Pub Date : 2024-08-09DOI: 10.1177/01423312241263637
Peng Liu, Nianyi Sun, Haiying Wan, Chengxi Zhang, Jin Zhao, Guangwei Wang
Metaheuristic swarm-based intelligent algorithms are extensively employed for engineering optimization tasks due to their efficacy in addressing nonlinear and high-dimensional challenges. This study presents an improved snake optimization algorithm (SOEA) to overcome the limitations of the standard snake optimization algorithm (SOA), such as slow convergence, subpar optimization accuracy, and vulnerability to local optima. The integration of elite opposition-based learning strategy enables the adjustment of snake population positions, thereby enhancing the algorithm’s global search capacity and iteration speed. Moreover, the incorporation of the adaptive threshold method enhances its local search performance and convergence speed. Experimental results demonstrate the superior performance of the proposed SOEA algorithm in achieving global optimization and accelerating convergence speed. The SOEA algorithm achieves a remarkable 34% reduction in the average number of iterations required compared to the SOA algorithm, and it also exhibits a lower mean squared error. Finally, the effectiveness of the proposed algorithm is validated through its successful application to solving the multi-UAV path planning problem.
基于元搜索群的智能算法因其在解决非线性和高维挑战方面的功效而被广泛用于工程优化任务。本研究提出了一种改进的蛇形优化算法（SOEA），以克服标准蛇形优化算法（SOA）的局限性，如收敛速度慢、优化精度不佳和容易出现局部最优等。基于精英对抗的学习策略可以调整蛇群的位置，从而提高算法的全局搜索能力和迭代速度。此外，自适应阈值方法的加入也提高了局部搜索性能和收敛速度。实验结果表明，所提出的 SOEA 算法在实现全局优化和加快收敛速度方面表现出色。与 SOA 算法相比，SOEA 算法所需的平均迭代次数显著减少了 34%，而且平均平方误差也更小。最后，通过成功应用于解决多无人机路径规划问题，验证了所提算法的有效性。
{"title":"Improved adaptive snake optimization algorithm with application to multi-UAV path planning","authors":"Peng Liu, Nianyi Sun, Haiying Wan, Chengxi Zhang, Jin Zhao, Guangwei Wang","doi":"10.1177/01423312241263637","DOIUrl":"https://doi.org/10.1177/01423312241263637","url":null,"abstract":"Metaheuristic swarm-based intelligent algorithms are extensively employed for engineering optimization tasks due to their efficacy in addressing nonlinear and high-dimensional challenges. This study presents an improved snake optimization algorithm (SOEA) to overcome the limitations of the standard snake optimization algorithm (SOA), such as slow convergence, subpar optimization accuracy, and vulnerability to local optima. The integration of elite opposition-based learning strategy enables the adjustment of snake population positions, thereby enhancing the algorithm’s global search capacity and iteration speed. Moreover, the incorporation of the adaptive threshold method enhances its local search performance and convergence speed. Experimental results demonstrate the superior performance of the proposed SOEA algorithm in achieving global optimization and accelerating convergence speed. The SOEA algorithm achieves a remarkable 34% reduction in the average number of iterations required compared to the SOA algorithm, and it also exhibits a lower mean squared error. Finally, the effectiveness of the proposed algorithm is validated through its successful application to solving the multi-UAV path planning problem.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"36 28","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141924754","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 : 2024-08-09DOI: 10.1177/01423312241265781
Fuqiang Li, Lisai Gao, Kang Li, Chen Peng
This paper investigates the event-driven fuzzy [Formula: see text] control of direct-current (DC) microgrids subject to deception attacks, persistent bounded (PB) disturbances, premise mismatching, quantizer, and delays. First, using states of the fuzzy plant and a constant, a Zeno-free dynamic event-triggered mechanism (ETM) is presented, which is more robust to the PB disturbances than the static state-related ETMs (SSRETMs). Second, by virtually dividing the updating intervals of the controller, a unified time-delay fuzzy system model is established, which takes effects of dynamic ETM, deception attacks, disturbances, quantizer, and delays into account. Third, criteria for globally exponentially ultimately bounded (GEUB) stability in mean square with guaranteed [Formula: see text]-gain are obtained, and the quantitative relationship between the ultimate bound and the dynamic ETM is established. To overcome the inconvenience of the emulation method requiring two design steps, a co-design strategy is provided to simultaneously design the ETM and fuzzy controller subject to premise mismatching. Simulation results confirm that, even with the triggering rate 36.9% and the attacking rate 11.4%, satisfactory control performance can still be achieved; the dynamic ETM achieves better triggering performance than the SSRETMs; and the proposed controller achieves shorter settling time and smaller overshoot than the robust linear controller.
