Pub Date : 2024-11-01DOI: 10.1016/j.ejcon.2024.101068
David Ohlin, Emma Tegling, Anders Rantzer
We present a method for optimal control with respect to a linear cost function for positive linear systems with coupled input constraints. We show that the Bellman equation giving the optimal cost function and resulting sparse state feedback for these systems can be stated explicitly, with the solution given by a linear program. Our framework admits a range of network routing problems with underlying linear dynamics. These dynamics can be used to model traditional graph-theoretical problems like shortest path as a special case, but can also capture more complex behaviors. We provide an asynchronous and distributed value iteration algorithm for obtaining the optimal cost function and control law.
{"title":"Optimal control of linear cost networks","authors":"David Ohlin, Emma Tegling, Anders Rantzer","doi":"10.1016/j.ejcon.2024.101068","DOIUrl":"10.1016/j.ejcon.2024.101068","url":null,"abstract":"<div><div>We present a method for optimal control with respect to a linear cost function for positive linear systems with coupled input constraints. We show that the Bellman equation giving the optimal cost function and resulting sparse state feedback for these systems can be stated explicitly, with the solution given by a linear program. Our framework admits a range of network routing problems with underlying linear dynamics. These dynamics can be used to model traditional graph-theoretical problems like shortest path as a special case, but can also capture more complex behaviors. We provide an asynchronous and distributed value iteration algorithm for obtaining the optimal cost function and control law.</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"80 ","pages":"Article 101068"},"PeriodicalIF":2.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141510764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01DOI: 10.1016/j.ejcon.2024.101047
Pieter van Goor, Punjaya Wickramasinghe, Matthew Hampsey, Robert Mahony
Inertial Navigation Systems (INS) estimate a vehicle’s navigation states (attitude, velocity, and position) by combining measurements from an Inertial Measurement Unit (IMU) with other supporting sensors, typically including a Global Navigation Satellite System (GNSS) and a magnetometer. Recent nonlinear observer designs for INS provide powerful stability guarantees but do not account for some of the real-world challenges of INS. One of the key challenges is to account for the time-delay characteristic of GNSS measurements. This paper addresses this question by extending recent work on synchronous observer design for INS. The delayed GNSS measurements are related to the state at the current time using recursively-computable delay matrices, and this is used to design correction terms that leads to almost-globally asymptotic and locally exponential stability of the error. Simulation results verify the proposed observer and show that the compensation of time-delay is key to both transient and steady-state performance.
惯性导航系统（INS）通过将惯性测量单元（IMU）的测量数据与其他支持传感器（通常包括全球导航卫星系统（GNSS）和磁力计）的测量数据相结合来估计车辆的导航状态（姿态、速度和位置）。最近针对 INS 的非线性观测器设计提供了强大的稳定性保证，但没有考虑到 INS 在现实世界中面临的一些挑战。其中一个主要挑战是如何考虑 GNSS 测量的时延特性。本文通过扩展最近有关 INS 同步观测器设计的工作来解决这一问题。利用可递归计算的延迟矩阵，将延迟的 GNSS 测量与当前时间的状态联系起来，并以此设计校正项，从而实现误差的几乎全局渐近稳定性和局部指数稳定性。仿真结果验证了所提出的观测器，并表明时间延迟补偿是瞬态和稳态性能的关键。
{"title":"Constructive synchronous observer design for inertial navigation with delayed GNSS measurements","authors":"Pieter van Goor, Punjaya Wickramasinghe, Matthew Hampsey, Robert Mahony","doi":"10.1016/j.ejcon.2024.101047","DOIUrl":"10.1016/j.ejcon.2024.101047","url":null,"abstract":"<div><div>Inertial Navigation Systems (INS) estimate a vehicle’s navigation states (attitude, velocity, and position) by combining measurements from an Inertial Measurement Unit (IMU) with other supporting sensors, typically including a Global Navigation Satellite System (GNSS) and a magnetometer. Recent nonlinear observer designs for INS provide powerful stability guarantees but do not account for some of the real-world challenges of INS. One of the key challenges is to account for the time-delay characteristic of GNSS measurements. This paper addresses this question by extending recent work on synchronous observer design for INS. The delayed GNSS measurements are related to the state at the current time using recursively-computable delay matrices, and this is used to design correction terms that leads to almost-globally asymptotic and locally exponential stability of the error. Simulation results verify the proposed observer and show that the compensation of time-delay is key to both transient and steady-state performance.</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"80 ","pages":"Article 101047"},"PeriodicalIF":2.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141510832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01DOI: 10.1016/j.ejcon.2024.101048
