Pub Date : 2024-07-04DOI: 10.1016/j.isatra.2024.06.023
The electro-pneumatic braking system with ON/OFF solenoid valves has been widely used in trains due to its advantages and superiority. The undesirable impact of the thermal effect on the electro-pneumatic braking system leads to frequent valve switching, degradation of the pressure tracking performance and sometimes instability. This article presents an adaptive model predictive control approach to solve the pressure control problem under temperature uncertainty based on a switched unscented Kalman filter. First, a nonlinear switched dynamical model with the uncertain temperature parameter is derived for the electro-pneumatic braking system by comprehensively integrating its nonlinear, discontinuous dynamics and thermal effect. Using a switched unscented Kalman filter on the presented model of the system, the temperature parameter is accurately estimated to improve the model’s accuracy. Based on the corrected system model and the designed adaptive model predictive control method, the pressure tracking performance and the valves’ switchings of the electro-pneumatic braking system are improved, and the stability is guaranteed. The simulations and the experiments conducted for a braking system prototype confirm the performance validity of the proposed method.
{"title":"Online parameter identification based predictive pressure control for train electro-pneumatic braking systems with thermal effect","authors":"","doi":"10.1016/j.isatra.2024.06.023","DOIUrl":"10.1016/j.isatra.2024.06.023","url":null,"abstract":"<div><p><span><span>The electro-pneumatic braking system with ON/OFF solenoid valves has been widely used in trains due to its advantages and superiority. The undesirable impact of the thermal effect on the electro-pneumatic braking system leads to frequent valve switching, degradation of the pressure tracking performance and sometimes instability. This article presents an adaptive </span>model predictive control approach to solve the pressure control problem under temperature uncertainty based on a switched unscented </span>Kalman filter<span><span>. First, a nonlinear switched dynamical model with the uncertain temperature parameter is derived for the electro-pneumatic braking system by comprehensively integrating its nonlinear, discontinuous dynamics and thermal effect. Using a switched unscented Kalman filter on the presented model of the system, the temperature parameter is accurately estimated to improve the model’s accuracy. Based on the corrected system model and the designed adaptive </span>model predictive control method, the pressure tracking performance and the valves’ switchings of the electro-pneumatic braking system are improved, and the stability is guaranteed. The simulations and the experiments conducted for a braking system prototype confirm the performance validity of the proposed method.</span></p></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141592379","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 : 2024-07-03DOI: 10.1016/j.isatra.2024.06.032
This article delves into the intricate challenge of implementing prescribed-time command filtered control in the context of uncertain nonlinear systems. Firstly, a prescribed-time function is defined to lay the groundwork for subsequent controller design. Subsequently, a novel prescribed-time command Filtered controller is proposed for high-order nonlinear systems featuring unknown parameters. This controller guarantees swift error convergence within a predefined time range, with the added capability of periodic error convergence to zero during subsequent controller operations. A pivotal innovation in this study lies in the controller’s design, which remains unaffected by the system’s initial conditions. This unique feature enables the prescribed time to be flexibly set within physical constraints, diverging markedly from conventional finite-time control theory. Theoretical analysis has conclusively shown that the controller achieves full-state tracking error convergence within the specified time frame. The efficacy of the research findings is substantiated through two simulation cases, underscoring a substantial contribution to the refinement and adaptability of nonlinear system control theory.
