Pub Date : 2024-11-08DOI: 10.1016/j.conengprac.2024.106155
Aigerim Nurbayeva, Matteo Rubagotti
This paper proposes a methodology for safely planning the motion of a robot manipulator sharing its workspace with a human operator. The motion of the robot is continuously re-planned via nonlinear model predictive control (NMPC), imposing the so-called speed and separation monitoring (SSM) condition to guarantee human safety. Contrary to previous works in the field, the NMPC algorithm is designed with an ellipsoidal terminal constraint, to enlarge the domain of attraction compared to the case in which a point terminal constraint was imposed. This is a very important aspect in real-world applications, allowing the robot to plan its motion from initial configurations that are relatively far from the goal point. Theoretical results are proved on recursive feasibility and closed-loop stability for both cases of NMPC with point and set terminal constraints, under the simplifying assumption of a static human. The effectiveness of the proposed approach is verified via numerical evaluation of the domain of attraction and with experiments on a UR5 manipulator.
{"title":"Nonlinear model predictive control with set terminal constraint for safe robot motion planning via speed and separation monitoring","authors":"Aigerim Nurbayeva, Matteo Rubagotti","doi":"10.1016/j.conengprac.2024.106155","DOIUrl":"10.1016/j.conengprac.2024.106155","url":null,"abstract":"<div><div>This paper proposes a methodology for safely planning the motion of a robot manipulator sharing its workspace with a human operator. The motion of the robot is continuously re-planned via nonlinear model predictive control (NMPC), imposing the so-called <em>speed and separation monitoring</em> (SSM) condition to guarantee human safety. Contrary to previous works in the field, the NMPC algorithm is designed with an ellipsoidal terminal constraint, to enlarge the domain of attraction compared to the case in which a point terminal constraint was imposed. This is a very important aspect in real-world applications, allowing the robot to plan its motion from initial configurations that are relatively far from the goal point. Theoretical results are proved on recursive feasibility and closed-loop stability for both cases of NMPC with point and set terminal constraints, under the simplifying assumption of a static human. The effectiveness of the proposed approach is verified via numerical evaluation of the domain of attraction and with experiments on a UR5 manipulator.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106155"},"PeriodicalIF":5.4,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656377","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 : 2024-11-06DOI: 10.1016/j.conengprac.2024.106152
Leontine Aarnoudse , Peter den Toom , Tom Oomen
Iterative feedback tuning (IFT) enables the tuning of feedback controllers using only measured data to obtain the gradient of a cost criterion. The aim of this paper is to reduce the required number of experiments for MIMO IFT. It is shown that, through a randomization technique, an unbiased gradient estimate can be obtained from a single dedicated experiment, regardless of the size of the MIMO system. The gradient estimate is used in a stochastic gradient descent algorithm. The approach is experimentally validated on a mechatronic system, showing a significantly reduced number of experiments compared to standard IFT.
迭代反馈调谐（IFT）可以仅使用测量数据来获得成本准则的梯度，从而对反馈控制器进行调谐。本文旨在减少 MIMO IFT 所需的实验次数。研究表明，通过随机化技术，无论多输入多输出系统的规模如何，都能从一次专门实验中获得无偏梯度估计值。梯度估计值用于随机梯度下降算法。该方法在机电一体化系统上进行了实验验证，结果表明，与标准 IFT 相比，实验次数大大减少。
{"title":"Randomized iterative feedback tuning for fast MIMO feedback design of a mechatronic system","authors":"Leontine Aarnoudse , Peter den Toom , Tom Oomen","doi":"10.1016/j.conengprac.2024.106152","DOIUrl":"10.1016/j.conengprac.2024.106152","url":null,"abstract":"<div><div>Iterative feedback tuning (IFT) enables the tuning of feedback controllers using only measured data to obtain the gradient of a cost criterion. The aim of this paper is to reduce the required number of experiments for MIMO IFT. It is shown that, through a randomization technique, an unbiased gradient estimate can be obtained from a single dedicated experiment, regardless of the size of the MIMO system. The gradient estimate is used in a stochastic gradient descent algorithm. The approach is experimentally validated on a mechatronic system, showing a significantly reduced number of experiments compared to standard IFT.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106152"},"PeriodicalIF":5.4,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593553","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 : 2024-11-05DOI: 10.1016/j.conengprac.2024.106142
Sourav Sinha , Mazen Farhood , Daniel J. Stilwell
