IFAC Journal of Systems and Control最新文献

英文中文

Decoupled model predictive control with SCESO for contact force regulation of a shape-adaptive shield in assistive toileting robots 基于SCESO的形状自适应护罩接触力调节解耦模型预测控制

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2026-03-01 Epub Date: 2026-02-17 DOI: 10.1016/j.ifacsc.2026.100390

Lingling Chen , Yuxin Dong , Ziqian Wang , Chipu Zhang , Zhihao Yang , Pengyue Lai , Zhihao Liu

To overcome the limitations of discomfort and physical constraints in existing wearable nursing devices, this paper proposes a non-wearable shape-adaptive shield integrated with real-time pressure sensing for assistive toileting robots. The shield employs a bionic continuum structure with multiple adjustable spine segments and flexible sensors, enabling safe body conformity and contact force monitoring across diverse body types. To address multi-input multi-output coupling that compromise force control stability, an integral-type state feedback decoupling method is developed to mitigate inter-segment coupling. Furthermore, a novel control strategy integrating State-Compensated Extended State Observer (SCESO) and Model Predictive Control (MPC) is proposed to optimize dynamic force tracking under uncertainties. Simulations and experiments demonstrate that the decoupling-SCESO-MPC framework achieves superior tracking accuracy (average root mean square error of 0.017 N across 10 subjects with diverse body types) and robust performance, thereby providing a practical solution for safe, comfortable, and adaptive human–robot interaction in assistive toileting scenarios.

为了克服现有可穿戴式护理设备的不适和物理限制，本文提出了一种集成实时压力传感的非可穿戴式形状自适应护罩，用于辅助如厕机器人。该护罩采用仿生连续体结构，具有多个可调节的脊柱段和柔性传感器，可实现安全的身体一致性和不同体型的接触力监测。针对多输入多输出耦合影响力控制稳定性的问题，提出了一种积分型状态反馈解耦方法。在此基础上，提出了一种结合状态补偿扩展状态观测器（SCESO）和模型预测控制（MPC）的新型控制策略，用于不确定情况下的动态力跟踪优化。仿真和实验表明，解耦- sceso - mpc框架具有优异的跟踪精度（10个不同体型受试者的平均均方根误差为0.017 N）和鲁棒性，从而为辅助如厕场景中安全、舒适和自适应的人机交互提供了实用的解决方案。

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引用次数: 0

Model-free source seeking of exponentially convergent unicycle: Theoretical and robotic experimental results 指数收敛独轮车的无模型寻源：理论与机器人实验结果

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2026-03-01 Epub Date: 2026-02-12 DOI: 10.1016/j.ifacsc.2026.100378

Rohan Palanikumar , Ahmed A. Elgohary , Victoria Grushkovskaya , Sameh A. Eisa

This paper introduces a novel model-free, real-time unicycle-based source seeking design. This design autonomously steers the unicycle dynamic system towards the extremum point of an objective function or physical/scalar signal that is unknown expression-wise, but accessible via measurements. A key contribution of this paper is that the introduced design converges exponentially to the extremum point of objective functions (or scalar signals) that behave locally like a higher-degree power function (e.g., fourth-degree polynomial function) as opposed to locally quadratic objective functions, the usual case in literature. We provide theoretical results and design characterization, supported by a variety of simulation results that demonstrate the robustness of the proposed design, including cases with different initial conditions and measurement delays/noise. Also, for the first time in the literature, we provide experimental robotic results that demonstrate the effectiveness of the proposed design and its exponential convergence ability. These experimental results confirm that the proposed exponentially convergent extremum seeking design can be practically realized on a physical robotic platform under real-world sensing and actuation constraints.

介绍了一种新颖的无模型、实时的单轮寻源设计。这种设计自主地将独轮车动力系统导向目标函数或物理/标量信号的极值点，这些信号是未知的表达式，但可以通过测量获得。本文的一个关键贡献是，引入的设计指数收敛于目标函数（或标量信号）的极值点，这些目标函数在局部表现得像一个高次幂函数（例如，四次多项式函数），而不是局部二次目标函数，这是文献中常见的情况。我们提供了理论结果和设计特性，并得到了各种仿真结果的支持，这些结果证明了所提出设计的鲁棒性，包括具有不同初始条件和测量延迟/噪声的情况。此外，在文献中，我们首次提供了机器人实验结果，证明了所提出设计的有效性及其指数收敛能力。实验结果验证了指数收敛求极值设计可以在真实传感和驱动条件下的物理机器人平台上实际实现。

