computational efficiency, and mitigates the delays associated with cascaded-based control systems. Additionally, it facilitates straightforward real-time implementation. The performance of the designed algorithm is experimentally validated using commonly adopted driving cycles, namely the Federal Test Procedure (US06) drive cycle and the New European Driving Cycle (NEDC). The validity test is performed using a 5 HP IPMSM. Based on the driving cycles employed, an intensive comparative evaluation against MTPA field-oriented control (FOC) is established. A quantitative assessment is conducted using the MTPA FOC as a benchmark to investigate energy consumption. This assessment reveals that the designed strategy achieved energy savings of 1.318% and 2.26% under US06 and NEDC, respectively, compared to the MTPA FOC. The proposed method’s speed-tracking accuracy and computational efficiency are also investigated and compared to the FOC and existing direct voltage approaches, demonstrating an average improvement of 14% in speed-tracking accuracy and 6.8% in computational efficiency.
{"title":"Direct voltage MTPA control of interior permanent magnet synchronous motor driven electric vehicles","authors":"Alaref Elhaj, Mohamad Alzayed, Hicham Chaoui","doi":"10.1016/j.conengprac.2024.106197","DOIUrl":"10.1016/j.conengprac.2024.106197","url":null,"abstract":"<div><div>This manuscript proposes an efficient, straightforward, direct voltage maximum torque per ampere (MTPA) control scheme for an interior permanent magnet synchronous motor (IPMSM) propelling an electric vehicle (EV). The main feature of the traction control scheme is that the MTPA is attained by directly varying the amplitude and angle of the voltage vector, eliminating the need for current control loops and associated regulators. Instead, a single-speed controller is adopted. Furthermore, an analytical formulation based on the motor voltage model is developed to extract the desired voltage’s magnitude and angle to run the motor within the MTPA operating points, disregarding numerical solutions, control law approximation, long-winded iterative calculations, or approximate representation of the IPMSM. Such a methodology significantly reduces control scheme complexity, enhances computational efficiency, and mitigates the delays associated with cascaded-based control systems. Additionally, it facilitates straightforward real-time implementation. The performance of the designed algorithm is experimentally validated using commonly adopted driving cycles, namely the Federal Test Procedure (US06) drive cycle and the New European Driving Cycle (NEDC). The validity test is performed using a 5 HP IPMSM. Based on the driving cycles employed, an intensive comparative evaluation against MTPA field-oriented control (FOC) is established. A quantitative assessment is conducted using the MTPA FOC as a benchmark to investigate energy consumption. This assessment reveals that the designed strategy achieved energy savings of 1.318% and 2.26% under US06 and NEDC, respectively, compared to the MTPA FOC. The proposed method’s speed-tracking accuracy and computational efficiency are also investigated and compared to the FOC and existing direct voltage approaches, demonstrating an average improvement of 14% in speed-tracking accuracy and 6.8% in computational efficiency.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"156 ","pages":"Article 106197"},"PeriodicalIF":5.4,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-06DOI: 10.1016/j.conengprac.2024.106192
Carlos Conejo , Vicenç Puig , Bernardo Morcego , Francisco Navas , Vicente Milanés
In this paper, a solution is presented to address the sensor fault detection and isolation (FDI) problem in state estimation for autonomous vehicles (AVs). The primary impetus for autonomous driving lies in its potential to ensure vehicle safety, a goal that requires an accurate determination of location, heading, and speed. Although sensors can directly obtain these measurements, they are often affected by noise and disturbances with unknown but bounded (UBB) distributions. To mitigate these effects, state estimation techniques are commonly employed, leveraging sensor fusion. This work aims to design an FDI methodology that continuously evaluates the accuracy of the state estimation algorithm in an AV. In order to achieve this goal, various observation techniques for robust FDI are compared, including a novel approach of EKF formulated within the LPV framework, named LPV-EKF. A zonotopic LPV-EKF observer is implemented to perform FDI on both state estimation inputs and outputs, considering an UBB noise distribution. The proposed methodology for the identification of anomalies is optimised to minimise the detection time in real world scenarios. The experimental results for FDI, collected from an autonomous Renault Zoe (SAE Level 3), are analysed and discussed.
