Mechatronics最新文献_第10页

Fast switching and dynamic characteristics preservation of water hydraulic high-speed on-off valve using pressure-adaptive multistage voltage and sliding mode control 基于压力自适应多级电压滑模控制的水力高速开关阀的快速开关和动态特性保持

IF 3.1 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Mechatronics

Pub Date : 2025-07-05 DOI: 10.1016/j.mechatronics.2025.103384

Xing Yang, Boyang Zhang, Defa Wu, Yinshui Liu

Water hydraulic high-speed on-off valves (WHSVs) are crucial for managing fluid flow in water hydraulic manipulator systems. As the ambient pressure changes, the dynamic characteristics of WHSVs are affected, which reduces the overall control accuracy of the manipulator. To simultaneously achieve rapid switching and maintain consistent dynamic behavior of WHSVs under variable ambient pressure, a pressure-adaptive multistage voltage and sliding mode control (PMVS) algorithm is proposed. A sliding mode controller is utilized to precisely regulate the coil current at the pre-opening and holding current levels, significantly shortening the switching time of the WHSV. By optimizing the controller’s operation time and modifying the duty cycle of the excitation voltage, the switching time remains stable across different pressures. Based on the structure of the designed WHSV group, an innovative method combining a pressure sensor and a vibration sensor is proposed to capture the dynamic characteristics of the WHSV. Experimental validation demonstrates that the PMVS method efficiently controls the switching delay and regulates the excitation voltage. Dynamic characteristic tests of WHSVs under different pressures are conducted. The results show that PMVS effectively reduces the switching time of WHSVs. Comparative tests reveal that WHSVs driven by PMVS achieve an 86.3 % reduction in opening time and an 87.5 % reduction in closing time compared to conventional pulse width modulation (CPWM). Furthermore, PMVS ensures consistent dynamic characteristics within an ambient pressure range of 0 to 20 MPa, with an opening time deviation of 7.94 % and a closing time deviation of 3.03 %. The PMVS algorithm enables the WHSV to rapidly switch and preserve dynamic characteristics under variable ambient pressures.

水液压高速开关阀（WHSVs）是水液压机械手系统中控制流体流动的关键部件。随着环境压力的变化，其动态特性会受到影响，从而降低了机械手的整体控制精度。为了在变环境压力下同时实现快速开关和保持稳定的动态行为，提出了一种压力自适应多级电压滑模控制（PMVS）算法。利用滑模控制器精确调节预开和保持电流水平的线圈电流，大大缩短了WHSV的开关时间。通过优化控制器的工作时间和修改励磁电压的占空比，使开关时间在不同压力下保持稳定。基于所设计的WHSV组的结构，提出了一种结合压力传感器和振动传感器的创新方法来捕获WHSV的动态特性。实验验证表明，该方法能有效地控制开关延迟和调节励磁电压。进行了不同压力条件下的水轮机动态特性试验。结果表明，PMVS有效地缩短了wsvs的切换时间。对比试验表明，与传统脉宽调制（CPWM）相比，PMVS驱动的WHSVs实现了86.3%的打开时间减少和87.5%的关闭时间减少。在0 ~ 20 MPa的环境压力范围内，PMVS的动态特性保持一致，开启时间偏差为7.94%，关闭时间偏差为3.03%。PMVS算法使WHSV能够在可变环境压力下快速切换并保持动态特性。

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引用次数: 0

Practical and robust incremental model predictive control for flexible-joint robots 柔性关节机器人实用鲁棒增量模型预测控制

IF 3.1 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Mechatronics

Pub Date : 2025-06-26 DOI: 10.1016/j.mechatronics.2025.103364

Yongchao Wang , Tian Zheng , Maged Iskandar , Marion Leibold , Jinoh Lee

This article proposes an optimization-based method for robust yet efficient control of flexible-joint robots by using the model predictive control approach. The time-delay estimation (TDE) technique is used to approximate uncertain and nonlinear dynamic equations, where neither concrete knowledge of mathematical system model parameters is required in the approximation, thus granting the model-free property for dynamics compensation and real-time system linearization. TDE is integrated with model predictive control, which is designated as the incremental model predictive control (IMPC) framework. This approach guarantees the tracking performance of the flexible joint robot with input and output constraints, such as motor torque and joint states. Moreover, the proposed controller can practically circumvent high-order derivatives in implementation while providing robust tracking, a capability that conventional methods for flexible joint robots often face challenges due to the inherent nature of their high-order dynamics. The input-to-state stability of IMPC in a local region around the reachable reference trajectory is theoretically proven, and the high approximation accuracy of the resulting incremental system is analyzed. Finally, a series of experiments is conducted on a flexible-joint robot to verify the practical effectiveness of IMPC, and superior performance in terms of high accuracy, high computational efficiency, and constraint admissibility is demonstrated.