{"title":"Prescribed-time command filtered control for a class uncertain nonlinear systems","authors":"","doi":"10.1016/j.isatra.2024.06.032","DOIUrl":"10.1016/j.isatra.2024.06.032","url":null,"abstract":"<div><p><span>This article delves into the intricate challenge of implementing prescribed-time command filtered control in the context of uncertain nonlinear systems<span><span>. Firstly, a prescribed-time function is defined to lay the groundwork for subsequent controller design. Subsequently, a novel prescribed-time command Filtered controller is proposed for high-order </span>nonlinear systems featuring unknown parameters. This controller guarantees swift error convergence within a predefined time range, with the added capability of periodic error convergence to zero during subsequent controller operations. A pivotal innovation in this study lies in the controller’s design, which remains unaffected by the system’s initial conditions. This unique feature enables the prescribed time to be flexibly set within physical constraints, diverging markedly from conventional finite-time control theory. Theoretical analysis has conclusively shown that the controller achieves full-state tracking error convergence within the specified time frame. The efficacy of the research findings is substantiated through two simulation cases, underscoring a substantial contribution to the refinement and adaptability of </span></span>nonlinear system control theory.</p></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141556262","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 : 2024-07-03DOI: 10.1016/j.isatra.2024.06.033
This paper proposes a differential privacy decentralized zeroth-order gradient tracking optimization (DP-DZOGT) algorithm for solving optimization problems of decentralized systems, where the gradient information of the function is unknown. To address the challenge of unknown gradient information, a one-point zeroth-order gradient estimator (OPZOGE) is constructed, which can estimate the gradient based on the function value and guide the update of decision variables. Additionally, to prevent privacy leakage of agents, random noise is introduced into both the state and the gradient of the agents, which effectively enhances the level of privacy protection. The linear convergence of the proposed DP-DZOGT under a fixed step size can be guaranteed. Moreover, it has been applied to the fields of smart grid (SG) and decentralized federated learning (DFL). Finally, the effectiveness of the algorithm is validated through three numerical simulations.
{"title":"Zeroth-order gradient tracking for decentralized learning with privacy guarantees","authors":"","doi":"10.1016/j.isatra.2024.06.033","DOIUrl":"10.1016/j.isatra.2024.06.033","url":null,"abstract":"<div><p><span>This paper proposes a differential privacy decentralized zeroth-order gradient tracking optimization (DP-DZOGT) algorithm for solving </span>optimization problems<span><span><span> of decentralized systems, where the gradient information<span> of the function is unknown. To address the challenge of unknown gradient information, a one-point zeroth-order gradient estimator (OPZOGE) is constructed, which can estimate the gradient based on the function value and guide the update of decision variables. Additionally, to prevent </span></span>privacy leakage of agents, random noise is introduced into both the state and the gradient of the agents, which effectively enhances the level of privacy protection. The linear convergence of the proposed DP-DZOGT under a fixed step size can be guaranteed. Moreover, it has been applied to the fields of smart grid (SG) and decentralized </span>federated learning (DFL). Finally, the effectiveness of the algorithm is validated through three numerical simulations.</span></p></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141702508","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 : 2024-07-03DOI: 10.1016/j.isatra.2024.07.002
This paper proposes a novel fault-tolerant control (FTC) scheme for real-time uncertainty estimation in nonlinear systems. It addresses the challenges arising from nonlinear dynamics in system inputs, states, and outputs, along with measurement uncertainties, within an output feedback framework. Our approach leverages two key components: 1) A neural network NN descriptor-based observer: this novel observer concurrently estimates both system states and sensor uncertainties. It is particularly capable of handling unbounded sensor uncertainties in specific situations. It utilizes NNs as universal approximators to capture the system's complex nonlinearities. 2) A robust model reference tracking controller: this controller employs the estimated states from the NN descriptor-based observer to achieve the desired system performance despite the existence of uncertainties. It exhibits robustness, guaranteeing system stability and asymptotic state tracking to a given reference model. The efficacy of the proposed FTC scheme is validated through theoretical analysis and its application to two real-world case studies.
