Control Engineering Practice最新文献_第7页

Robust control design to prevent unsteady bulging in continuous slab casters 防止板坯连铸机不稳定胀形的鲁棒控制设计

IF 4.6 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2026-04-01 Epub Date: 2026-01-15 DOI: 10.1016/j.conengprac.2026.106768

Julian Landauer , Paul Dollhäubl , Stefan Fuchshumer , Wilhelm Posch , Andreas Kugi , Andreas Steinboeck

In continuous casting of steel slabs, the ferrostatic pressure in the liquid core of the strand causes bending (also called bulging) of the strand shell between the guiding rolls. Unsteady bulging refers to a time-varying bending of the strand shell, leading to unwanted mold level fluctuations that can degrade the quality of the cast strand. Most mold level controller designs presented in the literature do not explicitly account for this disturbance due to the absence of suitable unsteady bulging models. As a result, these controllers often fail to sufficiently suppress unsteady bulging or even provoke it. This work presents a novel robust model-based controller design that systematically considers unsteady bulging by incorporating a control-oriented unsteady bulging model. Unlike previous approaches, the proposed method also accounts for variations in plant parameters to ensure robust suppression of unsteady bulging and consistent control performance across the entire range of operating conditions. The proposed controller is validated on industrial continuous slab casters, where it is now permanently used and achieves a significant reduction in mold level fluctuations.

在连铸钢坯时，钢绞线液芯中的静铁压力会引起导辊之间钢绞线壳的弯曲（也称为胀形）。非稳态胀形是指钢绞线外壳的时变弯曲，导致不必要的模具水平波动，从而降低铸坯的质量。由于缺乏合适的非定常胀形模型，文献中提出的大多数模具液位控制器设计都没有明确地考虑这种干扰。因此，这些控制器往往不能充分抑制非定常胀形，甚至引起非定常胀形。本文提出了一种新的基于模型的鲁棒控制器设计，通过结合面向控制的非定常胀形模型，系统地考虑了非定常胀形。与以前的方法不同，该方法还考虑了装置参数的变化，以确保在整个操作条件范围内对非定常胀形的鲁棒抑制和一致的控制性能。所提出的控制器在工业连续板坯连铸机上进行了验证，现在它被永久使用，并显著减少了模具水平的波动。

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引用次数: 0

Experimental implementation of neural network integration into a dynamic recurrent neurocontroller in PV system application 神经网络集成于动态递归神经控制器的实验实现在光伏系统中的应用

IF 4.6 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2026-04-01 Epub Date: 2026-01-08 DOI: 10.1016/j.conengprac.2025.106753

Mustapha Meraouah , Faiza Kaddari , Said Hassaine , Sandrine Moreau , Youcef Mihoub

This study aims to develop a high–performance Maximum Power Point Tracking (MPPT) strategy for Photovoltaic (PV) systems, with the objective of achieving fast convergence, minimal oscillations, and stable operation under rapidly changing irradiance. The nonlinear behavior of PV systems poses a significant challenge to meeting these goals and maximizing harvested energy. To address this, the paper presents a dynamic MPPT strategy that leverages PV characteristics through an Artificial Neural Network (ANN) trained using graph–based data processing methods to estimate the reference voltage. The neural estimator is integrated with a dynamic recurrent neurocontroller, which is trained via the Real–Time Recurrent Learning (RTRL) algorithm. To ensure system stability, a Lyapunov–based adaptive learning rate is applied to dynamic ANN–RTRL. Simulation studies were first performed to assess the effectiveness and robustness of the proposed ANN under varying meteorological conditions, and the results were subsequently validated experimentally using a Chroma PV emulator and a dSPACE 1202 MicroLabBox. The ANN performance was also compared with conventional, advanced, optimization, and intelligent algorithms under severe environmental fluctuations. The proposed ANN–RTRL was experimentally benchmarked against two reference methods: an ANN using the Least Mean Squares (ANN–LMS) algorithm and a conventional Perturb and Observe (P&O) with a Proportional–Integral (PI) controller. Results under both standard and rapidly varying irradiance conditions show that ANN–RTRL and ANN–LMS achieve tracking efficiencies above 99%, with reduced oscillations and improved precision. Notably, ANN–RTRL exhibits superior robustness and reaches the Maximum Power Point (MPP) 58% faster than ANN–LMS and 5% faster than P&O–PI, confirming its suitability for high–performance, real–time PV MPPT applications.