{"title":"Enhancing safety in autonomous vehicles using zonotopic LPV-EKF for fault detection and isolation in state estimation","authors":"Carlos Conejo , Vicenç Puig , Bernardo Morcego , Francisco Navas , Vicente Milanés","doi":"10.1016/j.conengprac.2024.106192","DOIUrl":"10.1016/j.conengprac.2024.106192","url":null,"abstract":"<div><div>In this paper, a solution is presented to address the sensor fault detection and isolation (FDI) problem in state estimation for autonomous vehicles (AVs). The primary impetus for autonomous driving lies in its potential to ensure vehicle safety, a goal that requires an accurate determination of location, heading, and speed. Although sensors can directly obtain these measurements, they are often affected by noise and disturbances with unknown but bounded (UBB) distributions. To mitigate these effects, state estimation techniques are commonly employed, leveraging sensor fusion. This work aims to design an FDI methodology that continuously evaluates the accuracy of the state estimation algorithm in an AV. In order to achieve this goal, various observation techniques for robust FDI are compared, including a novel approach of EKF formulated within the LPV framework, named LPV-EKF. A zonotopic LPV-EKF observer is implemented to perform FDI on both state estimation inputs and outputs, considering an UBB noise distribution. The proposed methodology for the identification of anomalies is optimised to minimise the detection time in real world scenarios. The experimental results for FDI, collected from an autonomous Renault Zoe (SAE Level 3), are analysed and discussed.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"156 ","pages":"Article 106192"},"PeriodicalIF":5.4,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143157828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-06DOI: 10.1016/j.conengprac.2024.106180
Xiaoyun Sun, Qiang Shen, Shufan Wu
In this paper, the high-precision test mass suspension control of the space inertia sensor is investigated by proposing an enhanced output tracking self-triggered model reference adaptive control (MRAC) scheme. Based on the proposed control scheme, the test masses are adaptively suspended by using reduced-ordered output. To suppress the disturbance due to the test mass suspension, we introduce a -modification approach to the adaptive controller with a bounded switching gain. Furthermore, to reduce the actuation consumption, a self-triggering mechanism (STM) is applied to determine the triggering instants using the current responses. An extended Lyapunov analysis based on Filippov solutions proves the boundedness of all closed-loop signals for the suspension system with switching adaptive gains. Numerical simulations are performed to verify the reduction of the actuation cost and the effectiveness of the proposed control scheme, under external disturbances, parameter uncertainties, input saturation, and hysteresis.
{"title":"Self-triggered output tracking model reference adaptive control for test mass suspension","authors":"Xiaoyun Sun, Qiang Shen, Shufan Wu","doi":"10.1016/j.conengprac.2024.106180","DOIUrl":"10.1016/j.conengprac.2024.106180","url":null,"abstract":"<div><div>In this paper, the high-precision test mass suspension control of the space inertia sensor is investigated by proposing an enhanced output tracking self-triggered model reference adaptive control (MRAC) scheme. Based on the proposed control scheme, the test masses are adaptively suspended by using reduced-ordered output. To suppress the disturbance due to the test mass suspension, we introduce a <span><math><mi>σ</mi></math></span>-modification approach to the adaptive controller with a bounded switching gain. Furthermore, to reduce the actuation consumption, a self-triggering mechanism (STM) is applied to determine the triggering instants using the current responses. An extended Lyapunov analysis based on Filippov solutions proves the boundedness of all closed-loop signals for the suspension system with switching adaptive gains. Numerical simulations are performed to verify the reduction of the actuation cost and the effectiveness of the proposed control scheme, under external disturbances, parameter uncertainties, input saturation, and hysteresis.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"156 ","pages":"Article 106180"},"PeriodicalIF":5.4,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-05DOI: 10.1016/j.conengprac.2024.106191
Malena Caparroz , José Luis Guzmán , Juan Diego Gil , Manuel Berenguel , Francisco Gabriel Acién
This study presents the design, analysis and experimental evaluation of a hybrid Model Reference Adaptive Control (MRAC) structure for pH regulation through CO injection in an open raceway reactor, addressing the nonlinear and time-varying dynamics of outdoor microalgae production systems. Conducted at the IFAPA center-University of Almería, the research compares the MRAC’s performance with traditional control solutions in these processes. Results indicate that the hybrid MRAC controller significantly outperforms conventional controllers, as demonstrated by performance indices. The MRAC’s ability to adapt to varying conditions was particularly noteworthy, enabling real-time adjustments and effective pH regulation. During continuous operation in real facilities for twelve days in two different months, the MRAC maintained the pH within the desired range, highlighting its robustness and suitability for industrial applications in microalgae photobioreactors. These findings contribute to the development of efficient and adaptive solutions for complex industrial processes, promoting sustainable and cost-effective microalgae production.