本文针对非线性系统中的实时不确定性估计提出了一种新型容错控制(FTC)方案。该方案在输出反馈框架内解决了系统输入、状态和输出的非线性动态以及测量不确定性带来的挑战。我们的方法利用了两个关键组件:1) 基于神经网络 NN 描述符的观测器:这种新型观测器可同时估计系统状态和传感器的不确定性。在特定情况下,它尤其能够处理无限制的传感器不确定性。它利用 NN 作为通用近似值来捕捉系统的复杂非线性。2) 稳健的模型参考跟踪控制器:尽管存在不确定性,该控制器仍能利用基于 NN 描述符的观测器所估计的状态来实现理想的系统性能。它具有鲁棒性,能保证系统的稳定性和对给定参考模型的渐近状态跟踪。通过理论分析及其在两个实际案例研究中的应用,验证了所提出的 FTC 方案的有效性。
{"title":"Neural network-based adaptive fault-tolerant control for nonlinear systems with uncertainties","authors":"","doi":"10.1016/j.isatra.2024.07.002","DOIUrl":"10.1016/j.isatra.2024.07.002","url":null,"abstract":"<div><p><span>This paper proposes a novel fault-tolerant control (FTC) scheme for real-time uncertainty estimation in nonlinear systems<span><span>. It addresses the challenges arising from nonlinear dynamics in system inputs, states, and outputs, along with measurement uncertainties, within an output feedback framework. Our approach leverages two key components: 1) A </span>neural network NN descriptor-based observer: this novel observer concurrently estimates both system states and sensor uncertainties. It is particularly capable of handling unbounded sensor uncertainties in specific situations. It utilizes NNs as universal </span></span>approximators<span> to capture the system's complex nonlinearities. 2) A robust model reference tracking controller: this controller employs the estimated states from the NN descriptor-based observer to achieve the desired system performance<span> despite the existence of uncertainties. It exhibits robustness, guaranteeing system stability and asymptotic state tracking to a given reference model. The efficacy of the proposed FTC scheme is validated through theoretical analysis and its application to two real-world case studies.</span></span></p></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141539137","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 : 2024-07-02DOI: 10.1016/j.isatra.2024.06.027
In this paper, angle attitude control is investigated for a networked pneumatic muscle actuators system (NPMAS) with input quantization and disturbance. A hysteretic quantizer is presented to effectively avoid the problem of high frequency oscillation in the process of quantization. A novel prescribed-time nonlinear extended state observer (PTNESO) is designed to continuously observe states and lumped disturbances of NPMAS, which ensures that the observation error converges in prescribed time. An active disturbance rejection control (ADRC) method based on PTNESO is designed to compensate for the lumped disturbances and achieve accurate angle tracking. A sufficient condition of bounded stability for NPMAS is given by the Lyapunov method. Finally, comparative experiments are provided to verify the effectiveness of the proposed control method.
{"title":"Angle attitude control for a networked pneumatic muscle actuators system with input quantization: A prescribed-time nonlinear ESO approach","authors":"","doi":"10.1016/j.isatra.2024.06.027","DOIUrl":"10.1016/j.isatra.2024.06.027","url":null,"abstract":"<div><p><span>In this paper, angle attitude control is investigated for a networked pneumatic muscle actuators system<span><span><span> (NPMAS) with input quantization and disturbance. A hysteretic quantizer is presented to effectively avoid the problem of high frequency oscillation in the process of quantization. A novel prescribed-time nonlinear extended state observer (PTNESO) is designed to continuously observe states and lumped disturbances of NPMAS, which ensures that the observation error converges in prescribed time. An </span>active disturbance rejection control (ADRC) method based on PTNESO is designed to compensate for the lumped disturbances and achieve accurate angle tracking. A </span>sufficient condition of bounded stability for NPMAS is given by the </span></span>Lyapunov method<span>. Finally, comparative experiments are provided to verify the effectiveness of the proposed control method.</span></p></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141556259","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 : 2024-07-02DOI: 10.1016/j.isatra.2024.06.028
To solve some scheduling problems of batch processes based on timed Petri net models, timed extended reachability graphs (TERGs) and approximated TERGs can be used. Such graphs abstract temporal specifications and represent parts of timed languages. By exploring the feasible trajectories in a TERG, optimal schedules can be obtained with respect to the makespans of batch processes that are modeled by timed Petri nets. Nevertheless, the rapid growth of the number of states in a TERG makes the problem intractable for large systems. In this paper, we improve the existing clustering TERG approach, and we make it suitable for large sized batch processes. We also enlarge a systematic approach to model batch processes with timed Petri nets. Finally, a comprehensive example of scheduling problem is studied for an archetypal chemical production plant in order to illustrate the efficiency of the proposed approach.
