共查询到16条相似文献,搜索用时 171 毫秒
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利用零航速减摇鳍系统实现对海洋机器人在近水面低速航行时的横摇姿态控制. 基于零航速减摇鳍的非线 性动态特性和海洋机器人横摇解耦模型, 提出具有主从结构的横摇减摇控制规律. 设计具有积分滑模面的变结构控 制规律, 估算系统期望横摇扶正力矩, 并进一步结合非线性跟踪控制理论和反馈线性化方法, 建立减摇鳍子系统模 型, 设计从属控制规律驱动减摇鳍产生实际横摇稳定力矩. 仿真结果和理论分析表明, 所设计的控制规律是稳定且有 效的.
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零航速下鳍上的水动力与鳍角,角速度和角加速度存在记忆非线性关系,同时船舶横摇模型本身亦具有非线性和不确定性.导致常规控制方式无法直接求取控制量,且不能适应变化的模型和海况.本文通过主从控制器来分离减摇鳍系统的输出和输入非线性,对分离后的系统,采用基于径向基网络的逆控制构成主控制器来求取中间控制量,并自适应横摇模型和海况的变化.采用广义回归神经网络来逼近中间控制量到鳍角的映射作为从控制器.仿真结果表明该方法对横摇模型的不确定性具有自适应性,并可提高在变海况和高海况下的减摇效果. 相似文献
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为了满足工程船舶对低航速下减摇的需求,提高船舶在锚泊和低航速下的耐波性,提出减摇鳍在低航速下的控制策略,重点针对低航速下升力与鳍角之间相位关系对航速十分敏感的问题和船舶横摇模型的不确定性问题进行研究.通过数值迭代解决低航速下升力相位对航速的敏感性和升力与鳍角的非线性关系问题,相位调节器可以克服航速测量误差的影响,保证最佳的相位匹配;利用分数阶鲁棒控制器保证模型参数变化时整个系统的控制性能.对所设计的控制策略进行数值仿真,仿真数据表明,在随机海洋环境干扰下,减摇鳍可以获得满意的低航速减摇能力.进行船模水池试验,试验结果也表明,所设计的控制策略可以使减摇鳍在锚泊和低航速下产生满意的减摇效果. 相似文献
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利用一种非线性干扰观测器观测减摇鳍系统的不确定性和随机海浪干扰,通过选择设计参数使观测误差指数收敛.针对引入非线性干扰观测器后的系统采用滑模反演法设计控制器,控制律的设计保证了闭环系统的稳定性.仿真结果表明,在不同浪向角和航速的各种海况下采用该控制策略,系统均能取得较好的减摇效果,同时能很好地克服对象的不确定性和随机海浪干扰,鲁棒性较强. 相似文献
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针对一类具有全状态约束、未建模动态和动态扰动的严格反馈非线性系统,通过构造非线性滤波器,并利用Young’s不等式,提出一种新的有限时间自适应动态面控制方法.引入非线性映射处理全状态约束,将有约束系统变成无约束系统,利用径向基函数逼近未知光滑函数,利用辅助系统产生的动态信号处理未建模动态.对于变换后的系统,利用改进的动态面控制和有限时间方法设计的控制器结构简单,移去现有有限时间控制中出现的“奇异性”问题,可加快系统的收敛速度.理论分析表明,闭环系统中的所有信号在有限时间内有界,全状态不违背约束条件.数值算例的仿真结果表明,所提出的自适应动态面控制方案是有效的. 相似文献
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In this paper, an adaptive optimal control strategy is proposed for a class of strict‐feedback nonlinear systems with output constraints by using dynamic surface control. The controller design procedure is divided into two parts. One is the design of feedforward controller and the other is the design of optimal controller. To guarantee the satisfaction of output constraints in feedforward controller, nonlinear mapping is utilized to transform the constrained system into an unconstrained system. Neural‐network based adaptive dynamic programming algorithm is employed to approximate the optimal cost function and the optimal control law. By theoretical analysis, all the signals in the closed‐loop system are proved to be semi‐globally uniformly ultimately bounded and the output constraints are not violated. A numerical example illustrates the effectiveness of the proposed scheme. 相似文献
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仿生机器鱼胸/尾鳍协同推进闭环深度控制 总被引:1,自引:0,他引:1
为改善机器鱼定深控制过程中的动态性能与稳态性能,根据深度误差的大小将定深控制过程分解为趋近阶段与巡游阶段,给出了一种基于中枢模式发生器(central pattern generator,CPG)与模糊控制相结合的闭环运动控制方法.为此,首先建立了以压力传感器信号为反馈输入,通过模糊控制器调节控制参数的CPG运动控制模型.在此基础上,针对误差较大的趋近阶段,采用胸/尾鳍协同方式,通过趋近模糊控制器改变摇翼关节的偏置量与幅值来使机器鱼快速到达期望深度;针对误差较小的巡游阶段,采用改变攻角方式,通过巡游模糊控制器改变胸鳍攻角来使机器鱼保持在期望深度.两阶段之间通过胸鳍CPG的启停实现切换.模糊控制器设计时利用了基于最小二乘法对实验数据拟合而得出的俯仰角变化率与控制参数的近似关系,提高了机器鱼趋向期望深度的速度并减小了在期望深度巡游时的稳态误差.仿真与实验结果验证了所提控制方法的有效性. 相似文献
