用于二维源搜索的梯度自适应极值搜索算法

基于正弦扰动的二维源极值搜索算法存在着适应性差和快速性与准确性相互制约的缺点。针对这一问题,提出一种基于梯度估计的参数自适应极值搜索算法,该算法在传统极值搜索算法基础上,通过三个历史采样点估计当前区域的梯度,并依据当前区域梯度值自适应调整反馈增益参数。此外,利用平均值理论对所提算法进行了理论分析和收敛性证明。不同环境下的仿真对比表明本方法提高了源搜索效率和对复杂梯度环境的适应性。     

非完整约束移动机器人的轨迹跟踪控制

针对非完整约束移动机器人运动学与动力学模型,根据轨迹跟踪控制目标的需要,设计了一种简单的控制器,该控制器结合了运动学控制器设和动力学控制器两部分;针对运动学模型,采用自适应算法对其未知参数进行估计,针对系统动力学模型,采用单层神经网络算法克服未知扰动对系统稳定性的影响,使速度误差尽可能地缩小;在Lyapunov稳定性理论的基础上证明了系统的收敛性和稳定性,该控制算法简单有效,易于实现;仿真结果表明:该控制策略可以实现对移动机器人期望轨迹的稳定跟踪,验证了算法的有效性。     

非完整四轮式移动机器人反演轨迹跟踪控制

根据非完整约束的四轮式移动机器人的运动学模型,对其轨迹跟踪控制策略进行了深入研究;采用反演控制的相关理论和方法,设计了移动机器人轨迹跟踪控制策略;该控制策略将系统分解为低阶子系统来进行处理,利用中间虚拟控制量和部分Lyapunov函数简化了控制器设计,并且具有全局渐近稳定性;此外,在控制策略中对相应的控制量设置了阈值,保证了移动机器人运动的平滑性;最后,对所设计控制器的稳定性和平滑性进行了仿真实验,验证了其正确性和有效性。     

面向气味跟踪与定位的机器人变步长搜索算法

面向在地面搜索地下气味源的任务,针对传统六边形路径搜索算法的不足,提出移动机器人依靠气体传感器的气味跟踪与气味源定位变步长搜索算法,并进行了非均匀土壤中实际扩散情况下的计算机仿真,证实改进算法是可行的、优越的和实用的,具有气味跟踪与气味源定位双重功能。     

基于AUPF的TDOA几何定位跟踪算法研究

使用无源时差（TDOA）定位技术确定无人机等小型辐射源目标的位置是当前研究的热点,针对时差定位算法较为复杂的实际情况,推导了时差双曲线的几何解,并提出了一种基于自适应无迹粒子滤波（AUPF）技术的移动目标定位跟踪方法。通过仿真对该方法在不同场景的应用效果进行了验证,进一步比较分析了算法的定位精度。结果表明,基于自适应无迹粒子滤波的时差几何定位跟踪算法可以在多种情况下较好地拟合出目标真实运动轨迹,实现对运动目标的定位跟踪,同时拥有更低的定位误差和更高的轨迹包容度,使用该方法可以显著提高对非合作移动辐射源目标的位置估计性能。     

基于解耦控制的非完整移动机器人实时轨迹跟踪

本文研究了非完整两轮移动机器人的实时轨迹跟踪问题．首先,在介绍了一般的非线 性系统的输入 输出线性化方法的基础上,并研究了在两轮驱动移动机器人轨迹跟踪中的应 用;然后在研制的非完整两轮驱动移动机器人的实验平台上对该算法进行了实时轨迹跟踪实 验,结果表明了该非线性跟踪控制算法的有效性．     

基于激光传感器移动机器人的目标定位和多模式跟踪

移动机器人对运动目标的感知和跟踪是实现机器人与环境交互的一项重要能力。针对移动机器人以人为目标的跟踪中在复杂动态环境下经常出现的目标丢失和跟踪模式单一的问题,提出了基于机器学习的人物目标识别算法。该算法可以处理复杂环境下的目标检测和定位。同时设计了交互多模型跟踪算法,可以较好的跟踪以不规律模式运动的目标。最后在交龙移动机器人平台上实现了整个系统,验证了人物目标检测和多模式跟踪算法的鲁棒性和优越性。     