{"title":"A hybrid MRAC-PI approach to regulate pH in raceway reactors for microalgae production","authors":"Malena Caparroz , José Luis Guzmán , Juan Diego Gil , Manuel Berenguel , Francisco Gabriel Acién","doi":"10.1016/j.conengprac.2024.106191","DOIUrl":"10.1016/j.conengprac.2024.106191","url":null,"abstract":"<div><div>This study presents the design, analysis and experimental evaluation of a hybrid Model Reference Adaptive Control (MRAC) structure for pH regulation through CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> injection in an open raceway reactor, addressing the nonlinear and time-varying dynamics of outdoor microalgae production systems. Conducted at the IFAPA center-University of Almería, the research compares the MRAC’s performance with traditional control solutions in these processes. Results indicate that the hybrid MRAC controller significantly outperforms conventional controllers, as demonstrated by performance indices. The MRAC’s ability to adapt to varying conditions was particularly noteworthy, enabling real-time adjustments and effective pH regulation. During continuous operation in real facilities for twelve days in two different months, the MRAC maintained the pH within the desired range, highlighting its robustness and suitability for industrial applications in microalgae photobioreactors. These findings contribute to the development of efficient and adaptive solutions for complex industrial processes, promoting sustainable and cost-effective microalgae production.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"156 ","pages":"Article 106191"},"PeriodicalIF":5.4,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper develops a model for the joint assignment of traffic and energy demand over a network. The vehicles circulating in the network are partly conventional and partly electric. Some of the network links are equipped with electric charging stations. A stochastic model to represent the drivers’ choices is adopted, and Stochastic User Equilibrium (SUE) conditions are used to obtain the steady-state traffic and energy demand assignment. An unconstrained optimization problem is introduced, and it is proven that its solution is equivalent to imposing the set of SUE conditions without any limitation as regards the model representing the user choice function. A real case study related to a tourist area in the Liguria region (from Genoa airport to Portofino) is treated in detail and a further case study is considered to demonstrate the wide applicability of the approach.
{"title":"An unconstrained optimization approach to the stochastic traffic assignment with electric vehicles","authors":"Michele Aicardi, Giulio Ferro, Riccardo Minciardi, Michela Robba","doi":"10.1016/j.conengprac.2024.106177","DOIUrl":"10.1016/j.conengprac.2024.106177","url":null,"abstract":"<div><div>This paper develops a model for the joint assignment of traffic and energy demand over a network. The vehicles circulating in the network are partly conventional and partly electric. Some of the network links are equipped with electric charging stations. A stochastic model to represent the drivers’ choices is adopted, and Stochastic User Equilibrium (SUE) conditions are used to obtain the steady-state traffic and energy demand assignment. An unconstrained optimization problem is introduced, and it is proven that its solution is equivalent to imposing the set of SUE conditions without any limitation as regards the model representing the user choice function. A real case study related to a tourist area in the Liguria region (from Genoa airport to Portofino) is treated in detail and a further case study is considered to demonstrate the wide applicability of the approach.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"156 ","pages":"Article 106177"},"PeriodicalIF":5.4,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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