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针对基于障碍Lyapunov函数的非线性约束系统反推控制中, 控制器结构复杂、约束量初值选取区间小、会引入额外参数等问题, 提出了一种新的基于非线性映射的自适应反推控制方案. 该方法扩大约束量的初值选取区间为整个约束区间, 增加了系统初值选取和控制器设计的便易性. 约束量被映射至实数空间中, 因此映射后的新系统可以直接应用反推法设计控制器, 简化了控制器结构且不会引入额外参数. 证明了映射前后系统具有一致的收敛性, 保证闭环系统所有信号一致有界, 并且跟踪误差渐近收敛于零. 仿真结果进一步验证了本文方法的有效性. 相似文献
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In this article, an optimal command-filtered backstepping control approach is proposed for uncertain strict-feedback nonlinear multi-agent systems (MASs) including output constraints and unmodeled dynamics. One-to-one nonlinear mapping (NM) is utilized to recast constrained systems as corresponding unrestricted systems. A dynamical signal is applied to cope with unmodeled dynamics. Based on dynamic surface control (DSC), the feedforward controller is designed by introducing error compensating signals. The optimal feedback controller is produced applying adaptive dynamic programming (ADP) and integral reinforcement learning (IRL) techniques in which neural networks are utilized to approximate the relevant cost functions online with established weight updating laws. Therefore, the entire controller, including feedforward and feedback controllers, not only ensures that all signals in the closed-loop systems are cooperative semi-globally uniformly ultimately bounded (SGUUB) and the outputs maintain in the provided time-varying constraints, but also makes sure that the cost functions achieve minimization. A simulation example is presented to illustrate the feasibility of the proposed control algorithm. 相似文献
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This paper studies the output feedback tracking control problem for a class of strict‐feedback uncertain nonlinear systems with full state constraints and unmodeled dynamics using a prescribed performance adaptive neural dynamic surface control design approach. A nonlinear mapping technique is employed to address the state constraints. Radial basis function neural networks are utilized to approximate the unknown nonlinear functions. The unmodeled dynamics is addressed by introducing an available dynamic signal. Subsequently, we construct the controller and parameter adaptive laws using a backstepping technique. Based on Lyapunov stability theory, it is shown that all signals in the closed‐loop system are semiglobally uniformly ultimately bounded and that the tracking error always remains within the prescribed performance bound. Simulation results are presented to demonstrate the effectiveness of the proposed control scheme. 相似文献