为了解决一些基于定时 Petri 网模型的批处理调度问题,可以使用定时扩展可达性图(TERG)和近似 TERG。这类图抽象了时间规范,代表了定时语言的一部分。通过探索 TERG 中的可行轨迹,可以获得与定时 Petri 网建模的批处理过程的时间跨度相关的最优时间表。然而,TERG 中状态数量的快速增长使得该问题在大型系统中难以解决。在本文中,我们改进了现有的聚类 TERG 方法,使其适用于大型批处理流程。我们还扩大了用定时 Petri 网为批处理建模的系统方法。最后,我们研究了一个典型化工生产厂的综合调度问题实例,以说明所提方法的效率。
{"title":"Scheduling for batch processes based on clustering approximated timed reachability graphs","authors":"","doi":"10.1016/j.isatra.2024.06.028","DOIUrl":"10.1016/j.isatra.2024.06.028","url":null,"abstract":"<div><p>To solve some scheduling problems of batch processes based on timed Petri net models, timed extended reachability graphs (TERGs) and approximated TERGs can be used. Such graphs abstract temporal specifications and represent parts of timed languages. By exploring the feasible trajectories in a TERG, optimal schedules can be obtained with respect to the makespans of batch processes that are modeled by timed Petri nets. Nevertheless, the rapid growth of the number of states in a TERG makes the problem intractable for large systems. In this paper, we improve the existing clustering TERG approach, and we make it suitable for large sized batch processes. We also enlarge a systematic approach to model batch processes with timed Petri nets. Finally, a comprehensive example of scheduling problem is studied for an archetypal chemical production plant in order to illustrate the efficiency of the proposed approach.</p></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0019057824003161/pdfft?md5=2ee9b4ab42ef5d78e0de55fbd7209ad2&pid=1-s2.0-S0019057824003161-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141602359","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 : 2024-07-02DOI: 10.1016/j.isatra.2024.06.026
This work investigates the less conservative stability conditions for linear systems with a time-varying delay. At first, augmented Lyapunov–Krasovskii functionals(LKFs) are constructed with state vectors that have not been utilized in the existing works, and an augmented zero equality that can be derived according to the augmented vector is proposed. By utilizing them, a stability condition is proposed in the form of a linear matrix inequality. And, by using novel delay-dependent LKFs and the introduced ones, improved results are obtained than the previous result. The addition of the delay-dependent LKFs increases the number of decision variables in the results. Therefore, any vectors of integral inequalities utilized in the proposed criterion are appropriately adjusted to reduce computational complexity. To check the excellence and validity of the proposed results, several numerical examples are applied.
