Pub Date : 2023-03-15DOI: 10.1109/ICM54990.2023.10101958
Dominik Reitmeier, A. Mertens
In gearboxes, internal excitation mechanisms cause vibrations during power transmission. These periodic vibrations propagate through the shafts and bearings to the housing and are radiated from there as noise. In this paper, a method is presented to reduce the vibrations and noise by controlling the drive torque. An FxLMS algorithm with secondary path identification is used to determine the compensation torque. In addition, a control strategy is presented to provide the high frequency currents for the compensation torque. Experimental results on a three-stage spur gearbox show that acceleration can be reduced by up to 90%. Noise can be reduced by up to 10dB.
{"title":"Active Reduction of Gear Mesh Vibrations by Drive Torque Control","authors":"Dominik Reitmeier, A. Mertens","doi":"10.1109/ICM54990.2023.10101958","DOIUrl":"https://doi.org/10.1109/ICM54990.2023.10101958","url":null,"abstract":"In gearboxes, internal excitation mechanisms cause vibrations during power transmission. These periodic vibrations propagate through the shafts and bearings to the housing and are radiated from there as noise. In this paper, a method is presented to reduce the vibrations and noise by controlling the drive torque. An FxLMS algorithm with secondary path identification is used to determine the compensation torque. In addition, a control strategy is presented to provide the high frequency currents for the compensation torque. Experimental results on a three-stage spur gearbox show that acceleration can be reduced by up to 90%. Noise can be reduced by up to 10dB.","PeriodicalId":416176,"journal":{"name":"2023 IEEE International Conference on Mechatronics (ICM)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117267953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-15DOI: 10.1109/ICM54990.2023.10101984
Yifei Li, E. Kampen
This paper deals with the design of an adaptive optimal controller for a fixed-wing Unmanned Aerial Vehicle(UAV) using an incremental value iteration algorithm. The incremental model is firstly introduced to linearize a nonlinear system. The recursive least squares(RLS) identification algorithm is then used to identify the incremental model. Based on incremental control, the incremental value iteration algorithm is developed for a nonlinear optimal control problem. Moreover, this algorithm is applied to longitudinal attitude tracking of a fixed-wing unmanned aerial vehicle. Simulation results show that the designed adaptive flight controller is robust to variations in initial value of the angle of attack.
{"title":"Adaptive Optimal Flight Control for a Fixed-wing Unmanned Aerial Vehicle using Incremental Value Iteration","authors":"Yifei Li, E. Kampen","doi":"10.1109/ICM54990.2023.10101984","DOIUrl":"https://doi.org/10.1109/ICM54990.2023.10101984","url":null,"abstract":"This paper deals with the design of an adaptive optimal controller for a fixed-wing Unmanned Aerial Vehicle(UAV) using an incremental value iteration algorithm. The incremental model is firstly introduced to linearize a nonlinear system. The recursive least squares(RLS) identification algorithm is then used to identify the incremental model. Based on incremental control, the incremental value iteration algorithm is developed for a nonlinear optimal control problem. Moreover, this algorithm is applied to longitudinal attitude tracking of a fixed-wing unmanned aerial vehicle. Simulation results show that the designed adaptive flight controller is robust to variations in initial value of the angle of attack.","PeriodicalId":416176,"journal":{"name":"2023 IEEE International Conference on Mechatronics (ICM)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123075657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-15DOI: 10.1109/ICM54990.2023.10101884
Yipu Sun, Xin Chen, Wenpeng He, Luo Wang, E. F. Fukushima, Jinhua She
This paper present a feedback linearization technique for affine nonlinear systems that is independent of system dynamics. First, a input-output feedback linearization correction framework is described, and a interference estimator is employed to guarantee the stability of plant during the learning process. Then, a model-free Q-learning algorithm is presented to solve the feedback linearized controller. Finally, the position control of a single-link flexible joint manipulator system is used as an example to demonstrate the effectiveness of the method.