Saket Adhau , Sébastien Gros , Sigurd Skogestad
This paper presents an end-to-end learning approach to developing a Nonlinear Model Predictive Control (NMPC) policy, which does not require an explicit first-principles model and assumes that the system dynamics are either unknown or partially known. The paper proposes the use of available measurements to identify a nominal Recurrent Neural Network (RNN) model to capture the nonlinear dynamics, which includes constraints on the state variables and inputs. To address the issue of suboptimal control policies resulting from simply fitting the model to the data, this paper uses Reinforcement learning (RL) to tune the NMPC scheme and generate an optimal policy for the real system. The approach’s novelty lies in the use of RL to overcome the limitations of the nominal RNN model and generate a more accurate control policy. The paper discusses the implementation aspects of initial state estimation for RNN models and integration of neural models in MPC. The presented method is demonstrated on a classic benchmark control problem: cascaded two tank system (CTS).
{"title":"Reinforcement learning based MPC with neural dynamical models","authors":"Saket Adhau , Sébastien Gros , Sigurd Skogestad","doi":"10.1016/j.ejcon.2024.101048","DOIUrl":"10.1016/j.ejcon.2024.101048","url":null,"abstract":"<div><div>This paper presents an end-to-end learning approach to developing a Nonlinear Model Predictive Control (NMPC) policy, which does not require an explicit first-principles model and assumes that the system dynamics are either unknown or partially known. The paper proposes the use of available measurements to identify a nominal Recurrent Neural Network (RNN) model to capture the nonlinear dynamics, which includes constraints on the state variables and inputs. To address the issue of suboptimal control policies resulting from simply fitting the model to the data, this paper uses Reinforcement learning (RL) to tune the NMPC scheme and generate an optimal policy for the real system. The approach’s novelty lies in the use of RL to overcome the limitations of the nominal RNN model and generate a more accurate control policy. The paper discusses the implementation aspects of initial state estimation for RNN models and integration of neural models in MPC. The presented method is demonstrated on a classic benchmark control problem: cascaded two tank system (CTS).</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"80 ","pages":"Article 101048"},"PeriodicalIF":2.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141510834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01DOI: 10.1016/j.ejcon.2024.101089
Ran Chen , Amber Srivastava , Mingzhou Yin , Roy S. Smith
This paper introduces a dual input–output parameterization (dual IOP) for the identification of linear time-invariant systems from closed-loop data. It draws inspiration from the recent input–output parameterization developed to synthesize a stabilizing controller. The controller is parameterized in terms of closed-loop transfer functions, from the external disturbances to the input and output of the system, constrained to lie in a given subspace. Analogously, the dual IOP method parameterizes the unknown plant with analogous closed-loop transfer functions, also referred to as dual parameters. In this case, these closed-loop transfer functions are constrained to lie in an affine subspace guaranteeing that the identified plant is stabilized by the known controller. Compared with existing closed-loop identification techniques guaranteeing closed-loop stability, such as the dual Youla parameterization, the dual IOP requires neither a doubly-coprime factorization of the controller nor a nominal plant that is stabilized by the controller. The dual IOP does not depend on the order and the state-space realization of the controller either, as in the dual system-level parameterization. Simulation shows that the dual IOP outperforms the existing benchmark methods.