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引用次数: 0

Taming non-linearity with local data-driven predictive control 用局部数据驱动的预测控制控制非线性

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2026-03-01 Epub Date: 2026-03-03 DOI: 10.1016/j.ifacsc.2026.100396

Lorenzo Pasini, Mattia Bruschetta, Alessandro Chiuso

Model Predictive Control (MPC) relies on dynamical models to compute optimal control actions in a receding-horizon fashion. When accurate models are unavailable or expensive to obtain, data-driven predictive control (DDPC) offers a viable alternative for optimal control synthesis. While the linear case is now relatively well understood, both the theoretical foundations and practical implementations for nonlinear systems remain less developed. In this paper, we proposed an effective approach toward closing this gap by combining locally linear data-driven approximations that, when iteratively refined, yield a sequential data-driven quadratic programming algorithm for nonlinear DDPC. The proposed DDPC framework is validated through extensive numerical simulations on an inverted pendulum benchmark. In particular, we show that (i) as the amount of data increases, the closed-loop performance approaches that of a model-based MPC, (ii) our method compares favorably with a recently proposed nonlinear variant of Data Enabled Predictive Control (DeePC), and (iii) the algorithm can be effectively deployed online, achieving pendulum swing-up within a handful of trials, starting from small and easily collectable dataset.

模型预测控制（MPC）是一种基于动态模型的控制方法。当无法获得精确的模型或获得成本昂贵时，数据驱动预测控制（DDPC）为最优控制综合提供了可行的替代方案。虽然线性系统的情况现在已经得到了较好的理解，但非线性系统的理论基础和实际实现仍然不太发达。在本文中，我们提出了一种有效的方法，通过结合局部线性数据驱动近似来缩小这一差距，当迭代改进时，产生非线性DDPC的顺序数据驱动二次规划算法。通过倒立摆基准的大量数值模拟验证了所提出的DDPC框架。特别是，我们表明(i)随着数据量的增加，闭环性能接近基于模型的MPC，（ii）我们的方法与最近提出的数据支持预测控制（DeePC）的非线性变体相比更具优势，以及（iii）该算法可以有效地在线部署，在少量试验中实现钟摆摆动，从小型且易于收集的数据集开始。

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引用次数: 0

Direct data-driven interpolation and approximation of linear parameter-varying system trajectories 线性参数变化系统轨迹的直接数据驱动插值和逼近

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2026-03-01 Epub Date: 2025-12-11 DOI: 10.1016/j.ifacsc.2025.100352

Chris Verhoek , Ivan Markovsky , Roland Tóth

We consider the problem of estimating missing values in trajectories of linear parameter-varying (LPV) systems. We solve this interpolation problem for the class of shifted-affine LPV systems. Conditions for the existence and uniqueness of solutions are given and a direct data-driven algorithm for its computation is presented, i.e., the data-generating system is not given by a parametric model but is implicitly specified by data. We illustrate the applicability of the proposed solution on illustrative examples of a mass–spring-damper system with exogenous and endogenous parameter variation.

研究了线性变参系统轨迹缺失值的估计问题。我们解决了平移仿射LPV系统的插值问题。给出了解的存在唯一性条件，并给出了其计算的直接数据驱动算法，即数据生成系统不是由参数模型给出，而是由数据隐式指定。我们在具有外生和内生参数变化的质量-弹簧-阻尼器系统的实例上说明了所提出的解的适用性。

引用次数: 0

On continuous-time sparse identification of nonlinear polynomial systems 非线性多项式系统的连续时间稀疏辨识

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2026-03-01 Epub Date: 2026-01-15 DOI: 10.1016/j.ifacsc.2026.100365

Mazen Alamir

This paper leverages recent advances in high derivatives reconstruction from noisy-time series and sparse multivariate polynomial identification in order to improve the process of parsimoniously identifying, from a small amount of data, unknown Single-Input/Single-Output nonlinear dynamics of relative degree up to 4. The methodology is illustrated on the Electronic Throttle Controlled automotive system.