This paper addresses the design and analysis of path-following controllers for an autonomous underwater vehicle (AUV) using a robustness analysis framework based on integral quadratic constraints (IQCs). The AUV is modeled as a linear fractional transformation (LFT) on uncertainties and is affected by exogenous inputs such as measurement noise and ocean currents. The proposed approach leverages a learning-based method to approximate the nonlinear hydrodynamic model with a linear parameter-varying one. Additionally, modeling uncertainties are incorporated into the other subsystem models of the AUV to capture the discrepancies between the outputs of the postulated mathematical abstractions and the experimental data. The resulting uncertain LFT system adequately captures the AUV behavior within a desired envelope. Ocean current disturbances are treated as uncertainties within the LFT system and properly characterized to reduce conservatism. The robust performance level, obtained from IQC analysis, serves as a qualitative measure of a controller’s performance, and is utilized in guiding the controller design process. The proposed approach is employed to design and controllers for the AUV. A comprehensive IQC-based analysis is subsequently conducted to demonstrate the robustness of the designed controllers to modeling uncertainties and disturbances. To validate the analysis results, extensive nonlinear simulations and underwater experiments are performed. The outcomes showcase the efficacy and reliability of the proposed approach in achieving robust control for the AUV.
{"title":"Control design and analysis for autonomous underwater vehicles using integral quadratic constraints","authors":"Sourav Sinha , Mazen Farhood , Daniel J. Stilwell","doi":"10.1016/j.conengprac.2024.106142","DOIUrl":"10.1016/j.conengprac.2024.106142","url":null,"abstract":"<div><div>This paper addresses the design and analysis of path-following controllers for an autonomous underwater vehicle (AUV) using a robustness analysis framework based on integral quadratic constraints (IQCs). The AUV is modeled as a linear fractional transformation (LFT) on uncertainties and is affected by exogenous inputs such as measurement noise and ocean currents. The proposed approach leverages a learning-based method to approximate the nonlinear hydrodynamic model with a linear parameter-varying one. Additionally, modeling uncertainties are incorporated into the other subsystem models of the AUV to capture the discrepancies between the outputs of the postulated mathematical abstractions and the experimental data. The resulting uncertain LFT system adequately captures the AUV behavior within a desired envelope. Ocean current disturbances are treated as uncertainties within the LFT system and properly characterized to reduce conservatism. The robust performance level, obtained from IQC analysis, serves as a qualitative measure of a controller’s performance, and is utilized in guiding the controller design process. The proposed approach is employed to design <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> and <span><math><msub><mrow><mi>H</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> controllers for the AUV. A comprehensive IQC-based analysis is subsequently conducted to demonstrate the robustness of the designed controllers to modeling uncertainties and disturbances. To validate the analysis results, extensive nonlinear simulations and underwater experiments are performed. The outcomes showcase the efficacy and reliability of the proposed approach in achieving robust control for the AUV.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106142"},"PeriodicalIF":5.4,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586645","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 : 2024-11-05DOI: 10.1016/j.conengprac.2024.106154
Weiguang Yu , Daokui Qu , Fang Xu , Lei Zhang , Fengshan Zou , Zhenjun Du
To meet the demands of high-speed and high-accuracy applications of robotic manipulators, this paper proposes a time-optimal multi-point trajectory planning method with continuous jerk and constant average acceleration. A piecewise sine jerk model is developed for jerk continuity throughout the entire motion profile. An equivalent transformation of this complex model into the simple trapezoidal velocity model is proposed, effectively reducing the computational complexity and ensuring the reliability of real-time planning. The introduction of a parameter, named the trajectory smoothness coefficient, allows for a convenient trade-off between the priorities of speed and smoothness. The adaptive computation algorithm for peak jerk results in a constant average acceleration along paths of any length, ensuring a consistent level of work efficiency regardless of the density of path control points. Through a comprehensive evaluation of the critical constraints for each potential profile type, the single joint’s time-optimal and multiple joints’ time-synchronized planning problems are solved with closed-form solutions. Furthermore, by designing a multi-joint multi-point velocity look-ahead strategy, time-optimal multi-point trajectory planning for robotic manipulators is realized. Simulation and experimental results on a manipulator demonstrate the effectiveness of the proposed approach in improving time efficiency.