{"title":"Improvements on stability criteria for linear systems with a time-varying delay via novel delay-dependent Lyapunov functionals","authors":"","doi":"10.1016/j.isatra.2024.06.026","DOIUrl":"10.1016/j.isatra.2024.06.026","url":null,"abstract":"<div><p>This work investigates the less conservative stability conditions for linear systems with a time-varying delay. At first, augmented Lyapunov–Krasovskii functionals(LKFs) are constructed with state vectors that have not been utilized in the existing works, and an augmented zero equality that can be derived according to the augmented vector is proposed. By utilizing them, a stability condition is proposed in the form of a linear matrix inequality. And, by using novel delay-dependent LKFs and the introduced ones, improved results are obtained than the previous result. The addition of the delay-dependent LKFs increases the number of decision variables in the results. Therefore, any vectors of integral inequalities utilized in the proposed criterion are appropriately adjusted to reduce computational complexity. To check the excellence and validity of the proposed results, several numerical examples are applied.</p></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141556260","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 : 2024-06-30DOI: 10.1016/j.isatra.2024.06.024
In this study, an ultra-high-precision pneumatic force servo system (UPFSS) is proposed. On the one hand, a novel air-floating pneumatic cylinder (AFPC) with an air-floating piston capable of independent air supply and exhaust is developed for this system, and its special flow channel design allows the air-floating piston to be suspended in the cylinder without being constrained by the pressure in the chambers. The friction force of the AFPC is less than 0.0049 N. On the other hand, a leakage chamber is constructed to simulate the clearance between the air-floating piston and the cylinder wall, and a fuzzy proportional integral (FPI)-based pressure control system (PCS) is designed for the simulated leakage chamber. Furthermore, a novel particle swarm optimization algorithm integrating Gaussian mutation and fuzzy theory (IGF-PSO) is presented. After testing, the IGF-PSO algorithm is found to have outstanding optimization performance. Then, the parameters of the FPI controller are optimized through the IGFPSO algorithm. Experimental comparisons reveal that the steady-state error achieved by the parameter-optimized pressure controller in response to the leakage condition is about 38 % smaller than that achieved by the pressure controller with parameters obtained using the trial-and-error method. Finally, the UPFSS is tested by using the optimized PCS to supply compressed air to the chamber of the AFPC. The results show that the UPFSS achieves a steady-state error of no more than 0.0279 N in the continuous step response within the range of 240 N.
{"title":"Ultra-high-precision pneumatic force servo system based on a novel improved particle swarm optimization algorithm integrating Gaussian mutation and fuzzy theory","authors":"","doi":"10.1016/j.isatra.2024.06.024","DOIUrl":"10.1016/j.isatra.2024.06.024","url":null,"abstract":"<div><p><span>In this study, an ultra-high-precision pneumatic force servo system<span> (UPFSS) is proposed. On the one hand, a novel air-floating pneumatic cylinder<span> (AFPC) with an air-floating piston capable of independent air supply and exhaust is developed for this system, and its special flow channel design allows the air-floating piston to be suspended in the cylinder without being constrained by the pressure in the chambers. The friction force of the AFPC is less than 0.0049 N. On the other hand, a leakage chamber is constructed to simulate the clearance between the air-floating piston and the </span></span></span>cylinder wall<span><span><span>, and a fuzzy proportional integral (FPI)-based pressure control system (PCS) is designed for the simulated leakage chamber. Furthermore, a novel particle swarm optimization<span> algorithm integrating Gaussian mutation and fuzzy theory (IGF-PSO) is presented. After testing, the IGF-PSO algorithm is found to have outstanding optimization performance. Then, the parameters of the </span></span>FPI controller are optimized through the IGFPSO algorithm. Experimental comparisons reveal that the steady-state error achieved by the parameter-optimized pressure controller in response to the leakage condition is about 38 % smaller than that achieved by the pressure controller with parameters obtained using the trial-and-error method. Finally, the UPFSS is tested by using the optimized PCS to supply </span>compressed air<span> to the chamber of the AFPC. The results show that the UPFSS achieves a steady-state error of no more than 0.0279 N in the continuous step response within the range of 240 N.</span></span></p></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500001","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 : 2024-06-28DOI: 10.1016/j.isatra.2024.06.020
This paper presents an adaptive probabilistic recurrent Takagi-Sugeno-Kang fuzzy neural PID controller for handling the problems of uncertainties in nonlinear systems. The proposed controller combines probabilistic processing with a Takagi-Sugeno-Kang fuzzy neural system to proficiently address stochastic uncertainties in controlled systems. The stability of the controlled system is ensured through the utilization of Lyapunov function to adjust the controller parameters. By tuning the probability parameters of the controller design, an additional level of control is achieved, leading to enhance the controller performance. Furthermore, it can operate without relying on the system's mathematical model. The proposed control approach is employed in nonlinear dynamical plants and compared to other existing controllers to validate its applicability in engineering domains. Simulation and experimental investigations demonstrate that the proposed controller surpasses alternative controllers in effectively managing external disturbances, random noise, and a broad spectrum of system uncertainties.