本研究旨在开发一种高性能的光伏（PV）系统最大功率点跟踪（MPPT）策略，其目标是在快速变化的辐照度下实现快速收敛、最小振荡和稳定运行。光伏系统的非线性行为对实现这些目标和最大限度地获取能量提出了重大挑战。为了解决这个问题，本文提出了一种动态MPPT策略，该策略通过使用基于图的数据处理方法训练的人工神经网络（ANN）来利用光伏特性来估计参考电压。神经估计器与动态递归神经控制器相结合，通过实时递归学习（RTRL）算法进行训练。为了保证系统的稳定性，将基于李雅普诺夫的自适应学习率应用于动态ANN-RTRL。首先进行了模拟研究，以评估所提出的人工神经网络在不同气象条件下的有效性和鲁棒性，随后使用Chroma PV模拟器和dSPACE 1202 MicroLabBox对结果进行了实验验证。并将人工神经网络与传统算法、高级算法、优化算法和智能算法在剧烈环境波动下的性能进行了比较。所提出的ANN - rtrl与两种参考方法进行了实验基准测试：一种是使用最小均方差（ANN - lms）算法的ANN，另一种是使用比例积分（PI）控制器的传统扰动与观察（P&；O）方法。在标准和快速变化辐照度条件下的结果表明，ANN-RTRL和ANN-LMS的跟踪效率都在99%以上，振荡减少，精度提高。值得注意的是，ANN-RTRL表现出卓越的鲁棒性，比ANN-LMS快58%，比P& - O-PI快5%，达到最大功率点（MPP），证实了其适用于高性能、实时光伏MPPT应用。

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引用次数: 0

High-accuracy tool-center-point trajectory tracking of large-scale hydraulic manipulators using dual-timescale dynamic decoupling 基于双时间尺度动态解耦的大型液压机械臂高精度刀具中心点轨迹跟踪

IF 4.6 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2026-04-01 Epub Date: 2026-01-08 DOI: 10.1016/j.conengprac.2025.106739

Junhui Zhang , Zhiwei Chen , Ruqi Ding , Weidi Huang , Min Cheng , Huaizhi Zong , Ruiheng Jia , Bing Xu

For large-scale hydraulic manipulators (LHMs) equipped on non-road mobile machines, tool-center-point (TCP) control offers significant potential for alleviating operational burden. However, conventional TCP controllers designed for industrial manipulators generally fail to achieve high accuracy trajectory tracking for LHMs, due to the complex coupling properties of rigidity, flexibility and hydraulic nonlinearity. A dual-timescale dynamic decoupling framework is proposed to facilitate the design of high-accuracy TCP controllers for LHMs in engineering applications. Firstly, an accurate multibody flexible dynamic model of the LHM is established, and both of the rigid motion and elastic deformation are expressed in the generalized coordinate frame. The generalized coordinates and the system stiffness are extended in timescale based on the singular perturbation (SP) theory, so that the dynamic model of the LHM can be decomposed into two low-order independent subsystems. Further, the TCP controller is directly designed based on the decoupled model. It is validated on a concrete pumping spreader with a 13 m LHM. The maximum trajectory tracking error can be maintained within ±0.25 m when 2 of the 3 DOFs of the LHM are actuated, while the TCP can rapidly return to a stable state within 6.5 s following an external instantaneous excitation. Compared with the controller designed based on the undecomposed model, these two indicators are reduced by 30.04% and 55.45%, respectively. The results validate the effectiveness of the proposed method for engineering applications.

对于装备在非道路移动机械上的大型液压机械手（lhm），工具中心点（TCP）控制为减轻操作负担提供了巨大的潜力。然而，由于工业机械臂的刚度、柔性和液压非线性等复杂耦合特性，传统的TCP控制器无法实现对机械臂的高精度轨迹跟踪。提出了一种双时间尺度动态解耦框架，以方便工程应用中LHMs高精度TCP控制器的设计。首先，建立了精确的多体柔性动力学模型，将其刚性运动和弹性变形均表示为广义坐标系；基于奇异摄动（SP）理论，对广义坐标和系统刚度进行了时间尺度上的扩展，从而将LHM动力学模型分解为两个低阶独立子系统。在此基础上，直接设计了TCP控制器。在13 m LHM的混凝土泵送撒布机上进行了验证。当LHM的3个dof中的2个被驱动时，最大轨迹跟踪误差可以保持在±0.25 m以内，而TCP在外部瞬时激励后可以在6.5 s内快速恢复到稳定状态。与基于未分解模型设计的控制器相比，这两个指标分别降低了30.04%和55.45%。结果验证了该方法在工程应用中的有效性。