{"title":"Time-optimal multi-point trajectory generation for robotic manipulators with continuous jerk and constant average acceleration","authors":"Weiguang Yu , Daokui Qu , Fang Xu , Lei Zhang , Fengshan Zou , Zhenjun Du","doi":"10.1016/j.conengprac.2024.106154","DOIUrl":"10.1016/j.conengprac.2024.106154","url":null,"abstract":"<div><div>To meet the demands of high-speed and high-accuracy applications of robotic manipulators, this paper proposes a time-optimal multi-point trajectory planning method with continuous jerk and constant average acceleration. A piecewise sine jerk model is developed for jerk continuity throughout the entire motion profile. An equivalent transformation of this complex model into the simple trapezoidal velocity model is proposed, effectively reducing the computational complexity and ensuring the reliability of real-time planning. The introduction of a parameter, named the trajectory smoothness coefficient, allows for a convenient trade-off between the priorities of speed and smoothness. The adaptive computation algorithm for peak jerk results in a constant average acceleration along paths of any length, ensuring a consistent level of work efficiency regardless of the density of path control points. Through a comprehensive evaluation of the critical constraints for each potential profile type, the single joint’s time-optimal and multiple joints’ time-synchronized planning problems are solved with closed-form solutions. Furthermore, by designing a multi-joint multi-point velocity look-ahead strategy, time-optimal multi-point trajectory planning for robotic manipulators is realized. Simulation and experimental results on a manipulator demonstrate the effectiveness of the proposed approach in improving time efficiency.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106154"},"PeriodicalIF":5.4,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586646","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 : 2024-11-05DOI: 10.1016/j.conengprac.2024.106115
Mehdi Naderi , Panagiotis Typaldos , Markos Papageorgiou
The operation of signal-free intersections, where Connected Automated Vehicles (CAVs) cross simultaneously for all Origin-Destination (OD) movements, has the potential to greatly increase throughput and reduce fuel consumption. Since the intersection crossing areas naturally include no lanes, an extended crossing area, appropriately delineated, can be considered as a lane-free infrastructure so as to enable further efficiency benefits. This paper presents two Model Predictive Control (MPC) schemes to manage CAVs in signal-free and lane-free intersections. In fact, the control inputs of all vehicles are optimized over a time-horizon by online solving of a joint Optimal Control Problem (OCP) that minimizes a cost function including proper terms to ensure smooth and collision-free vehicle motion, while also considering fuel consumption and desired-speed tracking, when possible. Additionally, appropriate constraints are designed to respect the intersection boundaries and ensure smooth vehicle movements towards their respective destinations. A fast Feasible Direction Algorithm (FDA) is employed for the numerical solution of the introduced OCP. Multiple simulations are carried out to assess the efficiency and practicality of the proposed methods. A comparison with signalized intersection operation is provided.