{"title":"Closed-loop identification of stabilized models using dual input–output parameterization","authors":"Ran Chen , Amber Srivastava , Mingzhou Yin , Roy S. Smith","doi":"10.1016/j.ejcon.2024.101089","DOIUrl":"10.1016/j.ejcon.2024.101089","url":null,"abstract":"<div><div>This paper introduces a dual input–output parameterization (dual IOP) for the identification of linear time-invariant systems from closed-loop data. It draws inspiration from the recent input–output parameterization developed to synthesize a stabilizing controller. The controller is parameterized in terms of closed-loop transfer functions, from the external disturbances to the input and output of the system, constrained to lie in a given subspace. Analogously, the dual IOP method parameterizes the unknown plant with analogous closed-loop transfer functions, also referred to as dual parameters. In this case, these closed-loop transfer functions are constrained to lie in an affine subspace guaranteeing that the identified plant is <em>stabilized</em> by the known controller. Compared with existing closed-loop identification techniques guaranteeing closed-loop stability, such as the dual Youla parameterization, the dual IOP requires neither a doubly-coprime factorization of the controller nor a nominal plant that is stabilized by the controller. The dual IOP does not depend on the order and the state-space realization of the controller either, as in the dual system-level parameterization. Simulation shows that the dual IOP outperforms the existing benchmark methods.</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"80 ","pages":"Article 101089"},"PeriodicalIF":2.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141781381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01DOI: 10.1016/j.ejcon.2024.101052
Alexander Kyuroson, Avijit Banerjee, Nektarios Aristeidis Tafanidis, Sumeet Satpute, George Nikolakopoulos
The recent advances in space technology are focusing on fully autonomous, real-time, long-term orbit management and mission planning for large-scale satellite constellations in Low-Earth Orbit (LEO). Thus, a pioneering approach for autonomous orbital station-keeping has been introduced using a model-free Deep Policy Gradient-based Reinforcement Learning (DPGRL) strategy explicitly tailored for LEO. Addressing the critical need for more efficient and self-regulating orbit management in LEO satellite constellations, this work explores the potential synergy between Deep Reinforcement Learning (DRL) and Neuro-Evolution of Augmenting Topology (NEAT) to optimize station-keeping strategies with the primary goal to empower satellite to autonomously maintain their orbit in the presence of external perturbations within an allowable tolerance margin, thereby significantly reducing operational costs while maintaining precise and consistent station-keeping throughout their life cycle. The study specifically tailors DPGRL algorithms for LEO satellites, considering low-thrust constraints for maneuvers and integrating dense reward schemes and domain-based reward shaping techniques. By showcasing the adaptability and scalability of the combined NEAT and DRL framework in diverse operational scenarios, this approach holds immense promise for revolutionizing autonomous orbit management, paving the way for more efficient and adaptable satellite operations while incorporating the physical constraints of satellite, such as thruster limitations.
{"title":"Towards fully autonomous orbit management for low-earth orbit satellites based on neuro-evolutionary algorithms and deep reinforcement learning","authors":"Alexander Kyuroson, Avijit Banerjee, Nektarios Aristeidis Tafanidis, Sumeet Satpute, George Nikolakopoulos","doi":"10.1016/j.ejcon.2024.101052","DOIUrl":"10.1016/j.ejcon.2024.101052","url":null,"abstract":"<div><div>The recent advances in space technology are focusing on fully autonomous, real-time, long-term orbit management and mission planning for large-scale satellite constellations in Low-Earth Orbit (LEO). Thus, a pioneering approach for autonomous orbital station-keeping has been introduced using a model-free Deep Policy Gradient-based Reinforcement Learning (DPGRL) strategy explicitly tailored for LEO. Addressing the critical need for more efficient and self-regulating orbit management in LEO satellite constellations, this work explores the potential synergy between Deep Reinforcement Learning (DRL) and Neuro-Evolution of Augmenting Topology (NEAT) to optimize station-keeping strategies with the primary goal to empower satellite to autonomously maintain their orbit in the presence of external perturbations within an allowable tolerance margin, thereby significantly reducing operational costs while maintaining precise and consistent station-keeping throughout their life cycle. The study specifically tailors DPGRL algorithms for LEO satellites, considering low-thrust constraints for maneuvers and integrating dense reward schemes and domain-based reward shaping techniques. By showcasing the adaptability and scalability of the combined NEAT and DRL framework in diverse operational scenarios, this approach holds immense promise for revolutionizing autonomous orbit management, paving the way for more efficient and adaptable satellite operations while incorporating the physical constraints of satellite, such as thruster limitations.