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王健安 , 闫慧娴 , 赵志诚 . 事件触发策略下多移动机器人抗干扰固定时间编队控制 [J ] . 电子学报 , 2023 , 51 ( 5 ): 1256 - 1265 . DOI: 10.12263/DZXB.20211610 http://doi.org/10.12263/DZXB.20211610 为节约有限通信资源，针对不确定多移动机器人系统的编队控制问题，提出一种基于抗干扰观测器的事件触发固定时间编队控制方法.首先，设计了一类自适应固定时间滑模干扰观测器（Adaptive Fixed-time Sliding Mode Disturbance Observer，AFSMDO），用来估计系统模型参数不确定和未知扰动.其次，在干扰观测器的基础上，结合反步法和固定时间理论，引入事件触发机制，设计固定时间分布式编队控制器，实现多机器人系统固定时间编队且收敛时间不依赖于系统初始状态，并经理论推导无Zeno行为.最后，通过对三个移动机器人进行编队仿真研究，验证了方法的有效性.

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李睿 , 项昌乐 , 王超 , 等 . 自动驾驶履带车辆鲁棒自适应轨迹跟踪控制方法 [J ] . 兵工学报 , 2021 , 42 ( 6 ): 1128 - 1137 . DOI: 10.3969/j.issn.1000-1093.2021.06.002 http://doi.org/10.3969/j.issn.1000-1093.2021.06.002 针对野外环境中自动驾驶履带车辆轨迹跟踪控制问题，考虑建模误差、参数不确定性及外界随机强干扰，以强鲁棒性及精确跟踪为目标，提出一种基于误差符号鲁棒积分的自动驾驶履带车辆鲁棒自适应轨迹跟踪控制方法。基于拉格朗日动力学方程建立自动驾驶履带车辆的运动学与动力学耦合模型；采用自适应控制方法实现对模型的精确前馈补偿，抵消模型非线性的影响；通过误差符号鲁棒积分有效抑制外界干扰及不确定性的影响；利用Lyapunov稳定性理论证明了闭环系统的全局渐进稳定性与收敛性。对仿真结果进行了实车实验一致性验证。仿真和实验结果证明：该方法在存在建模误差、参数不确定性、外界干扰条件下，在实现自动驾驶履带车辆高精度轨迹跟踪控制的同时，具有较强的自适应和鲁棒性。

LI R , XIANG C L , WANG C , et al. Robust adaptive trajectory tracking control approach for autonomous tracked vehicles [J ] . Acta Armamentarii , 2021 , 42 ( 6 ): 1128 - 1137 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2021.06.002 http://doi.org/10.3969/j.issn.1000-1093.2021.06.002 A robust adaptive trajectory tracking control approach based on robust integral of sign of error is presented for the trajectory tracking control of autonomous tracked vehicles in the field environment. In the proposed approach, the modelling errors, parametric uncertainties, and external random and strong disturbances are taken into account. A kinematic and dynamic coupling model of autonomous tracked vehicles is established based on Lagrangian dynamical equation. The feedforward compensation of the established model is realized by adaptive control approach, and the external disturbances and uncertainties can be suppressed by using the robust integral of sign of error. And then the asymptotical global stability and convergence of the closed loop system is demonstrated by Lyapunov stability theory. The simulated results were verified through real vehicle test. Simulated and experimental results show that the proposed approach can be used to realize the high accuracy trajectory tracking and insure the adaptiveness and robustness for autonomous tracked vehicles in the presence of modelling errors, parametric uncertainties and external disturbances.

