This paper introduces a novel data‐driven approach to develop a fault‐tolerant model predictive controller (MPC) for non‐linear systems. By adopting a Koopman operator‐theoretic perspective, the proposed method leverages historical data from the system to construct a data‐driven model that captures the non‐linear behaviour and fault characteristics. The fault influence is addressed through an online estimation of a time‐varying Koopman predictor, which allows for adjusting the MPC control law to counteract the fault effects. This estimation is performed in a higher dimensional Koopman feature space, where the dynamics behave linearly. As a result, the non‐linear fault‐tolerant MPC optimization problem can be replaced with a more practical and feasible linear time‐varying one using the approximated Koopman predictor. Moreover, by incorporating the online update procedure, the time‐varying Koopman predictor can represent the dynamics of the faulty system. Hence, the controller can adapt and compensate for the faults in real‐time, integrating the fault diagnosis module in the MPC framework and eliminating the need for a separate fault detection unit. Finally, the efficacy of the proposed approach is demonstrated through case study results, which highlight the ability of the controller to mitigate faults and maintain desired system behaviour.
{"title":"Koopman fault‐tolerant model predictive control","authors":"Mohammadhosein Bakhtiaridoust, Meysam Yadegar, Fatemeh Jahangiri","doi":"10.1049/cth2.12629","DOIUrl":"https://doi.org/10.1049/cth2.12629","url":null,"abstract":"This paper introduces a novel data‐driven approach to develop a fault‐tolerant model predictive controller (MPC) for non‐linear systems. By adopting a Koopman operator‐theoretic perspective, the proposed method leverages historical data from the system to construct a data‐driven model that captures the non‐linear behaviour and fault characteristics. The fault influence is addressed through an online estimation of a time‐varying Koopman predictor, which allows for adjusting the MPC control law to counteract the fault effects. This estimation is performed in a higher dimensional Koopman feature space, where the dynamics behave linearly. As a result, the non‐linear fault‐tolerant MPC optimization problem can be replaced with a more practical and feasible linear time‐varying one using the approximated Koopman predictor. Moreover, by incorporating the online update procedure, the time‐varying Koopman predictor can represent the dynamics of the faulty system. Hence, the controller can adapt and compensate for the faults in real‐time, integrating the fault diagnosis module in the MPC framework and eliminating the need for a separate fault detection unit. Finally, the efficacy of the proposed approach is demonstrated through case study results, which highlight the ability of the controller to mitigate faults and maintain desired system behaviour.","PeriodicalId":502998,"journal":{"name":"IET Control Theory & Applications","volume":" 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139793208","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}
Şükrü Ünver, Erman Selim, E. Tatlıcıoğlu, E. Zergeroğlu, M. Alcı
In this study, task space tracking control of robot manipulators driven by brushless DC (BLDC) motors is considered. Dynamics of actuators are taken into account and the entire electromechanical system (i.e. kinematic, dynamic, and electrical models) is assumed to include parametric/structured uncertainties. A novel adaptive controller is designed and the stability of the closed loop system is ensured via novel Lyapunov type tools. To demonstrate performance and applicability of the proposed method, a simulation study is conducted using the model of a two degree of freedom, planar robotic manipulator driven by BLDC motors.
{"title":"Adaptive control of BLDC driven robot manipulators in task space","authors":"Şükrü Ünver, Erman Selim, E. Tatlıcıoğlu, E. Zergeroğlu, M. Alcı","doi":"10.1049/cth2.12631","DOIUrl":"https://doi.org/10.1049/cth2.12631","url":null,"abstract":"In this study, task space tracking control of robot manipulators driven by brushless DC (BLDC) motors is considered. Dynamics of actuators are taken into account and the entire electromechanical system (i.e. kinematic, dynamic, and electrical models) is assumed to include parametric/structured uncertainties. A novel adaptive controller is designed and the stability of the closed loop system is ensured via novel Lyapunov type tools. To demonstrate performance and applicability of the proposed method, a simulation study is conducted using the model of a two degree of freedom, planar robotic manipulator driven by BLDC motors.","PeriodicalId":502998,"journal":{"name":"IET Control Theory & Applications","volume":"14 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139800577","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}
Paulo V. G. Simplício, João R. S. Benevides, R. S. Inoue, Marco H. Terra
The combination of artificial neural networks with advanced control techniques has shown great potential to reject uncertainties and disturbances that affect the quadrotor during trajectory tracking. However, it is still a complex and little‐explored challenge. In this sense, this work proposes the development of robust and intelligent architectures for position control of quadrotors, improving flight performance during trajectory tracking. The proposed architectures combine a robust linear quadratic regulator (RLQR) with deep neural networks (DNNs). In addition, a comparative study is performed to evaluate the performance of the proposed architectures using three other widely used controllers: linear quadratic regulator (LQR), proportional‐integral‐derivative (PID), and feedback linearization (FL). The architectures were developed using the robot operating system (ROS), and the experiments were performed with a commercial quadrotor, the ParrotTM Bebop 2.0. Flights were performed by applying wind gusts to the aircraft's body, and the experimental results showed that using neural networks combined with controllers, robust or not, improves quadrotors' flight performance.