</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"80 ","pages":"Article 101052"},"PeriodicalIF":2.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141397533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01DOI: 10.1016/j.ejcon.2024.101037
Qingkai Meng , Andreas Kasis , Hao Yang , Marios M. Polycarpou
This paper studies the problem of secure state estimation of networked switched systems in the presence of denial-of-service (DoS) attacks, as well as disturbances and measurement noise. Firstly, a state transformation rule is designed to partition the original system into two subsystems, facilitating the design of discrete and continuous state observers. Secondly, by modifying the traditional super-twisting sliding-mode method and taking into account the frequency and duration characteristics of DoS attacks, we employ dynamic differential properties between different modes to design a switching law identification strategy. We show that this strategy can accurately estimate the switching state without imposing any requirement on the switching times and sequences. Thirdly, based on the identified activated mode, a set of mode-dependent continuous state sliding-mode observers is designed, that achieves continuous state estimation in finite time. The practicality and applicability of the developed results are validated through numerical simulations.
本文研究了存在拒绝服务（DoS）攻击以及干扰和测量噪声的网络交换系统的安全状态估计问题。首先，本文设计了一种状态变换规则，将原始系统划分为两个子系统，从而方便设计离散和连续状态观测器。其次，通过修改传统的超扭曲滑动模式方法，并考虑到 DoS 攻击的频率和持续时间特征，我们利用不同模式之间的动态差分特性设计了一种切换规律识别策略。我们的研究表明，这种策略可以准确估计切换状态，而无需对切换时间和顺序提出任何要求。第三，基于识别出的激活模式，我们设计了一组与模式相关的连续状态滑动模式观测器，可在有限时间内实现连续状态估计。通过数值模拟验证了所开发成果的实用性和适用性。
{"title":"Secure state estimation of networked switched systems under denial-of-service attacks","authors":"Qingkai Meng , Andreas Kasis , Hao Yang , Marios M. Polycarpou","doi":"10.1016/j.ejcon.2024.101037","DOIUrl":"10.1016/j.ejcon.2024.101037","url":null,"abstract":"<div><div><span>This paper studies the problem of secure state estimation of networked </span>switched systems in the presence of denial-of-service (DoS) attacks, as well as disturbances and measurement noise. Firstly, a state transformation rule is designed to partition the original system into two subsystems, facilitating the design of discrete and continuous state observers. Secondly, by modifying the traditional super-twisting sliding-mode method and taking into account the frequency and duration characteristics of DoS attacks, we employ dynamic differential properties between different modes to design a switching law identification strategy. We show that this strategy can accurately estimate the switching state without imposing any requirement on the switching times and sequences. Thirdly, based on the identified activated mode, a set of mode-dependent continuous state sliding-mode observers is designed, that achieves continuous state estimation in finite time. The practicality and applicability of the developed results are validated through numerical simulations.</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"80 ","pages":"Article 101037"},"PeriodicalIF":2.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141510835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01DOI: 10.1016/j.ejcon.2024.101128
Torbjørn Smith, Olav Egeland
A method for learning Hamiltonian dynamics from a limited and noisy dataset is proposed. The method learns a Hamiltonian vector field on a reproducing kernel Hilbert space (RKHS) of inherently Hamiltonian vector fields, and in particular, odd Hamiltonian vector fields. This is done with a symplectic kernel, and it is shown how the kernel can be modified to an odd symplectic kernel to impose the odd symmetry. A random feature approximation is developed for the proposed odd kernel to reduce the problem size. The performance of the method is validated in simulations for three Hamiltonian systems. It is demonstrated that the use of an odd symplectic kernel improves prediction accuracy and data efficiency, and that the learned vector fields are Hamiltonian and exhibit the imposed odd symmetry characteristics.
