Mini Rotorcraft Flight Formation Control Using Bounded Inputs

In this paper, the flight formation control and trajectory tracking control design of multiple mini rotorcraft systems are discussed. The dynamic model of a mini rotorcraft is presented using the Newton-Euler formalism. Our approach is based on a leader/follower structure of multiple robot systems. The centroid of the coordinated control subsystem is used for trajectory tracking purposes. A nonlinear controller based on separated saturations and a multi-agent consensus algorithm is developed. The analytic results are supported by simulation tests. Experimental results include yaw coordination and tracking only.     

一种模型参考鲁棒控制的跟踪性能改善

This paper considers the tracking performance problem of a model reference robust control (MRRC) for plants with relative degree greater than one. A new algorithm is proposed based on the earlier research. It is shown that by applying a special transformation to the tracking system, the L1 bound of the tracking error can be achieved even when the high frequency gain is unknown, and both the tracking performance and the control effort can be improved significantly. Furthermore, the strictly positive real (SPR) condition, which is an essential assumption of the earlier design, can be removed.     

Robust face tracking control of a mobile robot using self‐tuning Kalman filter and echo state network

This paper presents a novel design of face tracking algorithm and visual state estimation for a mobile robot face tracking interaction control system. The advantage of this design is that it can track a user's face under several external uncertainties and estimate the system state without the knowledge about target's 3D motion‐model information. This feature is helpful for the development of a real‐time visual tracking control system. In order to overcome the change in skin color due to light variation, a real‐time face tracking algorithm is proposed based on an adaptive skin color search method. Moreover, in order to increase the robustness against colored observation noise, a new visual state estimator is designed by combining a Kalman filter with an echo state network‐based self‐tuning algorithm. The performance of this estimator design has been evaluated using computer simulation. Several experiments on a mobile robot validate the proposed control system. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Research on unmanned electric shovel autonomous driving path tracking control based on improved pure tracking and fuzzy control

This paper proposes a path tracking control method combining pure tracking algorithms and self-adaptive fuzzy control for autonomous driving of an unmanned electric shovel. An improved pure tracking controller was designed based on the kinematic model of heavy crawler taking both the value of deviation and its variation as inputs with the crawler speed on each side as output. The proposed controller and MPC algorithm were simulated using MATLAB for comparison. The results show that the proposed controller has more anthropomorphic characteristics than the MPC method. To verify the actual control effect of the controller, experiments were carried out using a prototype electric shovel for different working conditions. The experimental results proved that the controller is able to meet the control requirements for unmanned electric shovel path tracking.     

基于反步自适应神经网络的船舶航迹控制

针对欠驱动船舶在稳定航速条件下轨迹跟踪问题,提出了一种基于自适应神经网络与反步法相结合的控制算法.该算法将实际的欠驱动船舶视为模型完全未知的非线性系统,利用神经网络的函数逼近特性实现控制器中非线性部分的在线估计,采用同时调整输入层-隐层、隐层-输出层间的权值阵的方法进行神经网络权值调整.通过选取积分型Lyapunov函数证明了闭环系统的稳定性.仿真实验表明该控制策略具有良好的跟踪特性,可以实现对期望航迹的精确跟踪.     

基于多尺度动态模型的自适应跟踪算法

This paper simply introduces multi-scale analysis and target-tracking theory, presents a better solution for Px(t), with theoretical proof of the solution's correctness. Based on multi-scale analytical idea, combing the dynamic system analysis of movement model with wavelet transform method, this paper puts forward a multi-scale self-adap-tive fusion tracking algorithm,and gives the concrete implementing steps of this algorithm, with simulation experi-ment. In the simulation experiment, makes a comparison between the algorithm and the traditional single-scale track-ing method. And the theoretical proof and the experiment results prove the algorithm to be both feasible and effec-tive. This algorithm adjusts tracking scale automatically according the state of model target. And it also can use de-tective data effectively and more accurately portray the variation of track. It avoids the disadvantages of single scaleand realizes the tracking towards dynamic model. Comparing with the traditional single tracking method, this algo-rithm more fits the needs of target tracking and has practical value.     

3D head tracking under partial occlusion

A new algorithm for 3D head tracking under partial occlusion from 2D monocular image sequences is proposed. The extended superquadric (ESQ) is used to generate a geometric 3D face model in order to reduce the shape ambiguity during tracking. Optical flow is then regularized by this model to estimate the 3D rigid motion. To deal with occlusion, a new motion segmentation algorithm using motion residual error analysis is developed. The occluded areas are successfully detected and discarded as noise. Furthermore, accumulation error is heavily reduced by a new post-regularization process based on edge flow. This makes the algorithm more stable over long image sequences. The algorithm is applied to both synthetic occlusion sequence and real image sequences. Comparisons with the ground truth indicate that our method is effective and is not sensitive to occlusion during head tracking.     

环卫车辆轨迹跟踪系统的无模型自适应迭代学习控制

针对环卫车辆周期重复性工作特点,考虑模型时变以及未知扰动问题,提出一种基于无模型自适应迭代学习的环卫车辆轨迹跟踪控制方法.首先,针对环卫车辆建立了两轮移动机器人的运动学模型,然后,给出带时变参数和非线性不确定项的迭代域下全格式动态线性化数据模型,引入时间差分估计算法,设计基于最优性能指标的轨迹跟踪无模型自适应迭代学习控制方法,并进行仿真分析.结果表明,环卫车轨迹跟踪系统车身角随迭代增加超调减小,与传统迭代学习控制算法相比,具有松弛的条件限制和较好的鲁棒性,同时提高了控制系统精度.     

Position control of a mobile inverted pendulum system using radial basis function network

This article presents the implementation of position control of a mobile inverted pendulum (MIP) system by using the radial basis function (RBF) network. The MIP has two wheels to move on the plane and to balance the pendulum. The MIP is a nonlinear system whose dynamics is nonholonomic. The goal of this study was to control the MIP to maintain the balance of the pendulum while tracking a desired position of the cart. The reference compensation technique scheme is used as a neural network control method for the MIP. The back-propagation learning algorithm of the RBF network is derived for online learning and control. The control algorithm has been embedded on a DSP 2812 board to achieve real-time control. Experimental results are conducted and show successful control performances of both balancing and tracking the desired position of the MIP.     

空间机械臂的鲁棒复合自适应控制

对于本体姿态受控而位置不受控的空间机械臂系统,考虑存在参数不确定性及非参数 不确定性,首先建立了系统的估计模型,并且提出了一种鲁棒复合自适应控制方法,其参数适应 律由估计误差和跟踪误差共同决定.证明了这种方法不仅可维持系统的全局渐近稳定,而且还 可快速收敛和减小跟踪误差.