{"title":"Lane-free signal-free intersection crossing via model predictive control","authors":"Mehdi Naderi , Panagiotis Typaldos , Markos Papageorgiou","doi":"10.1016/j.conengprac.2024.106115","DOIUrl":"10.1016/j.conengprac.2024.106115","url":null,"abstract":"<div><div>The operation of signal-free intersections, where Connected Automated Vehicles (CAVs) cross simultaneously for all Origin-Destination (OD) movements, has the potential to greatly increase throughput and reduce fuel consumption. Since the intersection crossing areas naturally include no lanes, an extended crossing area, appropriately delineated, can be considered as a lane-free infrastructure so as to enable further efficiency benefits. This paper presents two Model Predictive Control (MPC) schemes to manage CAVs in signal-free and lane-free intersections. In fact, the control inputs of all vehicles are optimized over a time-horizon by online solving of a joint Optimal Control Problem (OCP) that minimizes a cost function including proper terms to ensure smooth and collision-free vehicle motion, while also considering fuel consumption and desired-speed tracking, when possible. Additionally, appropriate constraints are designed to respect the intersection boundaries and ensure smooth vehicle movements towards their respective destinations. A fast Feasible Direction Algorithm (FDA) is employed for the numerical solution of the introduced OCP. Multiple simulations are carried out to assess the efficiency and practicality of the proposed methods. A comparison with signalized intersection operation is provided.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106115"},"PeriodicalIF":5.4,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142585986","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 : 2024-11-04DOI: 10.1016/j.conengprac.2024.106137
Longqing Li , Kang Song , Guojie Tang , Wenchao Xue , Hui Xie , Jingping Ma
In this paper, a novel algorithm for vehicle path tracking control is introduced, focusing on maintaining tracking accuracy and minimizing steering wheel oscillation to enhance mechanism lifespan and passenger comfort. Vehicle kinematics model is utilized to formulate a second-order dynamic equation for lateral error, integrating yaw error into the standard first-order dynamic equation. A Proportional-Derivative (PD) controller is designed, incorporating an ‘extended state’ to compensate for the discrepancy between the model and actual vehicle dynamics, termed as the ‘total disturbance’. This ‘total disturbance’ is observed by an Extended State Observer (ESO), and a disturbance rejection law, combined with the PD controller, is employed to achieve the desired yaw rate. For improved vehicle safety and comfort, a dynamic constraint on the yaw rate, based on the vehicle’s motion and dynamic principles, is proposed. The vehicle’s nonlinear dynamics are addressed through feedback linearization, converting the target yaw rate into the required steering angle, which is then executed by the steer-by-wire system. An adaptive online algorithm for adjusting the ESO bandwidth, using Q-learning, is implemented. This optimization aims to balance tracking accuracy and steering wheel oscillation. A mathematical analysis confirms the stability of the time-varying bandwidth ESO and the overall system, ensuring limited estimation and control errors. Experimental comparison with the classical Stanley and Model Predictive Control (MPC) method demonstrates the algorithm’s effectiveness, maintaining lateral error within ±0.1 m.
{"title":"Active disturbance rejection path tracking control of vehicles with adaptive observer bandwidth based on Q-learning","authors":"Longqing Li , Kang Song , Guojie Tang , Wenchao Xue , Hui Xie , Jingping Ma","doi":"10.1016/j.conengprac.2024.106137","DOIUrl":"10.1016/j.conengprac.2024.106137","url":null,"abstract":"<div><div>In this paper, a novel algorithm for vehicle path tracking control is introduced, focusing on maintaining tracking accuracy and minimizing steering wheel oscillation to enhance mechanism lifespan and passenger comfort. Vehicle kinematics model is utilized to formulate a second-order dynamic equation for lateral error, integrating yaw error into the standard first-order dynamic equation. A Proportional-Derivative (PD) controller is designed, incorporating an ‘extended state’ to compensate for the discrepancy between the model and actual vehicle dynamics, termed as the ‘total disturbance’. This ‘total disturbance’ is observed by an Extended State Observer (ESO), and a disturbance rejection law, combined with the PD controller, is employed to achieve the desired yaw rate. For improved vehicle safety and comfort, a dynamic constraint on the yaw rate, based on the vehicle’s motion and dynamic principles, is proposed. The vehicle’s nonlinear dynamics are addressed through feedback linearization, converting the target yaw rate into the required steering angle, which is then executed by the steer-by-wire system. An adaptive online algorithm for adjusting the ESO bandwidth, using Q-learning, is implemented. This optimization aims to balance tracking accuracy and steering wheel oscillation. A mathematical analysis confirms the stability of the time-varying bandwidth ESO and the overall system, ensuring limited estimation and control errors. Experimental comparison with the classical Stanley and Model Predictive Control (MPC) method demonstrates the algorithm’s effectiveness, maintaining lateral error within ±0.1 m.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106137"},"PeriodicalIF":5.4,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578335","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 : 2024-11-04DOI: 10.1016/j.conengprac.2024.106156