{"title":"Learning Hamiltonian dynamics with reproducing kernel Hilbert spaces and random features","authors":"Torbjørn Smith, Olav Egeland","doi":"10.1016/j.ejcon.2024.101128","DOIUrl":"10.1016/j.ejcon.2024.101128","url":null,"abstract":"<div><div>A method for learning Hamiltonian dynamics from a limited and noisy dataset is proposed. The method learns a Hamiltonian vector field on a reproducing kernel Hilbert space (RKHS) of inherently Hamiltonian vector fields, and in particular, odd Hamiltonian vector fields. This is done with a symplectic kernel, and it is shown how the kernel can be modified to an odd symplectic kernel to impose the odd symmetry. A random feature approximation is developed for the proposed odd kernel to reduce the problem size. The performance of the method is validated in simulations for three Hamiltonian systems. It is demonstrated that the use of an odd symplectic kernel improves prediction accuracy and data efficiency, and that the learned vector fields are Hamiltonian and exhibit the imposed odd symmetry characteristics.</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"80 ","pages":"Article 101128"},"PeriodicalIF":2.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01DOI: 10.1016/j.ejcon.2024.101130
Ningsheng Xu , Fang Deng , Weiwen Huang , Li Liang , Xiang Shi
This paper investigates a pursuit-evasion game with multiple evaders and a superior pursuer situated on a two-dimensional plane, divided by two lifelines into the play area and goal areas. The goal of the evaders is to reach one of the two goal areas, while the pursuer aims to capture them before they reach the lifeline. This paper constructs barriers without time delay and with time delay, respectively. For each evader, the entire barrier divides the game area into regions of dominance for the evader and pursuer, respectively. Cooperative and non-cooperative strategies between two evaders are studied when the evaders’ positions are within the pursuer’s dominance region. We consider the impact of different strategies adopted by the evaders and variations in the distance between the two lifelines on the number of captures by the pursuer. Furthermore, Apollonius circles and Cartesian ovals are used to determine optimal target points for the evaders under different circumstances. Subsequently, we extend the cooperative strategies of the evaders to multiplayer cooperative games, transform them into optimization problems, and use optimization algorithm to derive the cooperative strategies of multiple evaders. Finally, numerical simulations for various cases are presented in this paper.
{"title":"One superior pursuer and multiple-evader differential games with two lifelines","authors":"Ningsheng Xu , Fang Deng , Weiwen Huang , Li Liang , Xiang Shi","doi":"10.1016/j.ejcon.2024.101130","DOIUrl":"10.1016/j.ejcon.2024.101130","url":null,"abstract":"<div><div>This paper investigates a pursuit-evasion game with multiple evaders and a superior pursuer situated on a two-dimensional plane, divided by two lifelines into the play area and goal areas. The goal of the evaders is to reach one of the two goal areas, while the pursuer aims to capture them before they reach the lifeline. This paper constructs barriers without time delay and with time delay, respectively. For each evader, the entire barrier divides the game area into regions of dominance for the evader and pursuer, respectively. Cooperative and non-cooperative strategies between two evaders are studied when the evaders’ positions are within the pursuer’s dominance region. We consider the impact of different strategies adopted by the evaders and variations in the distance between the two lifelines on the number of captures by the pursuer. Furthermore, Apollonius circles and Cartesian ovals are used to determine optimal target points for the evaders under different circumstances. Subsequently, we extend the cooperative strategies of the evaders to multiplayer cooperative games, transform them into optimization problems, and use optimization algorithm to derive the cooperative strategies of multiple evaders. Finally, numerical simulations for various cases are presented in this paper.</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"80 ","pages":"Article 101130"},"PeriodicalIF":2.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01DOI: 10.1016/j.ejcon.2024.101033
Max van Haren , Kentaro Tsurumoto , Masahiro Mae , Lennart Blanken , Wataru Ohnishi , Tom Oomen
Iterative learning control (ILC) techniques are capable of improving the tracking performance of control systems that repeatedly perform similar tasks by utilizing data from past iterations. The aim of this paper is to achieve both the task flexibility enabled by ILC with basis functions and the performance of frequency-domain ILC, with an intuitive design procedure. The cost function of norm-optimal ILC is determined that recovers frequency-domain ILC, and consequently, the feedforward signal is parameterized in terms of basis functions and frequency-domain ILC. The resulting method has the performance and design procedure of frequency-domain ILC and the task flexibility of basis functions ILC, and are complimentary to each other. Validation on a benchmark example confirms the capabilities of the framework.