本文提出了一种基于优化的柔性关节机器人鲁棒高效控制方法——模型预测控制方法。时延估计（TDE）技术用于逼近不确定和非线性动力学方程，在逼近时不需要具体的系统数学模型参数知识，从而使动态补偿和实时系统线性化具有无模型性。将TDE与模型预测控制相结合，称为增量模型预测控制（IMPC）框架。该方法在电机转矩和关节状态等输入输出约束条件下保证了柔性关节机器人的跟踪性能。此外，所提出的控制器实际上可以在实现中绕过高阶导数，同时提供鲁棒跟踪，这是柔性关节机器人的传统方法由于其高阶动力学的固有性质而经常面临挑战的能力。从理论上证明了IMPC在可达参考轨迹附近局部区域的输入-状态稳定性，并分析了由此产生的增量系统的高逼近精度。最后，在一个柔性关节机器人上进行了一系列实验，验证了IMPC的实际有效性，并在高精度、高计算效率和约束容忍度方面表现出优异的性能。

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引用次数: 0

Matrix-sensitivity-based active disturbance rejection control for hydraulic servo positioning systems with friction compensation 基于矩阵灵敏度的摩擦补偿液压伺服定位系统自抗扰控制

IF 3.1 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Mechatronics

Pub Date : 2025-06-25 DOI: 10.1016/j.mechatronics.2025.103378

Kaixian Ba , Ning Liu , Jinbo She , Yuan Wang , Guoliang Ma , Bin Yu , Xiangdong Kong

Accurate position regulation in hydraulic servo systems (HDU) plays a critical role in ensuring system stability, operational efficiency, and achieving high-accuracy performance. However, friction-induced nonlinearities, including Stribeck effects and internal friction dynamics, significantly impact tracking accuracy. This paper introduces a matrix-sensitivity-based active disturbance rejection control (MSADRC) method that compensates for friction without requiring an explicit friction model. By leveraging matrix sensitivity, MSADRC effectively decouples system dynamics and enhances control accuracy, particularly in suppressing frictional effects. A third-order extended state observer (ESO) first estimates total system disturbances, while a model predictive mechanism converts nonlinear time-varying disturbances into a feedforward compensation term. The resulting matrix sensitivity-based compensation optimally adjusts system response, ensuring improved performance. Experimental results show that MSADRC effectively mitigates nonlinear disturbances, reducing peak error by up to 55 % compared to conventional ADRC methods. This approach provides a reliable and efficient strategy to address adaptive friction compensation issues in hydraulic control systems.

在液压伺服系统（HDU）中，精确的位置调节对保证系统的稳定性、运行效率和实现高精度性能起着至关重要的作用。然而，摩擦引起的非线性，包括斯特里贝克效应和内摩擦动力学，显著影响跟踪精度。本文介绍了一种基于矩阵灵敏度的自抗扰控制（MSADRC）方法，该方法可以在不需要显式摩擦模型的情况下补偿摩擦。通过利用矩阵灵敏度，MSADRC有效地解耦了系统动力学，提高了控制精度，特别是在抑制摩擦效应方面。三阶扩展状态观测器（ESO）首先估计系统的总扰动，而模型预测机制将非线性时变扰动转换为前馈补偿项。由此产生的基于矩阵灵敏度的补偿优化调整系统响应，确保提高性能。实验结果表明，与传统的自抗扰方法相比，MSADRC有效地减轻了非线性干扰，峰值误差降低了55%。该方法为解决液压控制系统中的自适应摩擦补偿问题提供了一种可靠、有效的策略。

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引用次数: 0

Active compensation of position dependent flexible dynamics in high-precision mechatronics 高精度机电一体化中位置相关柔性动力学的主动补偿