{"title":"Event-driven fuzzy L∞ control of DC microgrids under cyber attacks and quantization","authors":"Fuqiang Li, Lisai Gao, Kang Li, Chen Peng","doi":"10.1177/01423312241265781","DOIUrl":"https://doi.org/10.1177/01423312241265781","url":null,"abstract":"This paper investigates the event-driven fuzzy [Formula: see text] control of direct-current (DC) microgrids subject to deception attacks, persistent bounded (PB) disturbances, premise mismatching, quantizer, and delays. First, using states of the fuzzy plant and a constant, a Zeno-free dynamic event-triggered mechanism (ETM) is presented, which is more robust to the PB disturbances than the static state-related ETMs (SSRETMs). Second, by virtually dividing the updating intervals of the controller, a unified time-delay fuzzy system model is established, which takes effects of dynamic ETM, deception attacks, disturbances, quantizer, and delays into account. Third, criteria for globally exponentially ultimately bounded (GEUB) stability in mean square with guaranteed [Formula: see text]-gain are obtained, and the quantitative relationship between the ultimate bound and the dynamic ETM is established. To overcome the inconvenience of the emulation method requiring two design steps, a co-design strategy is provided to simultaneously design the ETM and fuzzy controller subject to premise mismatching. Simulation results confirm that, even with the triggering rate 36.9% and the attacking rate 11.4%, satisfactory control performance can still be achieved; the dynamic ETM achieves better triggering performance than the SSRETMs; and the proposed controller achieves shorter settling time and smaller overshoot than the robust linear controller.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"8 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141921651","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 : 2024-08-09DOI: 10.1177/01423312241267036
Yu Lu, Cheng-De Zheng
This paper studies the leader-following event-triggered consensus of multi-agent systems with nonlinear dynamics and semi-Markov jump. The information on transition rates is incompletely known. First, by applying weighted orthogonal polynomials, two new integral inequalities are established, which include some existing ones. Second, an event-triggered scheme is presented to save the precious communication resources. After constructing an augmented Lyapunov–Krasovskii functional, a few sufficient conditions are derived to achieve the leader-following event-triggered consensus by utilizing the presented inequalities to manipulate the derivative of the functional. Finally, numerical simulation shows the effectiveness of the proposed method.
{"title":"Event-triggered leader-following consensus of nonlinear semi-Markovian multi-agent systems via improved integral inequalities","authors":"Yu Lu, Cheng-De Zheng","doi":"10.1177/01423312241267036","DOIUrl":"https://doi.org/10.1177/01423312241267036","url":null,"abstract":"This paper studies the leader-following event-triggered consensus of multi-agent systems with nonlinear dynamics and semi-Markov jump. The information on transition rates is incompletely known. First, by applying weighted orthogonal polynomials, two new integral inequalities are established, which include some existing ones. Second, an event-triggered scheme is presented to save the precious communication resources. After constructing an augmented Lyapunov–Krasovskii functional, a few sufficient conditions are derived to achieve the leader-following event-triggered consensus by utilizing the presented inequalities to manipulate the derivative of the functional. Finally, numerical simulation shows the effectiveness of the proposed method.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"4 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141921177","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 : 2024-08-09DOI: 10.1177/01423312241263911
M. Abdelbaky, Xiangjie Liu, Xiaobing Kong
Due to the strong nonlinearities of the boiler-turbine system, feasibility and high response capability are hard to realize using traditional linear controllers. Advanced controllers that can ensure stability and feasibility without violating system constraints, and enhance the system’s dynamic output performance, are needed. Therefore, this paper proposes a stable input/output feedback linearization (IOFL) model predictive controller (MPC) technique for the boiler-turbine system. This nonlinear system is decoupled using the IOFL method into a novel linearized model, which is then used for constituting the proposed stable IOFL MPC problem. The proposed scheme uses a constraint mapping method that converts actual input limits into limits on the basis of the control output variable to ensure a feasible solution over the whole prediction horizon. Moreover, a min-max MPC technique in the form of linear matrix inequality with the realization of the input rate-of-change constraints is utilized to ensure the boiler-turbine unit’s stability. The process model and proposed control scheme are executed using MATLAB and the simulation results demonstrate that the proposed controller has an enhanced dynamic output performance compared with an advanced control scheme under various load variations.
