European Journal of Control最新文献

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A comparative study of sensitivity computations in ESDIRK-based optimal control problems 基于 ESDIRK 的最优控制问题中灵敏度计算的比较研究

IF 2.5 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

European Journal of Control

Pub Date : 2024-11-01 DOI: 10.1016/j.ejcon.2024.101064

Anders Hilmar Damm Christensen, John Bagterp Jørgensen

This paper compares the impact of iterated and direct approaches to sensitivity computation in fixed step-size explicit singly diagonally implicit Runge–Kutta (ESDIRK) methods when applied to optimal control problems (OCPs). We strictly use the principle of internal numerical differentiation (IND) for the iterated approach, i.e., reusing iteration matrix factorizations, the number of Newton-type iterations, and Newton iterates, to compute the sensitivities. The direct method computes the sensitivities without using the Newton schemes. We compare the impact of these sensitivity computations in OCPs for the quadruple tank system (QTS). We discretize the OCPs using multiple shooting and solve these with a sequential quadratic programming (SQP) solver. We benchmark the iterated and direct approaches against a base case. This base case applies the ESDIRK methods with exact Newton schemes and a direct approach for sensitivity computations. In these OCPs, we vary the number of integration steps between control intervals and evaluate the performance based on the number of SQP and QPs iterations, KKT violations, function evaluations, Jacobian updates, and iteration matrix factorizations. We also provide examples using the continuous-stirred tank reactor (CSTR), and the IPOPT algorithm instead of the SQP. For OCPs solved using SQP, the QTS results show the direct method converges only once, while the iterated approach and base case converges in all situations. Similar results are seen with the CSTR. Using IPOPT, both the iterated approach and base case converge in all cases. In contrast, the direct method only converges in all cases regarding the CSTR.

本文比较了固定步长显式单对角隐式 Runge-Kutta (ESDIRK) 方法应用于最优控制问题 (OCP) 时，迭代法和直接法对灵敏度计算的影响。我们在迭代法中严格使用内部数值微分（IND）原理，即重复使用迭代矩阵因式分解、牛顿型迭代次数和牛顿迭代次数来计算灵敏度。直接法计算敏感度时不使用牛顿方案。我们比较了这些灵敏度计算对四水箱系统（QTS）OCP 的影响。我们使用多重射击对 OCP 进行离散化，并使用顺序二次编程 (SQP) 求解器进行求解。我们将迭代法和直接法与一个基本案例进行比较。该基础案例采用带有精确牛顿方案的 ESDIRK 方法和直接方法进行灵敏度计算。在这些 OCP 中，我们改变了控制区间之间的积分步数，并根据 SQP 和 QPs 的迭代次数、KKT 违反情况、函数评估、雅各布更新和迭代矩阵因式分解来评估性能。我们还提供了使用连续搅拌罐反应器（CSTR）和 IPOPT 算法代替 SQP 的示例。对于使用 SQP 求解的 OCP，QTS 结果显示直接方法只收敛一次，而迭代方法和基本情况在所有情况下都收敛。CSTR 也有类似的结果。使用 IPOPT 时，迭代法和基准法在所有情况下都收敛。相比之下，直接法只在 CSTR 的所有情况下收敛。

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引用次数: 0

Nonlinear model predictive control based on K-step control invariant sets 基于 K 步控制不变集的非线性模型预测控制

IF 2.5 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

European Journal of Control

Pub Date : 2024-11-01 DOI: 10.1016/j.ejcon.2024.101040

Zhixin Zhao, Antoine Girard, Sorin Olaru

One of the fundamental issues in Nonlinear Model Predictive Control (NMPC) is to be able to guarantee the recursive feasibility of the underlying receding horizon optimization. In other terms, the primary condition for a safe NMPC design is to ensure that the closed-loop solution remains indefinitely within the feasible set of the optimization problem. This issue can be addressed by introducing a terminal constraint described in terms of a control invariant set. However, the control invariant sets of nonlinear systems are often impractical to use or even to construct due to their complexity. The

K

-step control invariant sets are representing generalizations of the classical one-step control invariant sets and prove to retain the useful properties for MPC design, but often with simpler representations, and thus greater applicability. In this paper, a novel NMPC scheme based on

K

-step control invariant sets is proposed. We employ symbolic control techniques to compute a

K

-step control invariant set and build the NMPC framework by integrating this set as a terminal constraint, thereby ensuring recursive feasibility.