Shunhua Ou , Yonghua Yu , Nao Hu , Lei Hu , Jianguo Yang
Closed-loop combustion control (CLCC) is an efficient method for minimizing cylinder-to-cylinder combustion variation by adjusting individual cylinder fuel injection parameters. It is complementary to the closed-loop speed control, which precisely controls the engine speed by manipulating the global fuel injection parameters. However, the application of CLCC changed the conventional closed-loop speed control to a complex control structure. In addition, the selection of combustion control parameters will not only influence the combustion heat release control precisely, but also lead to increased calibration effort for the combustion controller. In this research, a triple closed-loop control strategy, in conjunction with a set-point online generation method, was proposed to improve the cylinder-to-cylinder combustion homogeneity, maintain the desired engine speed, and reduce the calibration effort simultaneously. A coefficient of variation in crank angle domain was utilized to analyze the cylinder-to-cylinder combustion homogeneity. The triple closed-loop control strategy was implemented on a marine medium-speed diesel engine. The experimental results indicated that the proposed control strategy, compared with the speed & IMEP (indicated mean effective pressure) cooperative control and speed & MFB50 (crank angle when 50 % fuel is consumed) cooperative control, has a better potential to alleviate cylinder-to-cylinder pressure variations at the same crankshaft angle. The cylinder-to-cylinder variation of IMEP and MFB50 decreased by 61 % and 38 % compared to the closed-loop speed control, respectively. The cylinder-to-cylinder combustion inhomogeneity, resulting from engine long-time operation and ambient conditions change, was significantly reduced as well. Therefore, the proposed strategy provides a multi-objective precise control method that allows the extension to low-carbon and zero-carbon marine engines.
{"title":"Study of control strategy for cylinder-to-cylinder combustion homogeneity of marine medium-speed diesel engines","authors":"Shunhua Ou , Yonghua Yu , Nao Hu , Lei Hu , Jianguo Yang","doi":"10.1016/j.conengprac.2024.106156","DOIUrl":"10.1016/j.conengprac.2024.106156","url":null,"abstract":"<div><div>Closed-loop combustion control (CLCC) is an efficient method for minimizing cylinder-to-cylinder combustion variation by adjusting individual cylinder fuel injection parameters. It is complementary to the closed-loop speed control, which precisely controls the engine speed by manipulating the global fuel injection parameters. However, the application of CLCC changed the conventional closed-loop speed control to a complex control structure. In addition, the selection of combustion control parameters will not only influence the combustion heat release control precisely, but also lead to increased calibration effort for the combustion controller. In this research, a triple closed-loop control strategy, in conjunction with a set-point online generation method, was proposed to improve the cylinder-to-cylinder combustion homogeneity, maintain the desired engine speed, and reduce the calibration effort simultaneously. A coefficient of variation in crank angle domain was utilized to analyze the cylinder-to-cylinder combustion homogeneity. The triple closed-loop control strategy was implemented on a marine medium-speed diesel engine. The experimental results indicated that the proposed control strategy, compared with the speed & IMEP (indicated mean effective pressure) cooperative control and speed & MFB50 (crank angle when 50 % fuel is consumed) cooperative control, has a better potential to alleviate cylinder-to-cylinder pressure variations at the same crankshaft angle. The cylinder-to-cylinder variation of IMEP and MFB50 decreased by 61 % and 38 % compared to the closed-loop speed control, respectively. The cylinder-to-cylinder combustion inhomogeneity, resulting from engine long-time operation and ambient conditions change, was significantly reduced as well. Therefore, the proposed strategy provides a multi-objective precise control method that allows the extension to low-carbon and zero-carbon marine engines.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106156"},"PeriodicalIF":5.4,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578334","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 : 2024-11-01DOI: 10.1016/j.conengprac.2024.106133
A. Benevieri, M. Marchesoni, M. Passalacqua, P. Pozzobon, L. Vaccaro
A direct torque control (DTC) algorithm with synchronous modulation for high-power induction motors is presented in this paper. While maintaining the dynamic response and robustness of a PI-based DTC operating in the stationary reference frame, the proposed scheme is able to keep an integer PWM modulation ratio, adjusting the stator flux angle and the switching period at each control step so that the synchronicity condition is always satisfied. In this way, it is possible to achieve an improvement of the very low-frequency harmonic spectrum of the torque, in particular by reducing torque sub-harmonics. These represent one of the main problems associated with low-frequency modulation typical of high-power drives and their reduction allows to avoid drawbacks such as resonance and mechanical stresses. The performance of the proposed algorithm is evaluated with experimental tests on a small-scale test bench.