具有参数不确定性的轮式移动机器人自适应backstepping控制

针对参数不确定的轮式移动机器人的轨迹跟踪问题,设计自适应跟踪控制器.基于移动机器人的动力学模型,采用backstepping积分方法,通过逐步递推选择适当的Lyapunov函数,设计基于状态反馈的自适应控制器,并进行了相应的稳定性分析.与传统PID控制进行仿真对比,结果表明提出的自适应控制策略能较好地补偿系统参数摄动的影响,提高了移动机器人的轨迹跟踪性能和鲁棒性.     

轮子纵向打滑条件下的移动机器人自适应跟踪控制

针对纵向滑动参数未知的轮式移动机器人的轨迹跟踪问题,提出一种自适应跟踪控制策略.利用两个未知参数来描述移动机器人左右轮的纵向打滑程度,建立了产生纵向滑动的差分驱动轮式移动机器人的运动学模型;设计了补偿纵向滑动的自适应非线性反馈控制律;应用 Lyapunov 稳定性理论与 Barbalat 定理证明了闭环系统的稳定性;同时,提出了一种由极点配置方法在线调整控制器增益的方法.仿真结果验证了所提出控制方法的有效性.     

基于深度相机的移动机器人视觉跟踪控制研究

基于视觉利用移动机器人进行运动目标跟踪,该文提出一种基于二自由度云台和RGB-D相机的运动目标视觉跟踪及移动机器人路径实时规划、跟踪方法。该方法利用核相关滤波算法在图像中实时追踪目标,控制二自由度云台使深度相机实时对准目标,并根据深度相机得到目标的深度信息,利用坐标转换得到目标相对于机器人的位置信息;其后移动机器人根据目标的位置信息,基于五次多项式进行路径规划;最后采用李雅普诺夫控制律对移动机器人进行轨迹跟踪控制,使得机器人能够平稳地跟踪目标运动。该算法在阿克曼移动机器人上进行了实验,实验结果验证了算法的有效性和实时性。     

Distributed extremum seeking and formation control for nonholonomic mobile network

In this paper, an integrated control and optimization problem is studied in the context of formation and coverage of a cluster of nonholonomic mobile robots. In particular, each communication channel is modeled by its outage probability, and hence, connectivity is maintained if the outage probability is less than a certain threshold. The objective of the communication network is to not only maintain resilient communication quality but also extend the network coverage. An information theory based performance index is defined to quantify this control objective. Unlike most of the existing results, the proposed cooperative control design does not assume the knowledge of any gradient (of the performance index). Rather, a distributed extremum seeking algorithm is designed to optimize the connectivity and coverage of the mobile network. The proposed approach retains all the advantages of cooperative control, and it can not only perform extremum seeking individually, but also ensures a consensus of estimates between any pair of connected systems. Simulation results demonstrate effectiveness of the proposed methodology.     

Robust adaptive control for mobile manipulators

This paper addresses the trajectory tracking control of a nonholonomic wheeled mobile manipulator with parameter uncertainties and disturbances. The proposed algorithm adopts a robust adaptive control strategy where parametric uncertainties are compensated by adaptive update techniques and the disturbances are suppressed. A kinematic controller is first designed to make the robot follow a desired end-effector and platform trajectories in task space coordinates simultaneously. Then, an adaptive control scheme is proposed, which ensures that the trajectories are accurately tracked even in the presence of external disturbances and uncertainties. The system stability and the convergence of tracking errors to zero are rigorously proven using Lyapunov theory. Simulations results are given to illustrate the effectiveness of the proposed robust adaptive control law in comparison with a sliding mode controller.     

Extremum seeking on submanifolds in the Euclidian space

Extremum seeking is a powerful control method to steer a dynamical system to an extremum of a partially unknown function. In this paper, we introduce extremum seeking systems on submanifolds in the Euclidian space. Using a trajectory approximation technique based on Lie brackets, we prove that uniform asymptotic stability of the so-called Lie bracket system on the manifold implies practical uniform asymptotic stability of the corresponding extremum seeking system on the manifold. We illustrate the approach with an example of extremum seeking on a torus.     