IF 3.1 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Mechatronics

Pub Date : 2025-06-25 DOI: 10.1016/j.mechatronics.2025.103377

Yorick Broens , Hans Butler , Ramidin Kamidi , Koen Verkerk , Siep Weiland

Growing demands in the semiconductor industry necessitate increasingly stringent requirements on throughput and positioning accuracy of lithographic equipment. Meeting these demands involves employing highly aggressive motion profiles, which introduce position-dependent flexible dynamics, thus compromising achievable position tracking performance. This paper introduces a control approach enabling active compensation of position-dependent flexible dynamics by extending the conventional rigid-body control structure to include active control of flexible dynamics. To facilitate real-time implementation of the control algorithm, appropriate position-dependent weighting functions are introduced, ensuring computationally efficient execution of the proposed approach. The efficacy of the proposed control design approach is demonstrated through experiments conducted on a state-of-the-art extreme ultraviolet (EUV) wafer stage.

半导体行业的需求日益增长，对光刻设备的吞吐量和定位精度要求也越来越高。为了满足这些要求，需要采用高度激进的运动轮廓，引入与位置相关的灵活动力学，从而影响可实现的位置跟踪性能。本文将传统的刚体控制结构扩展到柔性动力学主动控制，提出了一种位置相关柔性动力学主动补偿的控制方法。为了便于控制算法的实时实现，引入了适当的位置相关加权函数，确保了所提出方法的计算效率。通过在最先进的极紫外（EUV）晶圆台上进行的实验，证明了所提出的控制设计方法的有效性。

引用次数: 0

A low-cost 3D printed electromagnetic gripper for robotic arms 一种用于机械臂的低成本3D打印电磁夹具

IF 3.1 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Mechatronics

Pub Date : 2025-06-24 DOI: 10.1016/j.mechatronics.2025.103374

Andrea Ruo, Luca Bernardi, Ludovico Campanelli, Mattia Grespan, Danila Trane, Roberto Sedoni, Diego Angeli, Lorenzo Sabattini, Valeria Villani

Grasping, carrying, and placing objects are fundamental capabilities and common operations for robots and robotic manipulators. To ensure secure grasping of objects with a wide variety of shapes, sizes, and materials, various sensors and control strategies are also necessary. In this paper, an electromagnetic robotic gripper is proposed. The exploitation of electromagnetism principles for grasping is not new in the literature, but the proposed design innovation aims at proposing an open-source and low-cost solution that can be 3D printed. The developed prototype was tested by performing pick and place operations on samples of progressively increasing mass. Finally, a thermodynamic analysis was conducted to determine the steady-state external temperature of the shell and identify its limitations.

抓取、搬运和放置物体是机器人和机械手的基本能力和常用操作。为了确保安全抓取各种形状、大小和材料的物体，还需要各种传感器和控制策略。本文提出了一种电磁机械手。利用电磁原理抓取在文献中并不新鲜，但提出的设计创新旨在提出一种可以3D打印的开源和低成本解决方案。开发的原型通过对质量逐渐增加的样品进行取放操作进行测试。最后，进行了热力学分析，确定了壳体的稳态外部温度，并确定了其局限性。

引用次数: 0

Testing human-hand segmentation on in-distribution and out-of-distribution data in human–robot interactions using a deep ensemble model 使用深度集成模型测试人机交互中分布内和分布外数据的人手分割

IF 3.1 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Mechatronics

Pub Date : 2025-06-21 DOI: 10.1016/j.mechatronics.2025.103365

Reza Jalayer , Yuxin Chen , Masoud Jalayer , Carlotta Orsenigo , Masayoshi Tomizuka