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引用次数: 0

Nash bargaining for power-fatigue co-optimization in wake-affected wind farms: A learning-aided approach 受尾流影响的风电场电力疲劳协同优化的纳什议价：一种学习辅助方法

IF 4.6 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2026-04-01 Epub Date: 2026-01-29 DOI: 10.1016/j.conengprac.2026.106809

Yiming Liu , Zhaojian Wang , Ruanming Huang , Bo Yang

This paper proposes a multi-objective control framework for wake-affected wind farms to manage the trade-off between power maximization and fatigue load minimization. The conflicting objectives are formulated using Nash bargaining theory, providing a fair, Pareto-efficient solution without heuristic weight tuning. A Warm-started Proximal Alternating Direction Method of Multipliers (W-PADMM) algorithm is proposed to efficiently solve the bargaining problem, which embeds a learning-aided mechanism using a Long Short-Term Memory (LSTM) network to proactively guide the optimization. Case studies on both an illustrative 9-turbine system and a real offshore wind farm under seasonally varying wind conditions demonstrate that the proposed W-PADMM approach achieves an improved power-fatigue trade-off together with substantial computational acceleration.

本文提出了一个尾迹风电场的多目标控制框架，以实现功率最大化和疲劳负荷最小化之间的平衡。相互冲突的目标是使用纳什议价理论制定的，提供了一个公平的，帕累托有效的解决方案，没有启发式的权重调整。为了有效地解决议价问题，提出了一种暖启动近邻交替方向乘数法（W-PADMM）算法，该算法嵌入了一种学习辅助机制，利用长短期记忆（LSTM）网络主动引导优化。在季节性变化的风力条件下，对一个示范性的9涡轮机系统和一个实际的海上风电场的案例研究表明，所提出的W-PADMM方法实现了改进的功率疲劳权衡以及可观的计算加速。

引用次数: 0

Robust unified dual-domain control framework for high-performance parallel robots 高性能并联机器人鲁棒统一双域控制框架

IF 4.6 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2026-04-01 Epub Date: 2026-01-25 DOI: 10.1016/j.conengprac.2026.106803

Eyyup Sincar , Zeki Y. Bayraktaroglu , Eray A. Baran , Evren Emre

This paper introduces a unified joint–task-space control framework for a 6-DoF Stewart platform that overcomes the limitations of pure joint-space methods, including inverse-kinematic ambiguities, configuration flips, and sensitivity to dynamic variations. The proposed architecture integrates a nonsingular fast terminal sliding mode (NFTSM) controller, a nonlinear disturbance observer, and model-based feedforward compensation in the joint space, together with a complementary NFTSM-based task-space controller that continuously refines end-effector motion through Jacobian feedback. A rigorous Lyapunov analysis establishes finite-time convergence and robustness under modeling uncertainties and external disturbances. Extensive experiments—including sinusoidal and square-wave tracking, frequency-sweep tests, and payload variations—demonstrate that the unified controller consistently achieves the lowest tracking errors, superior robustness to excitation frequency and load changes, and smoother actuator effort without increasing energy consumption. The results confirm the suitability of the proposed method for high-precision parallel manipulators operating in dynamic, uncertain, and disturbance-rich environments.

本文介绍了一种统一的6自由度Stewart平台联合任务空间控制框架，克服了纯联合空间方法的局限性，包括逆运动模糊性、构型翻转和对动态变化的敏感性。所提出的体系结构集成了非奇异快速终端滑模（NFTSM）控制器、非线性扰动观测器和关节空间中基于模型的前馈补偿，以及一个互补的基于NFTSM的任务空间控制器，该控制器通过雅可比反馈不断改进末端执行器的运动。严格的李雅普诺夫分析在建模不确定性和外部干扰下建立了有限时间收敛性和鲁棒性。广泛的实验——包括正弦波和方波跟踪、扫频测试和有效载荷变化——表明，统一控制器始终能够实现最低的跟踪误差，对激励频率和负载变化具有卓越的鲁棒性，并且在不增加能耗的情况下实现更平稳的执行器工作。结果表明，该方法适用于动态、不确定、多干扰环境下的高精度并联机器人。

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引用次数: 0

Hierarchical structure-based adaptive fault-tolerant control and collision avoidance for multiple fixed-wing UAVs: A fully actuated system approach 基于层次结构的多固定翼无人机自适应容错控制与避碰：一种全驱动系统方法