{"title":"A frequency-domain approach for enhanced performance and task flexibility in finite-time ILC","authors":"Max van Haren , Kentaro Tsurumoto , Masahiro Mae , Lennart Blanken , Wataru Ohnishi , Tom Oomen","doi":"10.1016/j.ejcon.2024.101033","DOIUrl":"10.1016/j.ejcon.2024.101033","url":null,"abstract":"<div><div>Iterative learning control (ILC) techniques are capable of improving the tracking performance of control systems that repeatedly perform similar tasks by utilizing data from past iterations. The aim of this paper is to achieve both the task flexibility enabled by ILC with basis functions and the performance of frequency-domain ILC, with an intuitive design procedure. The cost function of norm-optimal ILC is determined that recovers frequency-domain ILC, and consequently, the feedforward signal is parameterized in terms of basis functions and frequency-domain ILC. The resulting method has the performance and design procedure of frequency-domain ILC and the task flexibility of basis functions ILC, and are complimentary to each other. Validation on a benchmark example confirms the capabilities of the framework.</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"80 ","pages":"Article 101033"},"PeriodicalIF":2.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141510831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01DOI: 10.1016/j.ejcon.2024.101049
Felix Agner , Jonas Hansson , Pauline Kergus , Anders Rantzer , Sophie Tarbouriech , Luca Zaccarian
We consider control of multiple stable first-order agents which have a control coupling described by an M-matrix. These agents are subject to incremental sector-bounded input nonlinearities. We show that such plants can be globally asymptotically stabilized to a unique equilibrium using fully decentralized proportional–integral controllers equipped with anti-windup and subject to local tuning rules. In addition, we show that when the nonlinearities correspond to the saturation function, the closed loop asymptotically minimizes a weighted 1-norm of the agents state mismatch. The control strategy is finally compared to other state-of-the-art controllers on a numerical district heating example.
我们考虑对多个稳定的一阶代理进行控制，这些代理具有由 M 矩阵描述的控制耦合。这些代理受增量部门有界输入非线性的影响。我们的研究表明，使用配备防倒退功能的全分散比例积分控制器，并根据局部调整规则，可以将这些植物全局渐近地稳定在一个唯一的平衡点上。此外，我们还证明，当非线性与饱和函数相对应时，闭环可近似最小化代理状态失配的加权 1 正态。最后，我们在一个区域供热数值示例中将该控制策略与其他最先进的控制器进行了比较。
{"title":"Decentralized PI-control and anti-windup in resource sharing networks","authors":"Felix Agner , Jonas Hansson , Pauline Kergus , Anders Rantzer , Sophie Tarbouriech , Luca Zaccarian","doi":"10.1016/j.ejcon.2024.101049","DOIUrl":"10.1016/j.ejcon.2024.101049","url":null,"abstract":"<div><div>We consider control of multiple stable first-order agents which have a control coupling described by an M-matrix. These agents are subject to incremental sector-bounded input nonlinearities. We show that such plants can be globally asymptotically stabilized to a unique equilibrium using fully decentralized proportional–integral controllers equipped with anti-windup and subject to local tuning rules. In addition, we show that when the nonlinearities correspond to the saturation function, the closed loop asymptotically minimizes a weighted 1-norm of the agents state mismatch. The control strategy is finally compared to other state-of-the-art controllers on a numerical district heating example.</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"80 ","pages":"Article 101049"},"PeriodicalIF":2.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141406400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
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