Reliable detection and segmentation of human hands are critical for enhancing safety and facilitating advanced interactions in human–robot collaboration. Current research predominantly evaluates hand segmentation under in-distribution (ID) data, which reflects the training data of deep learning (DL) models. However, this approach fails to address out-of-distribution (OOD) scenarios that often arise in real-world human–robot interactions. In this work, we make three key contributions: first we assess the generalization of deep learning (DL) models for hand segmentation under both ID and OOD scenarios, utilizing a newly collected industrial dataset that captures a wide range of real-world conditions including simple and cluttered backgrounds with industrial tools, varying numbers of hands (0 to 4), gloves, rare gestures, and motion blur. Our second contribution is considering both egocentric and static viewpoints. We evaluated the models trained on four datasets, i.e. EgoHands, Ego2Hands (egocentric mobile camera), HADR, and HAGS (static fixed viewpoint) by testing them with both egocentric (head-mounted) and static cameras, enabling robustness evaluation from multiple points of view. Our third contribution is introducing an uncertainty analysis pipeline based on the predictive entropy of predicted hand pixels. This procedure enables flagging unreliable segmentation outputs by applying thresholds established in the validation phase. This enables automatic identification and filtering of untrustworthy predictions, significantly improving segmentation reliability in OOD scenarios. For segmentation, we used a deep ensemble model composed of UNet and RefineNet as base learners. Our experiments demonstrate that models trained on industrial datasets (HADR, HAGS) outperform those trained on non-industrial datasets, both in segmentation accuracy and in their ability to flag unreliable outputs via uncertainty estimation. These findings underscore the necessity of domain-specific training data and show that our uncertainty analysis pipeline can provide a practical safety layer for real-world deployment.

在人机协作中，手部的可靠检测和分割对于提高安全性和促进高级交互至关重要。目前的研究主要是对in-distribution （ID）数据下的手部分割进行评估，这反映了深度学习（DL）模型的训练数据。然而，这种方法无法解决在现实世界人机交互中经常出现的分布外（OOD）场景。在这项工作中，我们做出了三个关键贡献：首先，我们利用新收集的工业数据集评估了ID和OOD场景下手部分割的深度学习（DL）模型的泛化，该数据集捕获了广泛的现实世界条件，包括简单和混乱的背景，工业工具，不同数量的手（0到4），手套，罕见手势和运动模糊。我们的第二个贡献是考虑了自我中心和静态观点。我们评估了四个数据集上训练的模型，即EgoHands， Ego2Hands（以自我为中心的移动相机），HADR和HAGS（静态固定视点），通过以自我为中心（头戴式）和静态相机进行测试，从而从多个角度进行鲁棒性评估。我们的第三个贡献是引入了一个基于预测手部像素的预测熵的不确定性分析管道。此过程通过应用验证阶段中建立的阈值来标记不可靠的分割输出。这可以自动识别和过滤不可信的预测，显著提高OOD场景中的分割可靠性。对于分割，我们使用由UNet和RefineNet组成的深度集成模型作为基础学习器。我们的实验表明，在工业数据集（HADR， HAGS）上训练的模型在分割精度和通过不确定性估计标记不可靠输出的能力方面优于在非工业数据集上训练的模型。这些发现强调了特定领域训练数据的必要性，并表明我们的不确定性分析管道可以为实际部署提供实用的安全层。

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引用次数: 0

Natural efficient gaits from Nonholonomic Locomotion Nonlinear Normal Mode (NL-NNM): The Pendrivencar case 非完整运动非线性正态模式（NL-NNM）的自然有效步态：Pendrivencar情况

IF 3.1 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Mechatronics

Pub Date : 2025-06-20 DOI: 10.1016/j.mechatronics.2025.103366

Mirado Rajaomarosata, Luc Jaulin, Lionel Lapierre, Simon Rohou

Bio-inspired robots remain far less energy-efficient than animals because conventional controllers impose trajectories that fight passive dynamics, whereas animals exploit resonance through natural nonlinear normal modes (NNM), whose periodic internal motions form a smooth 2D invariant surface; We ask how to define and compute the natural motions of a conservative locomotion system: propulsion arises only from no-slip constraints, and once initiated, a gait persists without actuation—like a frictionless pendulum. We tackle non-holonomic constraints on the Pendrivencar, a vehicle driven by a motorised pendulum with a cubic torsional spring; We introduce the Nonholonomic Locomotion - NNM (NL-NNM): extract a high-speed spectral seed – where chassis oscillations vanish and the pendulum is neutrally stable – refine the periodic orbit, and continue the resulting 2D invariant manifold via pseudo-arclength across three slow centre manifolds (stable for positive speed, neutral at zero, unstable for negative) from non-isolated rectilinear equilibria; We demonstrate the first NL-NNM for a moving non-holonomic robot: internal orbits produce a pendulum–chassis choreography whose energy-dependent frequency shifts and harmonic richness exceed linear predictions. Via geometric phase, each orbit yields undulatory straight-line motion. A dual-loop control simulation confirms autonomous path tracking with only the pendulum; Extending to dissipative regimes via non-linear resonant modes offers a path to high-efficiency locomotion in aquatic, aerial, legged, soft-bodied, and other robots.