NING B D , HAN Q L , ZUO Z Y , et al. Fixed-time and prescribed-time consensus control of multiagent systems and its applications: a survey of recent trends and methodologies [J ] . IEEE Transactions on Industrial Informatics , 2023 , 19 ( 2 ): 1121 - 1135 . DOI: 10.1109/TII.2022.3201589 http://doi.org/10.1109/TII.2022.3201589 https://ieeexplore.ieee.org/document/9866828/ https://ieeexplore.ieee.org/document/9866828/

ZHANG H G , FENG T , LIANG H J , et al. LQR-based optimal distributed cooperative design for linear discrete-time multiagent systems [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2017 , 28 ( 3 ): 599 - 611 . DOI: 10.1109/TNNLS.2015.2490072 http://doi.org/10.1109/TNNLS.2015.2490072 In this paper, a novel linear quadratic regulator (LQR)-based optimal distributed cooperative design method is developed for synchronization control of general linear discrete-time multiagent systems on a fixed, directed graph. Sufficient conditions are derived for synchronization, which restrict the graph eigenvalues into a bounded circular region in the complex plane. The synchronizing speed issue is also considered, and it turns out that the synchronizing region reduces as the synchronizing speed becomes faster. To obtain more desirable synchronizing capacity, the weighting matrices are selected by sufficiently utilizing the guaranteed gain margin of the optimal regulators. Based on the developed LQR-based cooperative design framework, an approximate dynamic programming technique is successfully introduced to overcome the (partially or completely) model-free cooperative design for linear multiagent systems. Finally, two numerical examples are given to illustrate the effectiveness of the proposed design methods.

魏连震 , 龚建伟 , 陈慧岩 , 等 . 基于强化学习补偿的地面无人战车行进间跟瞄自适应控制 [J ] . 兵工学报 , 2022 , 43 ( 8 ): 1947 - 1955 . DOI: 10.12382/bgxb.2021.0786 http://doi.org/10.12382/bgxb.2021.0786 针对底盘运动和路面起伏对地面无人战车行进间跟瞄带来的非线性干扰问题，提出一种 基于强化学习补偿的地面无人战车行进间跟瞄自适应控制方法。该跟瞄控制方法由主控制器与补偿控制器两部分构成，主控制器利用PID控制算法结合当前跟瞄误差得到主控制量，补偿控制器利用Dueling Q 网络强化学习算法对战车当前状态和局部规划路径附近的路面起伏信息进行处理得到补偿控制量。建立地面无人战车一体化运动学模型，对基于强化学习的补偿控制算法进行阐述；基于V-REP动力学软件在三维场景中进行仿真验证。实验结果表明：基于强化学习补偿的跟瞄控制方法对底盘运动和路面起伏具备较好的自适应能力，有效地提升了无人战车行进间跟瞄的准确性与稳定性。

WEI L Z , GONG J W , CHEN H Y , et al. Tracking and aiming adaptive control for unmanned combat ground vehicle on the move based on reinforcement learning compensation [J ] . Acta Armamentarii , 2022 , 43 ( 8 ): 1947 - 1955 . (in Chinese)

张炳力 , 佘亚飞 . 基于深度强化学习的轨迹跟踪横向控制研究 [J ] . 合肥工业大学学报(自然科学版) , 2023 , 46 ( 7 ): 865 - 872 .

ZHANG B L , SHE Y F . Research on lateral control of trajectory tracking based on deep reinforcement learning [J ] . Journal of Hefei University of Technology (Natural Science) , 2023 , 46 ( 7 ): 865 - 872 . (in Chinese)

KOBER J , BAGNELL A J , PETERS J . Reinforcement learning in robotics: A survey [J ] . The International Journal of Robotics Research , 2013 , 32 ( 11 ): 1238 - 1274 . DOI: 10.1177/0278364913495721 http://doi.org/10.1177/0278364913495721 http://journals.sagepub.com/doi/10.1177/0278364913495721 http://journals.sagepub.com/doi/10.1177/0278364913495721 Reinforcement learning offers to robotics a framework and set of tools for the design of sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in reinforcement learning. The relationship between disciplines has sufficient promise to be likened to that between physics and mathematics. In this article, we attempt to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots. We highlight both key challenges in robot reinforcement learning as well as notable successes. We discuss how contributions tamed the complexity of the domain and study the role of algorithms, representations, and prior knowledge in achieving these successes. As a result, a particular focus of our paper lies on the choice between model-based and model-free as well as between value-function-based and policy-search methods. By analyzing a simple problem in some detail we demonstrate how reinforcement learning approaches may be profitably applied, and we note throughout open questions and the tremendous potential for future research.

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