{"title":"Decoherence time control by interconnection for finite-level quantum memory systems","authors":"Igor G. Vladimirov, Ian R. Petersen","doi":"10.1016/j.ejcon.2024.101054","DOIUrl":"10.1016/j.ejcon.2024.101054","url":null,"abstract":"<div><div>This paper is concerned with open quantum systems whose dynamic variables have an algebraic structure, similar to that of the Pauli matrices for finite-level systems. The Hamiltonian and the operators of coupling of the system to the external bosonic fields depend linearly on the system variables. The fields are represented by quantum Wiener processes which drive the system dynamics according to a quasilinear Hudson–Parthasarathy quantum stochastic differential equation whose drift vector and dispersion matrix are affine and linear functions of the system variables. This setting includes the zero-Hamiltonian isolated system dynamics as a particular case, where the system variables are constant in time, which makes them potentially applicable as a quantum memory. In a more realistic case of nonvanishing system-field coupling, we define a memory decoherence time when a mean-square deviation of the system variables from their initial values becomes relatively significant as specified by a weighting matrix and a fidelity parameter. We consider the decoherence time maximization over the energy parameters of the system and obtain a condition under which the zero Hamiltonian provides a suboptimal solution. This optimization problem is also discussed for a direct energy coupling interconnection of such systems.</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"80 ","pages":"Article 101054"},"PeriodicalIF":2.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141510838","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}
{"title":"Observer design for visual-inertial estimation of pose, linear velocity and gravity direction in planar environments","authors":"Tarek Bouazza , Tarek Hamel , Claude Samson","doi":"10.1016/j.ejcon.2024.101067","DOIUrl":"10.1016/j.ejcon.2024.101067","url":null,"abstract":"<div><div>Vision-aided inertial navigation<span> systems combine data from a camera and an IMU to estimate the position, orientation, and linear velocity of a moving vehicle. In planar environments, existing methods assume knowledge of the vertical direction and ground plane to exploit accelerometer measurements. This paper presents a new solution that extends the estimation to arbitrary planar environments. A deterministic Riccati observer is designed to estimate the direction of gravity along with the vehicle pose, linear velocity, and the normal direction to the plane by fusing bearing correspondences from an image sequence with angular velocity and linear acceleration data. Comprehensive observability and stability analysis establishes an explicit persistent excitation condition under which local exponential stability of the observer is achieved. Simulation and real-world experimental results illustrate the performance and robustness of the proposed approach.</span></div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"80 ","pages":"Article 101067"},"PeriodicalIF":2.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141577079","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}
{"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}
Pieter van Goor, Punjaya Wickramasinghe, Matthew Hampsey, Robert Mahony
{"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}
{"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}
Ran Chen , Amber Srivastava , Mingzhou Yin , Roy S. Smith
{"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}
Alexander Kyuroson, Avijit Banerjee, Nektarios Aristeidis Tafanidis, Sumeet Satpute, George Nikolakopoulos
{"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}
Qingkai Meng , Andreas Kasis , Hao Yang , Marios M. Polycarpou
{"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}
{"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}
Ningsheng Xu , Fang Deng , Weiwen Huang , Li Liang , Xiang Shi
{"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}