仿生机器人仍然远不如动物节能，因为传统的控制器施加的轨迹与被动动力学作斗争，而动物通过自然非线性正常模式（NNM）利用共振，其周期性内部运动形成光滑的二维不变表面；我们的问题是如何定义和计算一个保守运动系统的自然运动：推进只产生于无滑移约束，一旦启动，步态就会在没有驱动的情况下持续存在，就像一个无摩擦的钟摆。我们解决了Pendrivencar的非完整约束，Pendrivencar是一种由带有三次扭转弹簧的机动摆驱动的车辆；我们引入了非完整运动-NNM (NL-NNM)：提取高速光谱种子-底盘振荡消失，钟摆中立稳定-细化周期轨道，并通过非孤立直线平衡中的三个慢中心流形（正速度稳定，零中立，负不稳定）的伪弧长继续得到二维不变流形；我们展示了第一个用于移动非完整机器人的NL-NNM：内部轨道产生钟摆-底盘编排，其能量依赖的频移和谐波丰富度超过线性预测。通过几何相位，每个轨道产生波动的直线运动。双环控制仿真证实了单摆的自主路径跟踪；通过非线性共振模式扩展到耗散状态，为水生、空中、腿式、软体和其他机器人的高效运动提供了途径。

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引用次数: 0

Learning an inverse thermodynamic model for Pneumatic Artificial Muscles control 学习气动人工肌肉控制的逆热力学模型

IF 3.1 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Mechatronics

Pub Date : 2025-06-19 DOI: 10.1016/j.mechatronics.2025.103359

G. Wang , R. Chalard , J.A. Cifuentes , M.T. Pham

Pneumatic Artificial Muscles (PAMs) are highly nonlinear actuators widely used in robotics, rehabilitation, and other dynamic applications. Their complex behavior poses significant challenges for traditional system identification methods. Although machine learning techniques have shown remarkable success in modeling nonlinear systems, their black-box nature often leads to interpretability issues and susceptibility to overfitting. This study proposes a novel hybrid modeling approach that combines the strengths of analytical models with neural networks to capture the inverse thermodynamic behavior of PAMs. The results demonstrate that the hybrid model outperformed both analytical and purely neural network models. The obtained models were further used for model-based control design and the results show that the application of hybrid model improved the tracking performance.

气动人造肌肉（PAMs）是一种高度非线性的驱动器，广泛应用于机器人、康复和其他动态应用中。它们的复杂行为对传统的系统识别方法提出了重大挑战。尽管机器学习技术在建模非线性系统方面取得了显著的成功，但它们的黑箱性质往往会导致可解释性问题和过度拟合的易感性。本研究提出了一种新的混合建模方法，将分析模型的优势与神经网络相结合，以捕获pam的逆热力学行为。结果表明，混合模型优于分析模型和纯神经网络模型。将得到的模型进一步用于基于模型的控制设计，结果表明混合模型的应用提高了跟踪性能。

引用次数: 0

Robotized Incremental Sheet Forming trajectory control using deep neural network for force/torque compensator and task-space error tracking controller 基于深度神经网络的力/扭矩补偿器和任务空间误差跟踪控制器的自动化增量成形轨迹控制

IF 3.1 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Mechatronics

Pub Date : 2025-06-19 DOI: 10.1016/j.mechatronics.2025.103360

Xuan Dung To , Jefferson Roman Blanco , Sandra Zimmer-Chevret , Ghinwa Ouaidat , Thibaut Raharijaona , Farid Noureddine , Micky Rakotondrabe

In Robotized Incremental Sheet Forming (ISF), achieving precise geometrical accuracy is a challenging task due to trajectory tool center point (TCP) position errors at the forming tool attached to the robot’s end-effector. These errors primarily arise from external disturbance forces and torques generated during the interaction between the forming tool and the elastic metal sheet. While joint-torque space controllers can mitigate reaction forces and torques through dynamic modeling, joint-space control has inherent limitations, particularly for industrial high-load robots like the ABB IRB 8700. To overcome these challenges, this work implements an external force/torque (F/T) compensator in task-space using a deep neural network. The network predicts trajectory errors induced by reaction forces and torques measured via a 6-axis F/T sensor. Additionally, the forming tool’s trajectory is precisely monitored using a laser tracker, which serves as a feedback mechanism in a closed-loop task-space error-tracking controller. This controller detects and corrects trajectory deviations in real time. By integrating the F/T compensator and the task-space error-tracking controller, the proposed approach effectively compensates for reaction forces and torques while addressing additional errors introduced by other process-related factors. This integration results in significantly enhanced accuracy in robotic incremental forming processes.