非线性模型预测控制（NMPC）的基本问题之一是能够保证基础后退视界优化的递归可行性。换句话说，安全 NMPC 设计的首要条件是确保闭环解决方案无限期地保持在优化问题的可行集内。这个问题可以通过引入以控制不变集描述的终端约束来解决。然而，非线性系统的控制不变集往往由于其复杂性而难以使用，甚至难以构建。K 步控制不变集是对经典的一步控制不变集的概括，并被证明保留了 MPC 设计的有用特性，但通常具有更简单的表示方法，因此具有更大的适用性。本文提出了一种基于 K 步控制不变集的新型 NMPC 方案。我们采用符号控制技术计算 K 步控制不变集，并通过将该不变集整合为终端约束来构建 NMPC 框架，从而确保递归可行性。

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引用次数: 0

Decoherence time control by interconnection for finite-level quantum memory systems 通过互联控制有限级量子存储器系统的退相干时间

IF 2.5 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

European Journal of Control

Pub Date : 2024-11-01 DOI: 10.1016/j.ejcon.2024.101054

Igor G. Vladimirov, Ian R. Petersen

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.

本文研究的是开放量子系统，其动态变量的代数结构类似于有限级系统的保利矩阵。系统的哈密顿和与外部玻色子场耦合的算子线性依赖于系统变量。这些场由量子维纳过程表示，量子维纳过程根据准线性哈德逊-帕塔萨拉蒂量子随机微分方程驱动系统动力学，该方程的漂移向量和分散矩阵是系统变量的仿射和线性函数。这种设置包括零-哈密尔顿孤立系统动力学，作为一种特殊情况，系统变量在时间上是恒定的，这使得它们有可能被用作量子存储器。在更现实的非消失系统-场耦合情况下，当系统变量与其初始值的均方偏差变得相对显著时，我们定义了记忆退相干时间，该时间由加权矩阵和保真度参数指定。我们考虑了退相干时间在系统能量参数上的最大化问题，并得出了零哈密顿提供次优解的条件。我们还讨论了此类系统直接能量耦合互连的优化问题。

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引用次数: 0

Observer design for visual-inertial estimation of pose, linear velocity and gravity direction in planar environments 平面环境中姿态、线速度和重力方向视觉惯性估算的观测器设计

IF 2.5 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

European Journal of Control

Pub Date : 2024-11-01 DOI: 10.1016/j.ejcon.2024.101067

Tarek Bouazza , Tarek Hamel , Claude Samson

Vision-aided inertial navigation 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.

视觉辅助惯性导航系统结合摄像头和 IMU 的数据来估计移动车辆的位置、方向和线速度。在平面环境中，现有方法假定需要了解垂直方向和地平面，才能利用加速度计测量数据。本文提出了一种新的解决方案，可将估算扩展到任意平面环境。本文设计了一个确定性里卡提观测器，通过将图像序列中的方位对应关系与角速度和线性加速度数据融合，在估算重力方向的同时估算车辆姿态、线速度和平面法线方向。全面的可观测性和稳定性分析确立了一个明确的持续激励条件，在此条件下，观测器可实现局部指数稳定性。仿真和实际实验结果表明了所提方法的性能和稳健性。

引用次数: 0

Optimal control of linear cost networks 线性成本网络的优化控制

IF 2.5 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

European Journal of Control

Pub Date : 2024-11-01 DOI: 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.

我们提出了一种针对具有耦合输入约束条件的正线性系统的线性成本函数的最优控制方法。我们证明，这些系统的贝尔曼方程给出了最优成本函数和由此产生的稀疏状态反馈，可以用线性规划给出解。我们的框架适用于一系列具有基本线性动力学的网络路由问题。这些动力学可以用来模拟传统的图论问题，如作为特例的最短路径，也可以捕捉更复杂的行为。我们提供了一种异步分布式值迭代算法，用于获得最佳成本函数和控制法则。

引用次数: 0

Constructive synchronous observer design for inertial navigation with delayed GNSS measurements 利用延迟全球导航卫星系统测量进行惯性导航的建设性同步观测器设计

IF 2.5 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

European Journal of Control

Pub Date : 2024-11-01 DOI: 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。

引用次数: 0

Reinforcement learning based MPC with neural dynamical models 基于神经动力学模型的强化学习 MPC

IF 2.5 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

European Journal of Control

Pub Date : 2024-11-01 DOI: 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).