IF 4.6 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2026-04-01 Epub Date: 2026-01-14 DOI: 10.1016/j.conengprac.2026.106763

Bo Meng , Ke Zhang , Bin Jiang

This paper is dedicated to addressing fault-tolerant control and collision avoidance problems for fixed-wing unmanned aerial vehicles with engine degradation, control surface faults, and external disturbances. Primarily, a trajectory planning layer is developed based on the fully actuated system approach and the artificial potential function. In this framework, the projection of the relative velocity and the relative distance are fed into a fuzzy logic system to determine the collision avoidance potential intensity. Subsequently, the dynamic model of the fixed-wing unmanned aerial vehicle is divided into the translational and rotational dynamics subsystems, with control protocols designed separately via the fully actuated system approach. Simultaneously, adaptive disturbance observers are constructed to compensate for composite disturbances without prior knowledge of bounds. Moreover, to better align with practical flight control, the rotational subsystem is split into two channels. Reference signals for each channel are derived from the nonlinear guidance law and coordinated turn principle, and the Nussbaum function handles unknown control directions. Finally, the effectiveness of the proposed strategy is validated through simulation and comparative studies.

研究了固定翼无人机在发动机退化、控制面故障和外界干扰情况下的容错控制和避碰问题。首先，建立了基于全驱动系统方法和人工势函数的轨迹规划层。在该框架中，将相对速度和相对距离的投影输入到模糊逻辑系统中，以确定避碰潜在强度。随后，将固定翼无人机动力学模型划分为平移和旋转动力学子系统，并采用全驱动系统方法分别设计控制协议。同时，构造自适应扰动观测器来补偿复合扰动，而不需要预先知道边界。此外，为了更好地适应实际飞行控制，旋转子系统被分成两个通道。各通道参考信号由非线性制导律和协调转向原理导出，Nussbaum函数处理未知控制方向。最后，通过仿真和对比研究验证了所提策略的有效性。

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引用次数: 0

Hybrid shoe-approaching and pressure control strategy for multi-channel braking systems of mine hoists 矿井提升机多通道制动系统的进蹄与压力混合控制策略

IF 4.6 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2026-04-01 Epub Date: 2026-01-17 DOI: 10.1016/j.conengprac.2026.106791

Hui Xie , Sihan Chen , Gang Shen , Shuhui Fei , Yu Tang , Yongcun Guo , Yuanjing He

To address the challenges of inconsistent shoe-approaching motion and excessive transient impacts in the operation of multi-channel braking systems (MCBS) for mine hoists, this investigation presents a novel hybrid shoe-approaching/pressure control strategy utilizing real-time braking pressure feedback. First, the braking process is divided into two distinct stages: shoe-approaching motion and contact compression. A hybrid position/force switching control scheme, relying on braking pressure feedback, is developed using hysteresis switching principle. Second, an online fastest shoe-approaching trajectory planning algorithm is designed with a nonlinear filter, and a three-loop shoe-approaching control strategy is proposed, which consists of an outer loop for shoe-approaching trajectory planning, an inner loop for position tracking of brake, and a cross coupled loop for multi-channel synchronous shoe-approaching motion. Finally, two sets of comparative experiments are carried out on the multi-channel braking test bench of the hoist. The experimental results indicate that, compared with the traditional braking control mode, the proposed braking control strategy can effectively suppress the braking transient impact, shorten the shoe-approaching time, and enhance the consistency of shoe-approaching motion of the MCBS.

为了解决矿井提升机多通道制动系统（MCBS）在运行过程中存在的进蹄运动不一致和瞬态冲击过大的问题，本研究提出了一种利用实时制动压力反馈的进蹄/压力混合控制策略。首先，制动过程分为两个不同的阶段：接近鞋的运动和接触压缩。利用磁滞切换原理，提出了一种基于制动压力反馈的位置/力混合切换控制方案。其次，采用非线性滤波设计了一种在线最快追鞋轨迹规划算法，并提出了一种三环追鞋控制策略，该策略由追鞋轨迹规划的外环、制动器位置跟踪的内环和多通道同步追鞋运动的交叉耦合环组成。最后，在提升机多通道制动试验台上进行了两组对比试验。实验结果表明，与传统制动控制方式相比，所提出的制动控制策略能有效抑制制动瞬态冲击，缩短MCBS的接近蹄时间，增强MCBS的接近蹄运动一致性。