{"title":"Q-learning-based feedback linearization method for unknown dynamics","authors":"Yipu Sun, Xin Chen, Wenpeng He, Luo Wang, E. F. Fukushima, Jinhua She","doi":"10.1109/ICM54990.2023.10101884","DOIUrl":"https://doi.org/10.1109/ICM54990.2023.10101884","url":null,"abstract":"This paper present a feedback linearization technique for affine nonlinear systems that is independent of system dynamics. First, a input-output feedback linearization correction framework is described, and a interference estimator is employed to guarantee the stability of plant during the learning process. Then, a model-free Q-learning algorithm is presented to solve the feedback linearized controller. Finally, the position control of a single-link flexible joint manipulator system is used as an example to demonstrate the effectiveness of the method.","PeriodicalId":416176,"journal":{"name":"2023 IEEE International Conference on Mechatronics (ICM)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123260271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-15DOI: 10.1109/ICM54990.2023.10102093
L. Aguilar, Y. Orlov
Self-oscillations on a rope may help to release it when stuck inside a surface or rubbish. Here, we introduced the two-relay boundary controller to induce self-oscillations in a flexible rope or cable, governed as a hyperbolic partial differential equation. The two-relay controller has been used to induce periodic motion systems governed by nonlinear ordinary differential equations. As a contribution, the two-relay controller was extended to a class of partial differential equations. The asymptotic stability, without self-oscillator, was proved by means of a Lyapunov functional. Finally, we presented simulation results validating the proposed methodology.
{"title":"Generation of Self-oscillation in a Flexible Rope using Boundary Two-Relay Controller","authors":"L. Aguilar, Y. Orlov","doi":"10.1109/ICM54990.2023.10102093","DOIUrl":"https://doi.org/10.1109/ICM54990.2023.10102093","url":null,"abstract":"Self-oscillations on a rope may help to release it when stuck inside a surface or rubbish. Here, we introduced the two-relay boundary controller to induce self-oscillations in a flexible rope or cable, governed as a hyperbolic partial differential equation. The two-relay controller has been used to induce periodic motion systems governed by nonlinear ordinary differential equations. As a contribution, the two-relay controller was extended to a class of partial differential equations. The asymptotic stability, without self-oscillator, was proved by means of a Lyapunov functional. Finally, we presented simulation results validating the proposed methodology.","PeriodicalId":416176,"journal":{"name":"2023 IEEE International Conference on Mechatronics (ICM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127581227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-15DOI: 10.1109/ICM54990.2023.10101964
A. Chadha, Vishrut Jain, A. Lazcano, Barys Shyrokau
Driving simulators have been used in the automotive industry for many years because of their ability to perform tests in a safe, reproducible and controlled immersive virtual environment. The improved performance of the simulator and its ability to recreate in-vehicle experience for the user is established through motion cueing algorithms (MCA). Such algorithms have constantly been developed with model predictive control (MPC) acting as the main control technique. Currently, available MPC-based methods either compute the optimal controller online or derive an explicit control law offline. These approaches limit the applicability of the MCA for real-time applications due to online computational costs and/or offline memory storage issues. This research presents a solution to deal with issues of offline and online solving through a hybrid approach. For this, an explicit MPC is used to generate a look-up table to provide an initial guess as a warm-start for the implicit MPC-based MCA. From the simulations, it is observed that the presented hybrid approach is able to reduce online computation load by shifting it offline using the explicit controller. Further, the algorithm demonstrates a good tracking performance with a significant reduction of computation time in a complex driving scenario using an emulator environment of a driving simulator.
{"title":"Computationally-efficient Motion Cueing Algorithm via Model Predictive Control","authors":"A. Chadha, Vishrut Jain, A. Lazcano, Barys Shyrokau","doi":"10.1109/ICM54990.2023.10101964","DOIUrl":"https://doi.org/10.1109/ICM54990.2023.10101964","url":null,"abstract":"Driving simulators have been used in the automotive industry for many years because of their ability to perform tests in a safe, reproducible and controlled immersive virtual environment. The improved performance of the simulator and its ability to recreate in-vehicle experience for the user is established through motion cueing algorithms (MCA). Such algorithms have constantly been developed with model predictive control (MPC) acting as the main control technique. Currently, available MPC-based methods either compute the optimal controller online or derive an explicit control law offline. These approaches limit the applicability of the MCA for real-time applications due to online computational costs and/or offline memory storage issues. This research presents a solution to deal with issues of offline and online solving through a hybrid approach. For this, an explicit MPC is used to generate a look-up table to provide an initial guess as a warm-start for the implicit MPC-based MCA. From the simulations, it is observed that the presented hybrid approach is able to reduce online computation load by shifting it offline using the explicit controller. Further, the algorithm demonstrates a good tracking performance with a significant reduction of computation time in a complex driving scenario using an emulator environment of a driving simulator.","PeriodicalId":416176,"journal":{"name":"2023 IEEE International Conference on Mechatronics (ICM)","volume":"673 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115122240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-10DOI: 10.1109/ICM54990.2023.10101932
Mohammad Otoofi, William J. B. Midgley, L. Laine, Henderson Leon, L. Justham, James Fleming
It is common to utilise dynamic models to measure the tyre-road friction in real-time. Alternatively, predictive approaches estimate the tyre-road friction by identifying the environmental factors affecting it. This work aims to formulate the problem of friction estimation as a visual perceptual learning task. The problem is broken down into detecting surface characteristics by applying semantic segmentation and using the extracted features to predict the frictional force. This work for the first time formulates the friction estimation problem as a regression from the latent space of a semantic segmentation model. The preliminary results indicate that this approach can estimate frictional force.