本文提出了一种针对大功率感应电机的同步调制直接转矩控制（DTC）算法。在保持基于 PI 的 DTC 在静态参考帧中运行的动态响应和鲁棒性的同时，所提出的方案能够保持整数 PWM 调制比，在每个控制步骤中调整定子磁通角和开关周期，从而始终满足同步性条件。通过这种方式，可以改善转矩的极低频谐波频谱，特别是通过减少转矩次谐波。次谐波是与大功率驱动器典型的低频调制相关的主要问题之一，减少次谐波可以避免共振和机械应力等缺点。通过在小型试验台上进行实验测试，对所提出算法的性能进行了评估。
{"title":"Synchronous DTC for torque sub-harmonic reduction in low switching frequency induction motor drives","authors":"A. Benevieri, M. Marchesoni, M. Passalacqua, P. Pozzobon, L. Vaccaro","doi":"10.1016/j.conengprac.2024.106133","DOIUrl":"10.1016/j.conengprac.2024.106133","url":null,"abstract":"<div><div>A direct torque control (DTC) algorithm with synchronous modulation for high-power induction motors is presented in this paper. While maintaining the dynamic response and robustness of a PI-based DTC operating in the stationary reference frame, the proposed scheme is able to keep an integer PWM modulation ratio, adjusting the stator flux angle and the switching period at each control step so that the synchronicity condition is always satisfied. In this way, it is possible to achieve an improvement of the very low-frequency harmonic spectrum of the torque, in particular by reducing torque sub-harmonics. These represent one of the main problems associated with low-frequency modulation typical of high-power drives and their reduction allows to avoid drawbacks such as resonance and mechanical stresses. The performance of the proposed algorithm is evaluated with experimental tests on a small-scale test bench.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106133"},"PeriodicalIF":5.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571577","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 : 2024-10-30DOI: 10.1016/j.conengprac.2024.106141
Catalin Stefan Teodorescu, Andrew West, Barry Lennox
Designing an obstacle avoidance algorithm that incorporates the stochastic nature of human–robot-environment interactions is challenging. In high risk activities, such as those found in nuclear environments, a comprehensive approach towards handling uncertainty is essential. In this article, in the context of safe teleoperation of robots, an automated iterative sampling procedure based on Bayesian optimization is proposed, where the robot is trained to predict the behaviour of a human operator. Specifically, a Gaussian process regression model is used to learn an effective representation of a safe stop manoeuvre, required for implementing an obstacle avoidance shared control algorithm. This model is then used to predict the future time duration to execute a safe stop manoeuvre, given the current real-world circumstances. The control algorithm expects this value to be reasonably high; if not, it will gradually reduce the human operator’s authority. A distinctive attribute of the proposed method is the use of statistical confidence metrics as tuning parameters, intended to provide a statistical indication of whether or not an obstacle will be avoided. The proof-of-concept experiments were carried out using three robotic platforms suited for use in nuclear robotics, an amphibious SuperDroid HD2 robot equipped with a Velodyne VLP16 (a 3D lidar), an AgileX Scout Mini R&D Pro land robot fitted with a Realsense D435 depth camera, and a Husarion ROSBot 2.0 Pro supplied with an RPLIDAR A3 (a 2D lidar). The test results show that the proposed Bayesian optimization method uses 8 times less data compared to an exhaustive grid approach, and that it provides a robot-agnostic, robust obstacle avoidance.