在机器人增量成形（ISF）中，由于机器人末端执行器上的成形工具存在轨迹刀具中心点（TCP）位置误差，实现精确的几何精度是一项具有挑战性的任务。这些误差主要是由成形工具与弹性金属板相互作用过程中产生的外部扰动力和扭矩引起的。虽然关节-扭矩空间控制器可以通过动态建模来减轻反作用力和扭矩，但关节-空间控制具有固有的局限性，特别是对于像ABB IRB 8700这样的工业高负载机器人。为了克服这些挑战，本工作使用深度神经网络在任务空间中实现了外部力/扭矩（F/T）补偿器。该网络预测由反作用力和通过6轴F/T传感器测量的扭矩引起的轨迹误差。此外，使用激光跟踪器精确监测成形工具的轨迹，该跟踪器作为闭环任务空间误差跟踪控制器中的反馈机制。该控制器实时检测和纠正轨迹偏差。通过集成F/T补偿器和任务空间误差跟踪控制器，该方法有效地补偿了反作用力和扭矩，同时解决了由其他过程相关因素引入的附加误差。这种集成结果显著提高了机器人增量成形过程的精度。

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引用次数: 0

Adaptive backstepping finite-time output feedback control for path tracking of autonomous vehicle with asymmetric dead-zone 非对称死区自动驾驶汽车路径跟踪的自适应反演有限时间输出反馈控制

IF 3.1 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Mechatronics

Pub Date : 2025-06-17 DOI: 10.1016/j.mechatronics.2025.103362

Sucai Zhang , Yongfu Wang , Gang Li

A finite time adaptive output feedback control scheme with state constraint is proposed for the path tracking control of autonomous vehicle considering the asymmetric dead-zone. Firstly, the vehicle dynamics model and path tracking model are established by combining the dead-zone model, and the adaptive law is designed to approximate the parameters of dead-zone model. On this basis, an adaptive backstepping controller with output-constrained feedback control is designed by combining the filtering error compensation mechanism and the finite time technique, introducing the barrier Lyapunov function and the backstepping control technique. In order to save communication resources, a dynamic threshold event triggering mechanism is introduced. Finally, a rigorous stability analysis based on Lyapunov stability theory is presented to ensure that all signals of the closed-loop system are bounded in finite time. The effectiveness of the proposed method is verified by different simulations, hardware-in-the-loop experiments and real-time vehicle experiments. The results show that the proposed method is effective under different working conditions. The results of real-time vehicle experiments show that the controller can effectively improve the accuracy of path tracking control and reduce the maximum lateral position error to 0.1752 m compared with other methods, and the scheme can provide a theoretical reference for the control practice of autonomous vehicle.

针对非对称死区问题，提出了一种带状态约束的有限时间自适应输出反馈控制方案。首先，结合死区模型建立车辆动力学模型和路径跟踪模型，设计自适应律逼近死区模型参数；在此基础上，将滤波误差补偿机制与有限时间技术相结合，引入势垒Lyapunov函数和反演控制技术，设计了具有输出约束反馈控制的自适应反演控制器。为了节省通信资源，引入了动态门限事件触发机制。最后，基于李雅普诺夫稳定性理论进行了严格的稳定性分析，以保证闭环系统的所有信号在有限时间内是有界的。通过不同的仿真、硬件在环实验和实时车辆实验验证了该方法的有效性。结果表明，该方法在不同工况下都是有效的。实时车辆实验结果表明，与其他方法相比，该控制器能有效提高路径跟踪控制精度，最大横向位置误差降至0.1752 m，该方案可为自动驾驶车辆的控制实践提供理论参考。

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引用次数: 0