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引用次数: 0

Modeling and cascade-based robust dynamic control of a hyper-constrained parallel mechanism for high-precision robotic tasks 高精度机器人任务超约束并联机构建模及基于级联的鲁棒动态控制

IF 4.6 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2026-04-01 Epub Date: 2026-01-12 DOI: 10.1016/j.conengprac.2025.106756

Marco Fernandes dos Santos Xaud , Pål Johan From , Antonio Candea Leite

This paper presents the mechanical design, constructive aspects, kinematic modeling and control design methodology for a hyper-constrained parallel mechanism (HCPM), composed of six closed kinematic chains and twelve spherical joints, which is developed to carry out high-precision robotic tasks in industrial and agricultural environments. Here, we employ a systematic modeling methodology which considers the kinematic constraints of the mechanism from its structure equations, rather than explicitly using the constraint equations. This allows us to describe the kinematic constraints in the operation velocity space instead of the joint configuration space. From this analytical approach, we can compute the velocity of the non-actuated joints as a function of the velocity of the actuated ones. The control design uses an inverse kinematics algorithm based on the pseudo-inverse Jacobian matrix. In order to deal with many potential singular configurations which may occur during the task execution, we consider a recently proposed approach, called the Filtered Inverse method, which dynamically estimates the inverse of the Jacobian matrix instead of computing its true inverse instantaneously. The dynamic control of the HCPM is achieved using a simplified, Lagrangian-derived model to provide the computed-torque feedforward term for nonlinear compensation and decoupling, enabling motion simulation for high-speed trajectories while dismissing the need for a full explicit model, which is complex and difficult to obtain. Despite the simplifications, a robust cascade control strategy is proposed—featuring a second-order sliding mode (STA) compensator—to handle modeling uncertainties effectively. 3D computer modeling, numerical simulations, and laboratory experiments with two prototypes of the hyper-constrained parallel mechanism were conducted to validate the proposed approach and demonstrate its feasibility for high-precision robotic tasks.

本文介绍了一种由6个封闭运动链和12个球面关节组成的超约束并联机构（HCPM）的机械设计、构造、运动学建模和控制设计方法，该机构是为在工农业环境中执行高精度机器人任务而开发的。在这里，我们采用了一种系统的建模方法，该方法从其结构方程中考虑机构的运动约束，而不是明确地使用约束方程。这使得我们可以在运动速度空间而不是关节位形空间中描述运动约束。根据这种分析方法，我们可以计算出非驱动关节的速度作为驱动关节速度的函数。控制设计采用基于伪逆雅可比矩阵的逆运动学算法。为了处理在任务执行过程中可能出现的许多潜在的奇异构型，我们考虑了最近提出的一种方法，称为滤波逆方法，它动态估计雅可比矩阵的逆，而不是立即计算它的真逆。HCPM的动态控制采用简化的拉格朗日模型来实现，该模型为非线性补偿和解耦提供了计算扭矩前馈项，从而实现了高速轨迹的运动仿真，同时省去了复杂且难以获得的完整显式模型的需要。尽管进行了简化，但提出了一种鲁棒级联控制策略-具有二阶滑模（STA）补偿器-以有效地处理建模不确定性。对两种超约束并联机构进行了三维计算机建模、数值模拟和实验室实验，验证了所提出的方法，并证明了其在高精度机器人任务中的可行性。

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引用次数: 0

Simplified optimal backstepping control for a spacecraft based on flexible prescribed performance with non-initial constraint 基于柔性预定性能的航天器非初始约束简化最优反演控制

IF 4.6 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2026-04-01 Epub Date: 2026-01-16 DOI: 10.1016/j.conengprac.2026.106786

Zhen Li, Guohua Kang, Junfeng Wu, Jiaqi Wu, Chuanxiao Xu

This paper addresses the position and attitude control of combined spacecraft in on-orbit servicing missions, taking into account model parameter uncertainties, unknown external disturbances, and fuel-optimal constraints. A novel flexible prescribed-performance optimal backstepping controller without initial constraints is proposed by incorporating an Actor-Critic-Identify neural network architecture. First, a dynamic model of the combined spacecraft is established, with all uncertainties treated as lumped disturbances. To improve transient performance and remove initial value constraints, a flexible prescribed performance function is designed, which accommodates input saturation and decouples settling time from both initial states and controller parameters. Subsequently, a steady-state performance optimized Identify weight adaptation law is employed for rapid and accurate estimation of the nonlinear lumped disturbances. For fuel optimization, a simplified Actor-Critic adaptation law is developed, eliminating the need for complex step-by-step derivations while ensuring weight convergence. The uniform ultimate boundedness of the closed-loop system is proven using Lyapunov theory. Numerical simulations and semi-physical experiments verify the proposed method’s advantages in both steady-state and transient performance, as well as its applicability to on-orbit implementation.