{"title":"Stable constrained model predictive control based on IOFL technique for boiler-turbine system","authors":"M. Abdelbaky, Xiangjie Liu, Xiaobing Kong","doi":"10.1177/01423312241263911","DOIUrl":"https://doi.org/10.1177/01423312241263911","url":null,"abstract":"Due to the strong nonlinearities of the boiler-turbine system, feasibility and high response capability are hard to realize using traditional linear controllers. Advanced controllers that can ensure stability and feasibility without violating system constraints, and enhance the system’s dynamic output performance, are needed. Therefore, this paper proposes a stable input/output feedback linearization (IOFL) model predictive controller (MPC) technique for the boiler-turbine system. This nonlinear system is decoupled using the IOFL method into a novel linearized model, which is then used for constituting the proposed stable IOFL MPC problem. The proposed scheme uses a constraint mapping method that converts actual input limits into limits on the basis of the control output variable to ensure a feasible solution over the whole prediction horizon. Moreover, a min-max MPC technique in the form of linear matrix inequality with the realization of the input rate-of-change constraints is utilized to ensure the boiler-turbine unit’s stability. The process model and proposed control scheme are executed using MATLAB and the simulation results demonstrate that the proposed controller has an enhanced dynamic output performance compared with an advanced control scheme under various load variations.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"49 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141922975","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 : 2024-08-08DOI: 10.1177/01423312241257024
Zhiteng Zheng, Weimin Xu
This paper proposes a novel model-free controller, considering the overhead crane systems cannot be accurately modeled and affected by external disturbances. The suggested scheme contains a variable exponential decoupled-like sliding mode control (VEDSMC), an adaptive power-reaching law (APRL), and a dynamic learning law-based iterative learning controller (DLLILC); they are combined in a parallel structure to form VEDSMC–APRL–DLLILC. The VEDSMC can effectively enhance the convergence speed of the displacement variables and improve the transient performance of the crane system. The APRL consists of an adaptive switching gain; it can estimate the optimal switching gain according to the unknown dynamics of the crane system and the disturbance, reduce the controller chattering, and guarantee the robustness. The DLLILC term can further improve anti-swing and positioning performance of the overhead crane without accurate information of the crane dynamics model in advance. Moreover, a nonlinear dynamic learning law (DLL) is developed to guarantee both convergence speed and steady-state accuracy in the learning process. Finally, the stability analysis of the designed controller is performed using Lyapunov theory and Barbalat’s lemma, and the simulation results illustrate the effectiveness of the suggested control scheme.
{"title":"Robust adaptive decoupled-like sliding mode controller design based on iterative learning for overhead cranes","authors":"Zhiteng Zheng, Weimin Xu","doi":"10.1177/01423312241257024","DOIUrl":"https://doi.org/10.1177/01423312241257024","url":null,"abstract":"This paper proposes a novel model-free controller, considering the overhead crane systems cannot be accurately modeled and affected by external disturbances. The suggested scheme contains a variable exponential decoupled-like sliding mode control (VEDSMC), an adaptive power-reaching law (APRL), and a dynamic learning law-based iterative learning controller (DLLILC); they are combined in a parallel structure to form VEDSMC–APRL–DLLILC. The VEDSMC can effectively enhance the convergence speed of the displacement variables and improve the transient performance of the crane system. The APRL consists of an adaptive switching gain; it can estimate the optimal switching gain according to the unknown dynamics of the crane system and the disturbance, reduce the controller chattering, and guarantee the robustness. The DLLILC term can further improve anti-swing and positioning performance of the overhead crane without accurate information of the crane dynamics model in advance. Moreover, a nonlinear dynamic learning law (DLL) is developed to guarantee both convergence speed and steady-state accuracy in the learning process. Finally, the stability analysis of the designed controller is performed using Lyapunov theory and Barbalat’s lemma, and the simulation results illustrate the effectiveness of the suggested control scheme.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"45 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141929381","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 : 2024-08-08DOI: 10.1177/01423312241266275
Lihui Lu, Rifan Wang, Zhencong Chen, Jiaqi Chen
The main research objective of cross-modal person re-identification is to retrieve matching images of the same person from image repositories in both modalities, given visible light or infrared images of individuals. Due to the significant modality gap between pedestrian images, the task of person re-identification faces considerable challenges. To address this issue, a method is proposed that utilizes the fusion of local effective features and multi-scale features. First, images are transformed into pseudo-infrared images through data augmentation and then a dual-stream network is designed using ResNet50_IBN for feature extraction. Subsequently, pedestrian features extracted from different layers are fused at multiple scales to alleviate feature loss caused during the convolution process. Finally, the model is supervised using global features and local effective features to address issues related to cluttered backgrounds and varying pedestrian positions in images. The proposed method is experimentally validated on the current mainstream cross-modal person re-identification datasets SYSU-MM01 and RegDB, demonstrating improvements in Rank-1 and mAP metrics compared to current state-of-the-art algorithms.