{"title":"Estimating friction coefficient using generative modelling","authors":"Mohammad Otoofi, William J. B. Midgley, L. Laine, Henderson Leon, L. Justham, James Fleming","doi":"10.1109/ICM54990.2023.10101932","DOIUrl":"https://doi.org/10.1109/ICM54990.2023.10101932","url":null,"abstract":"It is common to utilise dynamic models to measure the tyre-road friction in real-time. Alternatively, predictive approaches estimate the tyre-road friction by identifying the environmental factors affecting it. This work aims to formulate the problem of friction estimation as a visual perceptual learning task. The problem is broken down into detecting surface characteristics by applying semantic segmentation and using the extracted features to predict the frictional force. This work for the first time formulates the friction estimation problem as a regression from the latent space of a semantic segmentation model. The preliminary results indicate that this approach can estimate frictional force.","PeriodicalId":416176,"journal":{"name":"2023 IEEE International Conference on Mechatronics (ICM)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123634143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-06DOI: 10.1109/ICM54990.2023.10102076
M. Aydin, E. Sariyildiz, Charbel D. Tawk, R. Mutlu, G. Alici
This paper proposes a new variable stiffness soft gripper that enables high-performance grasping tasks in industrial applications. The design of the proposed monolithic soft gripper includes a middle bellow and two side bellows (i.e., fingers). The positions of the fingers are regulated by adjusting the negative pressure in the middle bellow actuator via an on-off controller. The stiffness of the soft gripper is modulated by controlling the positive pressure in the fingers through the use of a proportional air-pressure regulator. It is experimentally shown that the proposed soft gripper can modulate its stiffness by 125% within 250ms. It is also shown that the variable stiffness soft gripper can help improve the safety and performance of grasping tasks in industrial applications.
{"title":"Variable Stiffness Improves Safety and Performance in Soft Robotics","authors":"M. Aydin, E. Sariyildiz, Charbel D. Tawk, R. Mutlu, G. Alici","doi":"10.1109/ICM54990.2023.10102076","DOIUrl":"https://doi.org/10.1109/ICM54990.2023.10102076","url":null,"abstract":"This paper proposes a new variable stiffness soft gripper that enables high-performance grasping tasks in industrial applications. The design of the proposed monolithic soft gripper includes a middle bellow and two side bellows (i.e., fingers). The positions of the fingers are regulated by adjusting the negative pressure in the middle bellow actuator via an on-off controller. The stiffness of the soft gripper is modulated by controlling the positive pressure in the fingers through the use of a proportional air-pressure regulator. It is experimentally shown that the proposed soft gripper can modulate its stiffness by 125% within 250ms. It is also shown that the variable stiffness soft gripper can help improve the safety and performance of grasping tasks in industrial applications.","PeriodicalId":416176,"journal":{"name":"2023 IEEE International Conference on Mechatronics (ICM)","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131869507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-06DOI: 10.1109/ICM54990.2023.10101940
E. Sariyildiz
This paper proposes a new stability analysis for the Reaction Torque Observer (RTOb) based robust force control systems in the discrete-time domain. The robust force controller is implemented by employing a Disturbance Observer (DOb) to suppress disturbances, such as friction and hysteresis, in an innerloop and another disturbance observer, viz RTOb, to estimate contact forces without using a force sensor. Since the RTOb-based robust force controllers are always implemented using computers and/or microcontrollers, this paper proposes a stability analysis in the discrete-time domain. It is shown that the bandwidth of the DOb is limited not only by the noise of velocity measurement but also by the waterbed effect. It is also shown that the stability of the robust force controller may significantly deteriorate when the design parameters of the RTOb are not properly tuned. For example, the robust force controller may have a non-minimum phase zero(s) as the design parameter of the identified inertia (torque coefficient) of the RTOb is increased (decreased). This may lead to poor stability and performance in force control applications. The proposed stability analysis conducted in the discrete-time domain is verified by simulations and experiments.