本文利用噪声时间序列的高导数重构和稀疏多元多项式辨识的最新进展，改进了从少量数据中简化识别相对程度高达4的未知单输入/单输出非线性动力学的过程。以电子节气门控制汽车系统为例说明了该方法。

引用次数: 0

Direct data-driven model-reference control for constrained systems 约束系统的直接数据驱动模型参考控制

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2026-03-01 Epub Date: 2025-12-26 DOI: 10.1016/j.ifacsc.2025.100355

Manas Mejari, Milad Banitalebi Dehkordi, Dario Piga

A central challenge in direct data-driven control design is to ensure constraint satisfaction and safe operation of the closed-loop system while maintaining certain performance. To address this, we propose a hierarchical data-driven control architecture for constrained linear time-invariant systems to track a given setpoint reference. The inner-loop consists of a model reference controller (MRC) synthesized directly from the noisy data, which ensures performance by attempting to match a user-specified reference model. The outer-loop is a robust model predictive control (RMPC), acting as a safety pre-filter which optimally modifies the reference signal given to the inner loop MRC, ensuring constraint satisfaction and improving overall tracking performance. Additionally, the RMPC scheme accounts for a potential mismatch between the achieved closed-loop and the desired reference model, in the case of imperfect matching by the inner loop controller. The effectiveness of the method is demonstrated via a numerical example.

直接数据驱动控制设计的核心挑战是在保持一定性能的同时保证闭环系统的约束满足和安全运行。为了解决这个问题，我们提出了一种用于约束线性定常系统的分层数据驱动控制体系结构，以跟踪给定的设定值参考。内环由一个直接从噪声数据合成的模型参考控制器（MRC）组成，它通过尝试匹配用户指定的参考模型来确保性能。外环是一个鲁棒模型预测控制（RMPC），作为一个安全预滤波器，最优地修改给定给内环MRC的参考信号，确保约束满足并提高整体跟踪性能。此外，在内环控制器不完全匹配的情况下，RMPC方案考虑了实现的闭环与期望的参考模型之间的潜在不匹配。通过数值算例验证了该方法的有效性。

引用次数: 0

Recursive identification for FIR systems with interval binary observations 区间二值观测FIR系统的递归辨识

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2026-03-01 Epub Date: 2026-02-05 DOI: 10.1016/j.ifacsc.2026.100383

Xingrui Liu , Xin Li , Mingjie Shao , Yanlong Zhao

This paper investigates the identification of finite impulse response systems based on interval binary observations, where only the information on whether the outputs lie within a prefixed interval is available. Unlike existing works that typically consider single-threshold binary observations, the distribution function of interval binary observations no longer exhibits monotonicity. This makes it difficult for most existing identification algorithms to ensure a unique solution at each recursive step. To overcome this challenge, a novel three-step recursive identification algorithm named the partition–convergence–decision algorithm is developed. First, to ensure that the distribution function remains monotonic, the parameter space is partitioned into multiple partition regions. Secondly, a recursive projection identification algorithm is executed in parallel across all partition regions. Finally, a recursive decision criterion based on the prediction errors of the interval binary observations is constructed to determine the partition region containing the true system parameter. Both the almost sure and the mean square convergence of the proposed recursive projection identification algorithm within the correct region are proved, and the effectiveness of the recursive decision criterion in the probabilistic sense is established. A simulation example is presented to validate the theoretical results.

本文研究了基于区间二值观测的有限脉冲响应系统辨识问题，其中只有关于输出是否在给定区间内的信息是可用的。与现有研究中一般考虑单阈值二值观测不同，区间二值观测的分布函数不再呈现单调性。这使得大多数现有的识别算法难以确保在每个递归步骤中都有唯一的解。为了克服这一挑战，提出了一种新的三步递归识别算法——划分-收敛-决策算法。首先，为了保证分布函数的单调性，将参数空间划分为多个划分区域。其次，在所有分区区域上并行执行递归投影识别算法。最后，根据区间二值观测值的预测误差构造递归决策准则，确定包含真实系统参数的分区区域。证明了所提出的递推投影识别算法在正确区域内的几乎确定性和均方收敛性，并证明了递推决策准则在概率意义上的有效性。通过仿真实例验证了理论结果。

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引用次数: 0

A recursive parameter identification algorithm for nonlinear errors-in-variables models 非线性变量误差模型的递归参数辨识算法

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2026-03-01 Epub Date: 2026-02-02 DOI: 10.1016/j.ifacsc.2026.100381

Hugo Koide , Jérémy Vayssettes , Guillaume Mercère

Recursive identification of model parameters from data is an essential tool in adaptive signal processing and adaptive control applications. For general nonlinear regression problems where errors are present in both dependent and independent variables, the errors-in-variables (EIV) model is frequently employed to avoid a biased estimation of the system parameters. This work presents a recursive identification algorithm for nonlinear EIV models which is derived from an offline sequential quadratic programming solution. A proof of convergence is provided and the performance of the proposed algorithm is illustrated on benchmark simulation examples. The results demonstrate the numerical efficiency of the recursive algorithm, as well as its ability to track time-varying system parameters.