{"title":"Cross-modal person re-identification using fused local effective features and multi-scale features","authors":"Lihui Lu, Rifan Wang, Zhencong Chen, Jiaqi Chen","doi":"10.1177/01423312241266275","DOIUrl":"https://doi.org/10.1177/01423312241266275","url":null,"abstract":"The main research objective of cross-modal person re-identification is to retrieve matching images of the same person from image repositories in both modalities, given visible light or infrared images of individuals. Due to the significant modality gap between pedestrian images, the task of person re-identification faces considerable challenges. To address this issue, a method is proposed that utilizes the fusion of local effective features and multi-scale features. First, images are transformed into pseudo-infrared images through data augmentation and then a dual-stream network is designed using ResNet50_IBN for feature extraction. Subsequently, pedestrian features extracted from different layers are fused at multiple scales to alleviate feature loss caused during the convolution process. Finally, the model is supervised using global features and local effective features to address issues related to cluttered backgrounds and varying pedestrian positions in images. The proposed method is experimentally validated on the current mainstream cross-modal person re-identification datasets SYSU-MM01 and RegDB, demonstrating improvements in Rank-1 and mAP metrics compared to current state-of-the-art algorithms.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141927580","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}
Maintaining the curved path-tracking accuracy and stability of autonomous vehicles under various road conditions is a significant challenge in the field of vehicle control. To address this limitation, a curved path-tracking strategy under variable velocity based on the adaptive model predictive control (MPC) is proposed for autonomous vehicles. Through the analysis of the vehicle dynamics model, the theoretical basis is presented to improve the performance of the curved path tracking by changing the vehicle velocity, and the adaptive velocity (AV) planner is designed to generate variable velocities depending on the path curvature and road friction coefficients. In addition, the adaptive model predictive controller, which adopts the fuzzy inference system with vehicle velocity and path curvature as inputs to obtain the adaptive prediction horizon (APH), is employed to realize curved path-tracking and velocity control with actuator constraints by manipulating the steering angle of the front wheels and the longitudinal tire forces of the vehicle. In comparison with the control strategy with three other control strategies based on MPC algorithm via simulation experiments on the Simulink/CarSim platform, the curved path-tracking control strategy with AV and APH proposed in this paper exhibits satisfactory performance in terms of path-tracking accuracy and stability.