{"title":"Robust and intelligent control of quadrotors subject to wind gusts","authors":"Paulo V. G. Simplício, João R. S. Benevides, R. S. Inoue, Marco H. Terra","doi":"10.1049/cth2.12615","DOIUrl":"https://doi.org/10.1049/cth2.12615","url":null,"abstract":"The combination of artificial neural networks with advanced control techniques has shown great potential to reject uncertainties and disturbances that affect the quadrotor during trajectory tracking. However, it is still a complex and little‐explored challenge. In this sense, this work proposes the development of robust and intelligent architectures for position control of quadrotors, improving flight performance during trajectory tracking. The proposed architectures combine a robust linear quadratic regulator (RLQR) with deep neural networks (DNNs). In addition, a comparative study is performed to evaluate the performance of the proposed architectures using three other widely used controllers: linear quadratic regulator (LQR), proportional‐integral‐derivative (PID), and feedback linearization (FL). The architectures were developed using the robot operating system (ROS), and the experiments were performed with a commercial quadrotor, the ParrotTM Bebop 2.0. Flights were performed by applying wind gusts to the aircraft's body, and the experimental results showed that using neural networks combined with controllers, robust or not, improves quadrotors' flight performance.","PeriodicalId":502998,"journal":{"name":"IET Control Theory & Applications","volume":"40 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139527911","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}
Jiyu Zhang, Wei Gao, Qing Guo, Xing Ren, Chen Wang, Yan Shi
Being different from many centralized mechatronic systems, the distributed transmission mechanism has the significant advantage such that realize cooperative task only based on small amount neighbour nodes with low computational complexity. In this study, a distributed cooperative control is proposed for multiple electrohydraulic system (MEHS) to guarantee the follower electrohydraulic node tracking the leader motion, based on the approach of directed spanning tree. Firstly, the MEHS model is constructed as three‐orders isomorphic nonlinear dynamics. Then, a disturbance observer is adopted to estimate uncertain nonlinearities caused by hydraulic parametric uncertainties and unknown external loads in the MEHS. To address unknown communication delays in the network topology of MEHS, a quasi‐synchronous controller is designed via Lyapunov–Krasovskii technique to guarantee that the synchronous errors asymptotically converge to a zero neighbourhood. Finally, the effectiveness of the proposed distributed synchronous control is verified by simulation results under uncertain nonlinearities and different communication delays.
{"title":"Distributed cooperative control of mechatronic system driving multiple electrohydraulic actuators with uncertain nonlinearity and communication delay","authors":"Jiyu Zhang, Wei Gao, Qing Guo, Xing Ren, Chen Wang, Yan Shi","doi":"10.1049/cth2.12600","DOIUrl":"https://doi.org/10.1049/cth2.12600","url":null,"abstract":"Being different from many centralized mechatronic systems, the distributed transmission mechanism has the significant advantage such that realize cooperative task only based on small amount neighbour nodes with low computational complexity. In this study, a distributed cooperative control is proposed for multiple electrohydraulic system (MEHS) to guarantee the follower electrohydraulic node tracking the leader motion, based on the approach of directed spanning tree. Firstly, the MEHS model is constructed as three‐orders isomorphic nonlinear dynamics. Then, a disturbance observer is adopted to estimate uncertain nonlinearities caused by hydraulic parametric uncertainties and unknown external loads in the MEHS. To address unknown communication delays in the network topology of MEHS, a quasi‐synchronous controller is designed via Lyapunov–Krasovskii technique to guarantee that the synchronous errors asymptotically converge to a zero neighbourhood. Finally, the effectiveness of the proposed distributed synchronous control is verified by simulation results under uncertain nonlinearities and different communication delays.","PeriodicalId":502998,"journal":{"name":"IET Control Theory & Applications","volume":"346 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139246031","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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