{"title":"Bayesian optimization with embedded stochastic functionality for enhanced robotic obstacle avoidance","authors":"Catalin Stefan Teodorescu, Andrew West, Barry Lennox","doi":"10.1016/j.conengprac.2024.106141","DOIUrl":"10.1016/j.conengprac.2024.106141","url":null,"abstract":"<div><div>Designing an obstacle avoidance algorithm that incorporates the stochastic nature of human–robot-environment interactions is challenging. In high risk activities, such as those found in nuclear environments, a comprehensive approach towards handling uncertainty is essential. In this article, in the context of safe teleoperation of robots, an automated iterative sampling procedure based on Bayesian optimization is proposed, where the robot is trained to predict the behaviour of a human operator. Specifically, a Gaussian process regression model is used to learn an effective representation of a safe stop manoeuvre, required for implementing an obstacle avoidance shared control algorithm. This model is then used to predict the future time duration to execute a safe stop manoeuvre, given the current real-world circumstances. The control algorithm expects this value to be reasonably high; if not, it will gradually reduce the human operator’s authority. A distinctive attribute of the proposed method is the use of statistical confidence metrics as tuning parameters, intended to provide a statistical indication of whether or not an obstacle will be avoided. The proof-of-concept experiments were carried out using three robotic platforms suited for use in nuclear robotics, an amphibious SuperDroid HD2 robot equipped with a Velodyne VLP16 (a 3D lidar), an AgileX Scout Mini R&D Pro land robot fitted with a Realsense D435 depth camera, and a Husarion ROSBot 2.0 Pro supplied with an RPLIDAR A3 (a 2D lidar). The test results show that the proposed Bayesian optimization method uses 8 times less data compared to an exhaustive grid approach, and that it provides a robot-agnostic, robust obstacle avoidance.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106141"},"PeriodicalIF":5.4,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553443","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 : 2024-10-29DOI: 10.1016/j.conengprac.2024.106140
Liujiayi Zhao, Pengyu Song, Chunhui Zhao
Existing root cause diagnosis (RCD) methods infer causal relationships among abnormal variables by decomposing causal graphs into intra-group and inter-group levels, reducing redundancy according to direct causality. However, the indirect causality trigged by wide-range fault propagation may be ignored when inferring within groups, leading to the mismatch between causality distribution and grouping results. To overcome the challenge, we propose a causal similarity learning method with multi-level predictive relation aggregation, which contains a complementary similarity measurement framework covering both single-level and high-level causal relationships. First, an attention mechanism with temporal misalignment is designed, which can convert the undirected correlations of features into directed high-level causal similarity by extracting lagged predictive relations. Further, a graph-cutting penalty term is proposed to promote causality distribution to exhibit intra-group denseness and inter-group sparsity, so that single-level causal similarity can be considered during grouping. Finally, a dual RCD method is proposed to search root causes from the causal graph with intra-group and inter-group causality. In this way, numerous redundant causations caused by complex fault propagation can be succinctly described by inter-group causation, and the search for root cause variables can be limited to subgroups to improve diagnosis efficiency. The validity of the proposed method is illustrated through both the Tennessee Eastman benchmark example and a real industrial process.
{"title":"Causal similarity learning with multi-level predictive relation aggregation for grouped root cause diagnosis of industrial faults","authors":"Liujiayi Zhao, Pengyu Song, Chunhui Zhao","doi":"10.1016/j.conengprac.2024.106140","DOIUrl":"10.1016/j.conengprac.2024.106140","url":null,"abstract":"<div><div>Existing root cause diagnosis (RCD) methods infer causal relationships among abnormal variables by decomposing causal graphs into intra-group and inter-group levels, reducing redundancy according to direct causality. However, the indirect causality trigged by wide-range fault propagation may be ignored when inferring within groups, leading to the mismatch between causality distribution and grouping results. To overcome the challenge, we propose a causal similarity learning method with multi-level predictive relation aggregation, which contains a complementary similarity measurement framework covering both single-level and high-level causal relationships. First, an attention mechanism with temporal misalignment is designed, which can convert the undirected correlations of features into directed high-level causal similarity by extracting lagged predictive relations. Further, a graph-cutting penalty term is proposed to promote causality distribution to exhibit intra-group denseness and inter-group sparsity, so that single-level causal similarity can be considered during grouping. Finally, a dual RCD method is proposed to search root causes from the causal graph with intra-group and inter-group causality. In this way, numerous redundant causations caused by complex fault propagation can be succinctly described by inter-group causation, and the search for root cause variables can be limited to subgroups to improve diagnosis efficiency. The validity of the proposed method is illustrated through both the Tennessee Eastman benchmark example and a real industrial process.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106140"},"PeriodicalIF":5.4,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539824","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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