在各种道路条件下保持自动驾驶车辆的曲线路径跟踪精度和稳定性是车辆控制领域的一项重大挑战。针对这一限制，提出了一种基于自适应模型预测控制（MPC）的自动驾驶车辆变速情况下的曲线路径跟踪策略。通过对车辆动力学模型的分析，提出了通过改变车辆速度来提高曲线路径跟踪性能的理论基础，并设计了自适应速度（AV）规划器，以根据路径曲率和道路摩擦系数生成可变速度。此外，自适应模型预测控制器采用模糊推理系统，以车辆速度和路径曲率为输入，获得自适应预测视界（APH），通过操纵前轮转向角和车辆纵向轮胎力，实现具有执行器约束的曲线路径跟踪和速度控制。通过在 Simulink/CarSim 平台上进行仿真实验，将该控制策略与其他三种基于 MPC 算法的控制策略进行比较，本文提出的带有 AV 和 APH 的曲线路径跟踪控制策略在路径跟踪精度和稳定性方面表现出令人满意的性能。
{"title":"Adaptive model predictive control–based curved path-tracking strategy for autonomous vehicles under variable velocity","authors":"Qian Zhang, Huifang Kong, Tiankuo Liu, Xiaoxue Zhang","doi":"10.1177/01423312241267067","DOIUrl":"https://doi.org/10.1177/01423312241267067","url":null,"abstract":"Maintaining the curved path-tracking accuracy and stability of autonomous vehicles under various road conditions is a significant challenge in the field of vehicle control. To address this limitation, a curved path-tracking strategy under variable velocity based on the adaptive model predictive control (MPC) is proposed for autonomous vehicles. Through the analysis of the vehicle dynamics model, the theoretical basis is presented to improve the performance of the curved path tracking by changing the vehicle velocity, and the adaptive velocity (AV) planner is designed to generate variable velocities depending on the path curvature and road friction coefficients. In addition, the adaptive model predictive controller, which adopts the fuzzy inference system with vehicle velocity and path curvature as inputs to obtain the adaptive prediction horizon (APH), is employed to realize curved path-tracking and velocity control with actuator constraints by manipulating the steering angle of the front wheels and the longitudinal tire forces of the vehicle. In comparison with the control strategy with three other control strategies based on MPC algorithm via simulation experiments on the Simulink/CarSim platform, the curved path-tracking control strategy with AV and APH proposed in this paper exhibits satisfactory performance in terms of path-tracking accuracy and stability.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"25 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141925492","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 : 2024-08-08DOI: 10.1177/01423312241263654
Behnam Gharib, Reza Mahboobi Esfanjani
This paper investigates the problem of cooperative source-seeking by networked multi-robot systems subject to variable communication time delay, while all the agents keep a desired formation and avoid collision with each other during the motion. The connection between the robots is modeled by a dynamic and undirected graph, with arbitrary switching based on communication range. The proposed distributed control law which steers the agents toward the source contains a collision avoidance term, developed based on a novel potential function and a field gradient estimation term, computed from the delayed information. Synthesis conditions to adjust the controller parameters are derived in terms of linear matrix inequalities to ensure the team’s convergence to a neighborhood of the source. Finally, simulation results in MATLAB® are presented to demonstrate the efficiency and applicability of the suggested scheme.
{"title":"Control of networked robots subject to communication delay and switching connection topology for cooperative source-seeking with collision avoidance","authors":"Behnam Gharib, Reza Mahboobi Esfanjani","doi":"10.1177/01423312241263654","DOIUrl":"https://doi.org/10.1177/01423312241263654","url":null,"abstract":"This paper investigates the problem of cooperative source-seeking by networked multi-robot systems subject to variable communication time delay, while all the agents keep a desired formation and avoid collision with each other during the motion. The connection between the robots is modeled by a dynamic and undirected graph, with arbitrary switching based on communication range. The proposed distributed control law which steers the agents toward the source contains a collision avoidance term, developed based on a novel potential function and a field gradient estimation term, computed from the delayed information. Synthesis conditions to adjust the controller parameters are derived in terms of linear matrix inequalities to ensure the team’s convergence to a neighborhood of the source. Finally, simulation results in MATLAB® are presented to demonstrate the efficiency and applicability of the suggested scheme.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"78 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141926648","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 : 2024-08-08DOI: 10.1177/01423312241261046
Shourui Wang, Wuyin Jin, Xia Zhang
In order to tackle the uncertainties encountered in the operation of three-dimensional (3D) overhead crane systems and enhance the overall robustness of the control system, an adaptive sliding mode control (SMC) method based on prescribed performance is proposed in this work. Specifically, an integral sliding mode controller (ISMC) based on prescribed performance is designed for the 3D overhead crane dynamics model with double-pendulum effect, which is used to constrict system error. By considering the case of model uncertainty, time-varying parameters, track friction, and so on, the neural network (NN) is employed to estimate unknown terms in the controller design, and the Lyapunov function is applied to analyze the stability of the close-loop system. The results demonstrated that the proposed method can effectively improve the positioning accuracy and payload swing suppression performance of the overhead crane system, and also improve the robustness of the control system to deal with uncertainties.