本文研究了考虑模型参数不确定性、未知外部干扰和燃料最优约束的组合航天器在轨服务任务的位置和姿态控制问题。提出了一种新的无初始约束的柔性规定性能最优反步控制器。首先，建立了组合航天器的动力学模型，将所有不确定因素作为集总扰动处理。为了改善暂态性能并消除初始值约束，设计了一个灵活的规定性能函数，该函数可以容纳输入饱和，并将初始状态和控制器参数的沉降时间解耦。然后，采用一种优化稳态性能的辨识权自适应律对非线性集总扰动进行快速准确的估计。对于燃料优化，开发了简化的Actor-Critic自适应律，在确保权重收敛的同时消除了复杂的逐步推导的需要。利用李雅普诺夫理论证明了闭环系统的一致极限有界性。数值仿真和半物理实验验证了该方法在稳态和瞬态性能上的优势，以及它在在轨实现中的适用性。

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引用次数: 0

Full actuation control for rail-less electric bus trains 无轨电动客车列车的全驱动控制

IF 4.6 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2026-04-01 Epub Date: 2026-01-13 DOI: 10.1016/j.conengprac.2026.106760

Shuo Wang , Baorui Wang , Guoxiang Gu

This paper proposes rail-less trains, composed of electric buses which are coupled together. The dynamic model of the rail-less electric bus train (REBT) involves not only nonlinearities but also unknown and uncertain parameters, which pose significant challenges. To mitigate model nonlinearities and parameter uncertainties in practical REBT system, we develop centralized and distributed adaptive control laws based on high-order full actuation (HOFA) control. For developing the centralized adaptive control law, we propose a novel adaptive control method, integrating gamma-projection operators with sigma-modification in adaptive estimation. This method robustly constrains parameter estimates within the known set while suppressing drift and offering tradeoffs between the magnitudes of control signal and tracking performance. For developing the distributed adaptive control law, we propose a different adaptive control method, employing both autonomous and cooperative control actions and using again the gamma-projection. In addition, adaptive estimation is aided by an off-line least-squares (LS) algorithm that ensures the adaptive estimates to converge to the true system parameters under the persistent excitation condition, leading to asymptotic feedback linearization and global asymptotic stabilization. Disturbance rejection in the framework of

H_{\infty}

-control is studied for the linearized REBT system. It is shown that the two proposed adaptive control laws ensure the

H_{\infty}

-norm from the input disturbance to the output tracking errors for velocity and inter-EB-distance controls to be strictly smaller than any γ > 0, effectively suppressing energy bounded disturbances in the worst-case. The simulation study includes industrial-level simulators and validates the proposed adaptive control methods.

本文提出了一种由电动客车耦合而成的无轨列车。无轨电动客车列车（REBT）的动力学模型不仅涉及非线性，而且涉及未知和不确定参数，这给整车动力学模型的建立带来了很大的挑战。为了缓解实际REBT系统中的模型非线性和参数不确定性，提出了基于高阶全致动（HOFA）控制的集中式和分布式自适应控制律。为了建立集中自适应控制律，我们提出了一种新的自适应控制方法，将伽玛投影算子与自适应估计中的西格玛修正相结合。该方法鲁棒地约束了已知集合内的参数估计，同时抑制了漂移，并在控制信号的大小和跟踪性能之间提供了折衷。为了发展分布式自适应控制律，我们提出了一种不同的自适应控制方法，采用自主和合作控制动作，并再次使用伽马投影。此外，采用离线最小二乘（LS）算法辅助自适应估计，确保自适应估计在持续激励条件下收敛到系统的真实参数，从而实现渐近反馈线性化和全局渐近稳定。研究了线性化REBT系统在H∞控制框架下的抗扰性。结果表明，所提出的两种自适应控制律保证了速度和eb -距离控制从输入扰动到输出跟踪误差的H∞范数严格小于任何γ >； 0，在最坏情况下有效地抑制了能量有界扰动。仿真研究包括工业级模拟器，并验证了所提出的自适应控制方法。

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引用次数: 0