从数据中递归识别模型参数是自适应信号处理和自适应控制应用中必不可少的工具。对于因变量和自变量都存在误差的一般非线性回归问题，通常采用变量中误差（EIV）模型来避免系统参数的偏估计。本文提出了一种非线性EIV模型的递归识别算法，该算法来源于离线顺序二次规划解。给出了收敛性证明，并通过基准仿真实例说明了该算法的性能。结果证明了递归算法的数值效率，以及它对时变系统参数的跟踪能力。

引用次数: 0

The waterbed effect on quasiperiodic disturbance observer: Avoidance of sensitivity tradeoff with time delays 准周期扰动观测器的水床效应：避免时延下的灵敏度权衡

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2026-03-01 Epub Date: 2026-02-23 DOI: 10.1016/j.ifacsc.2026.100392

Hisayoshi Muramatsu

In linear time-invariant systems, the sensitivity function to disturbances is designed under a sensitivity tradeoff known as the waterbed effect. To compensate for a quasiperiodic disturbance, a quasiperiodic disturbance observer using time delays was proposed. Its sensitivity function avoids the sensitivity tradeoff, achieving wideband harmonic suppression without amplifying aperiodic disturbances or shifting harmonic suppression frequencies. However, its open-loop transfer function is not rational and does not satisfy the assumptions of existing Bode sensitivity integrals due to its time delays. This paper provides Bode-like sensitivity integrals for the quasiperiodic disturbance observer in both continuous-time and discrete-time representations and clarifies the avoided sensitivity tradeoff with time delays.

在线性时不变系统中，对扰动的灵敏度函数是根据称为水床效应的灵敏度权衡来设计的。为了补偿准周期扰动，提出了一种使用时滞的准周期扰动观测器。其灵敏度函数避免了灵敏度的权衡，在不放大非周期干扰和不移动谐波抑制频率的情况下实现了宽带谐波抑制。但其开环传递函数不合理，由于存在时滞，不满足现有波德灵敏度积分的假设。本文给出了准周期扰动观测器在连续时间和离散时间两种形式下的类波德灵敏度积分，并阐明了在时滞下避免的灵敏度权衡。

引用次数: 0

Pulmonary air distribution during abdominal and chest breathing 腹部和胸部呼吸时肺部空气分布

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2026-03-01 Epub Date: 2026-02-05 DOI: 10.1016/j.ifacsc.2026.100364

B. Laufer , S. Krueger-Ziolek , K. Moeller

Wider knowledge of air distribution within the lungs during breathing would lead to a better understanding of respiratory mechanics and could both; enhance the therapy of patients with lung diseases and increase oxygenation. Electrical impedance tomography (EIT), a motion capture system and a spirometer were used to investigate differences in pulmonary air distribution during abdominal and chest breathing in 11 sitting subjects. The subjects inhaled comparable tidal volumes while EIT was used to analyse air distribution in two layers on the upper body during abdominal and chest breathing.

The lower part of the lung was stronger ventilated during abdominal breathing, while the entire lung was ventilated more homogeneously during chest breathing. It is possible to influence the distribution of air within the lungs, and thus controlled ventilation of the lungs could improve the oxygen supply of patients and might increase the success of lung therapies through the targeted delivery of medication and controlled ventilation of damaged lung areas.

对呼吸过程中肺内空气分布的更广泛的了解将有助于更好地理解呼吸力学，并且可以；加强对肺部疾病患者的治疗，增加氧合。采用电阻抗断层扫描（EIT）、运动捕捉系统和肺活量计研究了11名坐着受试者在腹部和胸部呼吸时肺部空气分布的差异。受试者吸入相当的潮气量，同时使用EIT分析腹部和胸部呼吸时上身两层的空气分布。腹式呼吸时肺下部通气较强，胸式呼吸时全肺通气较均匀。有可能影响肺内空气的分布，因此控制肺部通气可以改善患者的氧气供应，并可能通过有针对性地给药和对受损肺区进行控制通气来提高肺部治疗的成功率。

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