{"title":"A Stability Analysis for the Reaction Torque Observer-based Sensorless Force Control Systems","authors":"E. Sariyildiz","doi":"10.1109/ICM54990.2023.10101940","DOIUrl":"https://doi.org/10.1109/ICM54990.2023.10101940","url":null,"abstract":"This paper proposes a new stability analysis for the Reaction Torque Observer (RTOb) based robust force control systems in the discrete-time domain. The robust force controller is implemented by employing a Disturbance Observer (DOb) to suppress disturbances, such as friction and hysteresis, in an innerloop and another disturbance observer, viz RTOb, to estimate contact forces without using a force sensor. Since the RTOb-based robust force controllers are always implemented using computers and/or microcontrollers, this paper proposes a stability analysis in the discrete-time domain. It is shown that the bandwidth of the DOb is limited not only by the noise of velocity measurement but also by the waterbed effect. It is also shown that the stability of the robust force controller may significantly deteriorate when the design parameters of the RTOb are not properly tuned. For example, the robust force controller may have a non-minimum phase zero(s) as the design parameter of the identified inertia (torque coefficient) of the RTOb is increased (decreased). This may lead to poor stability and performance in force control applications. The proposed stability analysis conducted in the discrete-time domain is verified by simulations and experiments.","PeriodicalId":416176,"journal":{"name":"2023 IEEE International Conference on Mechatronics (ICM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122915347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-21DOI: 10.1109/ICM54990.2023.10102013
Shengbin Yu, Xiao Pan, Anastasis Georgiou, Boli Chen, I. Jaimoukha, S. Evangelou
The recent advancement in vehicular networking technology provides novel solutions for designing intelligent and sustainable vehicle motion controllers. This work addresses a car-following task, where the feedback linearisation method is combined with a robust model predictive control (RMPC) scheme to safely, optimally and efficiently control a connected electric vehicle. In particular, the nonlinear dynamics are linearised through a feedback linearisation method to maintain an efficient computational speed and to guarantee global optimality. At the same time, the inevitable model mismatch is dealt with by the RMPC design. The control objective of the RMPC is to optimise the electric energy efficiency of the ego vehicle with consideration of a bounded model mismatch disturbance subject to satisfaction of physical and safety constraints. Numerical results first verify the validity and robustness through a comparison between the proposed RMPC and a nominal MPC. Further investigation into the performance of the proposed method reveals a higher energy efficiency and passenger comfort level as compared to a recently proposed benchmark method using the space-domain modelling approach.
{"title":"A Robust Model Predictive Control Framework for Ecological Adaptive Cruise Control Strategy of Electric Vehicles","authors":"Shengbin Yu, Xiao Pan, Anastasis Georgiou, Boli Chen, I. Jaimoukha, S. Evangelou","doi":"10.1109/ICM54990.2023.10102013","DOIUrl":"https://doi.org/10.1109/ICM54990.2023.10102013","url":null,"abstract":"The recent advancement in vehicular networking technology provides novel solutions for designing intelligent and sustainable vehicle motion controllers. This work addresses a car-following task, where the feedback linearisation method is combined with a robust model predictive control (RMPC) scheme to safely, optimally and efficiently control a connected electric vehicle. In particular, the nonlinear dynamics are linearised through a feedback linearisation method to maintain an efficient computational speed and to guarantee global optimality. At the same time, the inevitable model mismatch is dealt with by the RMPC design. The control objective of the RMPC is to optimise the electric energy efficiency of the ego vehicle with consideration of a bounded model mismatch disturbance subject to satisfaction of physical and safety constraints. Numerical results first verify the validity and robustness through a comparison between the proposed RMPC and a nominal MPC. Further investigation into the performance of the proposed method reveals a higher energy efficiency and passenger comfort level as compared to a recently proposed benchmark method using the space-domain modelling approach.","PeriodicalId":416176,"journal":{"name":"2023 IEEE International Conference on Mechatronics (ICM)","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121522585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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