{"title":"Real time adaptive probabilistic recurrent Takagi-Sugeno-Kang fuzzy neural network proportional-integral-derivative controller for nonlinear systems","authors":"","doi":"10.1016/j.isatra.2024.06.020","DOIUrl":"10.1016/j.isatra.2024.06.020","url":null,"abstract":"<div><p><span><span>This paper presents an adaptive probabilistic recurrent Takagi-Sugeno-Kang fuzzy neural PID controller for handling the problems of uncertainties in </span>nonlinear systems<span>. The proposed controller combines probabilistic processing with a Takagi-Sugeno-Kang fuzzy neural system to proficiently address stochastic uncertainties in controlled systems. The stability of the controlled system is ensured through the utilization of Lyapunov function<span> to adjust the controller parameters<span>. By tuning the probability parameters of the controller design, an additional level of control is achieved, leading to enhance the controller performance. Furthermore, it can operate without relying on the system's mathematical model. The proposed control approach is employed in nonlinear dynamical plants and compared to other existing controllers to validate its applicability in engineering domains. Simulation and experimental investigations demonstrate that the proposed controller surpasses alternative controllers in effectively managing </span></span></span></span>external disturbances, random noise, and a broad spectrum of system uncertainties.</p></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141473950","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 : 2024-06-28DOI: 10.1016/j.isatra.2024.06.016
The optimal leaderless and leader-following time-varying formation (TVF) control problems for second-order multiagent systems (MASs) are investigated, where two optimal TVF control protocols are proposed to achieve the desired formations as well as minimize the comprehensive optimization function that contain the cooperative performance index and the control energy index. For leaderless case, the optimal formation control problem is reformulated as an infinite-time state regulator problem by employing the state space decomposition method, which is subject to specified constraints on energy and performance indices, and the analytic criterion for optimal TVF achievability is subsequently proposed. Then, the results of optimal leaderless TVF control are extended to the leader-following case with switching topologies, where the main challenge is changed to find the optimal controller rather than the optimal gain matrix, and the optimal value of the comprehensive index is accurately determined. Finally, two simulation cases are proposed to validate the effectiveness of the theoretical results, and comparisons with previous works are presented to expound the optimality of the proposed formation control method.
{"title":"Optimal distributed time-varying formation control for second-order multiagent systems: LQR-based method","authors":"","doi":"10.1016/j.isatra.2024.06.016","DOIUrl":"10.1016/j.isatra.2024.06.016","url":null,"abstract":"<div><p><span><span>The optimal leaderless and leader-following time-varying formation (TVF) control problems for second-order multiagent systems (MASs) are investigated, where two optimal TVF control protocols are proposed to achieve the desired formations as well as minimize the comprehensive optimization function that contain the cooperative performance index and the control energy index. For leaderless case, the optimal formation control problem is reformulated as an infinite-time state regulator problem by employing the state space decomposition method, which is subject to specified constraints on energy and performance indices, and the analytic criterion for optimal TVF achievability is subsequently proposed. Then, the results of optimal leaderless TVF control are extended to the leader-following case with switching topologies, where the main challenge is changed to find the </span>optimal controller rather than the optimal </span>gain matrix<span>, and the optimal value of the comprehensive index is accurately determined. Finally, two simulation cases are proposed to validate the effectiveness of the theoretical results, and comparisons with previous works are presented to expound the optimality of the proposed formation control method.</span></p></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141556261","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}