{"title":"Neural network–based adaptive sliding mode control of three-dimensional double-pendulum overhead cranes with prescribed performance","authors":"Shourui Wang, Wuyin Jin, Xia Zhang","doi":"10.1177/01423312241261046","DOIUrl":"https://doi.org/10.1177/01423312241261046","url":null,"abstract":"In order to tackle the uncertainties encountered in the operation of three-dimensional (3D) overhead crane systems and enhance the overall robustness of the control system, an adaptive sliding mode control (SMC) method based on prescribed performance is proposed in this work. Specifically, an integral sliding mode controller (ISMC) based on prescribed performance is designed for the 3D overhead crane dynamics model with double-pendulum effect, which is used to constrict system error. By considering the case of model uncertainty, time-varying parameters, track friction, and so on, the neural network (NN) is employed to estimate unknown terms in the controller design, and the Lyapunov function is applied to analyze the stability of the close-loop system. The results demonstrated that the proposed method can effectively improve the positioning accuracy and payload swing suppression performance of the overhead crane system, and also improve the robustness of the control system to deal with uncertainties.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"49 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141927937","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 : 2024-08-08DOI: 10.1177/01423312241262067
Hammad Zaki, Aamir Rashid, Usman Masud
Coupled nonlinear systems are difficult to control due to the adverse effects of uncertainties and coupling effects with increased sensor noise. This paper proposes an improved Lyapunov-based composite controller consisting of fractional-order proportional–integral–derivative (FOPID) and velocity-based disturbance observer to deal with the motion control of uncertain, nonlinear, and coupled system. FOPID utilizes the stable filtered error to facilitate the control development and stability analysis for the multi-input multi-output (MIMO) coupled system. Moreover, a disturbance observer is developed by utilizing the velocity signals to provide robustness against the disturbances and parametric uncertainties. Enhanced infinite order disturbance observer (EIFDOB) structure is used to improve the robustness of the introduced technique despite the high-frequency sensor noise. Stability analysis is provided to verify the introduced controller through the Lyapunov stability theorem, LaSalle’s invariance principle, and Barbalat’s lemma. Signal chasing is also presented to show that all signals are ultimately bounded. Comprehensive numerical simulations are performed on high-fidelity and coupled nonlinear model of the twin rotor MIMO system where the efficiency of the presented technique is examined against the external disturbances, matched uncertainties, and sensor noise. The results presented with different scenarios show that the proposed technique performed better with more robustness than FOPID and integer order proportional–integral–derivative.
{"title":"Lyapunov-based fractional-order PID controller design for coupled nonlinear system","authors":"Hammad Zaki, Aamir Rashid, Usman Masud","doi":"10.1177/01423312241262067","DOIUrl":"https://doi.org/10.1177/01423312241262067","url":null,"abstract":"Coupled nonlinear systems are difficult to control due to the adverse effects of uncertainties and coupling effects with increased sensor noise. This paper proposes an improved Lyapunov-based composite controller consisting of fractional-order proportional–integral–derivative (FOPID) and velocity-based disturbance observer to deal with the motion control of uncertain, nonlinear, and coupled system. FOPID utilizes the stable filtered error to facilitate the control development and stability analysis for the multi-input multi-output (MIMO) coupled system. Moreover, a disturbance observer is developed by utilizing the velocity signals to provide robustness against the disturbances and parametric uncertainties. Enhanced infinite order disturbance observer (EIFDOB) structure is used to improve the robustness of the introduced technique despite the high-frequency sensor noise. Stability analysis is provided to verify the introduced controller through the Lyapunov stability theorem, LaSalle’s invariance principle, and Barbalat’s lemma. Signal chasing is also presented to show that all signals are ultimately bounded. Comprehensive numerical simulations are performed on high-fidelity and coupled nonlinear model of the twin rotor MIMO system where the efficiency of the presented technique is examined against the external disturbances, matched uncertainties, and sensor noise. The results presented with different scenarios show that the proposed technique performed better with more robustness than FOPID and integer order proportional–integral–derivative.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"10 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141929071","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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