本文提出了一种开发非线性模型预测控制（NMPC）策略的端到端学习方法，该方法不需要明确的第一原理模型，并假定系统动态是未知或部分已知的。本文建议利用现有的测量数据来确定一个名义递归神经网络 (RNN) 模型，以捕捉非线性动态，其中包括状态变量和输入的约束条件。为了解决简单地根据数据拟合模型所产生的次优控制策略问题，本文使用强化学习（RL）来调整 NMPC 方案，并为实际系统生成最优策略。这种方法的新颖之处在于利用 RL 克服了名义 RNN 模型的局限性，并生成了更精确的控制策略。论文讨论了 RNN 模型的初始状态估计和 MPC 中神经模型集成的实施问题。本文提出的方法在一个经典的基准控制问题上进行了演示：级联双油箱系统 (CTS)。

引用次数: 0

Closed-loop identification of stabilized models using dual input–output parameterization 利用双输入输出参数化对稳定模型进行闭环识别

IF 2.5 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

European Journal of Control

Pub Date : 2024-11-01 DOI: 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.

本文介绍了从闭环数据识别线性时变系统的双输入输出参数化（dual IOP）。它借鉴了最近为合成稳定控制器而开发的输入输出参数化方法。控制器是以闭环传递函数为参数的，从外部干扰到系统的输入和输出，都受限于一个给定的子空间。与此类似，对偶 IOP 方法也是通过类似的闭环传递函数（也称为对偶参数）对未知工厂进行参数化。在这种情况下，这些闭环传递函数受限于一个仿射子空间，从而保证识别出的工厂由已知控制器控制。与现有的保证闭环稳定性的闭环识别技术（如双 Youla 参数化）相比，双 IOP 既不需要控制器的双同调因子化，也不需要控制器稳定的标称工厂。双 IOP 也不像双系统级参数化那样取决于控制器的阶数和状态空间实现。仿真表明，双 IOP 优于现有的基准方法。

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引用次数: 0

Towards fully autonomous orbit management for low-earth orbit satellites based on neuro-evolutionary algorithms and deep reinforcement learning 基于神经进化算法和深度强化学习实现低地轨道卫星的完全自主轨道管理

IF 2.5 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

European Journal of Control

Pub Date : 2024-11-01 DOI: 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.

空间技术的最新进展主要集中在为低地轨道（LEO）上的大型卫星群提供完全自主、实时、长期的轨道管理和任务规划。因此，一种开创性的自主轨道站保持方法已被引入，该方法采用了明确为低地轨道量身定制的无模型深度策略梯度强化学习（DPGRL）策略。为了满足低地轨道卫星群对更高效、更自律的轨道管理的迫切需求，这项研究探索了深度强化学习（DRL）与增强拓扑神经进化（NEAT）之间的潜在协同作用，以优化定点保持策略，其主要目标是使卫星能够在允许的容差范围内，在出现外部扰动时自主保持轨道，从而在整个生命周期内保持精确一致的定点保持，同时大幅降低运营成本。这项研究专门为低地球轨道卫星量身定制了 DPGRL 算法，考虑到了机动的低推力约束，并集成了密集奖励方案和基于域的奖励塑造技术。通过展示 NEAT 和 DRL 组合框架在不同运行场景中的适应性和可扩展性，这种方法有望彻底改变自主轨道管理，为更高效、适应性更强的卫星运行铺平道路，同时将推进器限制等卫星物理限制因素纳入其中。

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引用次数: 0

Secure state estimation of networked switched systems under denial-of-service attacks 拒绝服务攻击下网络交换系统的安全状态估计

IF 2.5 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

European Journal of Control

Pub Date : 2024-11-01 DOI: 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。

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