Adaptive extremum-seeking control of a continuous stirred tank bioreactor with Haldane's Kinetics

In this paper, we present an adaptive extremum seeking control scheme for a continuous stirred tank bioreactor with Haldane's kinetics. The proposed adaptive extremum seeking approach uses the kinetic model of the bioreactor to construct a seeking algorithm that drives the system states to the desired set-points that extremize the value of an objective function. Lyapunov's stability theorem is used in the design of the extremum seeking controller and the development of the parameter updating laws. Simulation experiments are given to show the effectiveness of the proposed approach.     

On-line optimization of fedbatch bioreactors by adaptive extremum seeking control

In this paper, we present an adaptive extremum seeking control scheme for fed-batch bioreactors with Haldane kinetics. The proposed adaptive extremum seeking approach utilizes the structure information of the process kinetics to derive a seeking algorithm that drives the system states to the desired setpoints that maximize the biomass production. It assumes that only the substrate concentration is available for on-line measurement. Lyapunov stability is used in the design of the extremum seeking controller structure and the development of the parameter learning laws. The performance of the approach is illustrated via numerical simulations.     

Adaptive extremum seeking control of nonlinear dynamic systems with parametric uncertainties

We pose and solve an extremum seeking control problem for a class of nonlinear systems with unknown parameters. Extremum seeking controllers are developed to drive system states to the desired set-points that extremize the value of an objective function. The proposed adaptive extremum seeking controller is “inverse optimal” in the sense that it minimizes a meaningful cost function that incorporates penalty on both the performance error and control action. Simulation studies are provided to verify the effectiveness of the proposed approach.     

Source seeking with non-holonomic unicycle without position measurement and with tuning of forward velocity

We consider the problem of seeking the source of a scalar signal using an autonomous vehicle modeled as the non-holonomic unicycle and equipped with a sensor of that scalar signal but not possessing the capability to sense either the position of the source nor its own position. We assume that the signal field is the strongest at the source and decays away from it. The functional form of the field is not available to our vehicle. We employ extremum seeking to estimate the gradient of the field in real time and steer the vehicle towards the point where the gradient is zero (the maximum of the field, i.e., the location of the source). We employ periodic forward–backward movement of the unicycle (implementable with mobile robots and some underwater vehicles but not with aircraft), where the forward velocity has a tunable bias term, which is appropriately combined with extremum seeking to produce a net effect of “drifting” towards the source. In addition to simulation results we present a local convergence proof via averaging, which exhibits a delicate periodic structure with two sinusoids of different frequencies—one related to the angular velocity of the unicycle and the other related to the probing frequency of extremum seeking.     

Adaptive extremum seeking control of continuous stirred tank bioreactors with unknown growth kinetics

In this paper, we present an adaptive extremum seeking control scheme for continuous stirred tank bioreactors. We assume limited knowledge of the growth kinetics. An adaptive learning technique is introduced to construct a seeking algorithm that drives the system states to the desired set-points that maximize the value of an objective function. Lyapunov's stability theorem is used in the design of the extremum seeking controller structure and the development of the parameter learning laws. Simulation results are given to show the effectiveness of the proposed approach.     

基于梯度下降和二分法的移动机器人轨迹规划方法

轨迹规划是移动焊接机器人轨迹控制的基础,是该系统中重要的组成部分。为了提高多关节移动焊接机器人轨迹规划的效率和精确性,同时考虑到多关节焊接机器人的运动特性提出了一种梯度下降法和二分法结合的轨迹规划方法。移动焊接机器人由机械杆和机器人移动平台组成,由于移动平台提供移动性使得移动焊接机器人相对固定的机械臂有更大的工作空间。近年来,此类系统的研究已在经学术界和工业界迅猛发展。论文首先建立移动焊接机器人的运动学模型,并且阐述梯度下降法和二分法结合算法的设计步骤。然后,采用典型的正弦波形作为焊缝轨迹,通过仿真验证该方法的应用前景和可行性。