Forwarding-based trajectory tracking control for nonlinear systems with bounded unknown disturbances

There are only a few methods for controlling high-order nonlinear systems, except for standard backstepping technique. Due to the fact that I&I theory proposed by Astolfi and Ortega is able to accomplish the system immersion by selecting a lower-order target system and a vital mapping, a bottom-up recursive procedure for designing tracking control laws for a class of n-dimensional strict-feedback nonlinear systems is focused in our work. The required mappings are transformed into virtual control inputs, and system order reduction is realized as design step is implemented repetitively. A first-order filter is employed at each step to compute analytic derivatives of mappings. The benefit of our proposed algorithm can not only improve the computation efficiency to simplify all controls’ forms, but also suppress the high-frequency noise. While the off-the-manifold coordinate is rendered insensitive to the time-varying and bounded but unknown disturbance, the robust stability can be ultimately guaranteed. A quadrotor helicopter is used to show designing procedures and controller performance via various simulations.     

Adaptive trajectory tracking despite unknown input delay and plant parameters

In a recent paper we presented the first adaptive control design for an ODE system with a possibly large actuator delay of unknown length. We achieved global stability under full state feedback. In this paper we generalize the design to the situation where, besides the unknown delay value, the ODE also has unknown parameters, and where trajectory tracking (rather than equilibrium regulation) is pursued.     

Smooth quadrotor trajectory generation for tracking a moving target in cluttered environments

In this paper,we present a trajectory generation method of a quadrotor,based on the optimal smoothing B-spline,for tracking a moving target with consideration o...     

不确定轮式移动机器人的任意轨迹跟踪

本文研究参数不确定轮式移动机器人的任意轨迹跟踪统一控制问题.通过引入坐标变换、输入变换和辅助动态,将机器人模型转换为合适的形式;进而运用Lyapunov方法和自适应技术设计了一种自适应统一控制器,该控制器可以保证跟踪误差全局一致最终有界,且最终界大小可以通过调整控制器参数而任意调节.仿真结果验证了控制律的有效性.     

Cooperative processing based on posture change detection and trajectory estimation for unknown multi-object tracking

ABSTRACT

Tracking of moving objects is a very important step in building an intelligent video surveillance system. The movement of non-rigid objects, appearance variations and luminosity changes make tracking even more difficult. This paper proposes a new automatic multi-target tracking system that can deal with the most confronted problems without any prior knowledge of the characteristics of objects. The system is a combination between classification, learning and tracking in a parallel architecture that allows the three tasks to be performed separately and efficiently to make the most of this combination. The permanent learning of the classifier guarantees the efficiency of the latter compared to the posture changes of moving objects. The classifier sends the new posture changes with a high degree of confidence as a new learning data. This cyclic aspect forces the system to adapt to all possible posture changes. In the case of occlusion, the system uses the estimated information of the trajectories to correct or cancel the learning process. The filtering process prevents the classifier from falling into a false classification, which significantly increases the system adaptability to the environment. Tests carried out on the CAVIAR and MOT16 datasets showed the efficiency and effectiveness of the proposed system.     

未知环境下基于虚拟角速度跟踪的避障策略EI北大核心CSCD

当智能体自主执行任务时,局部障碍物可测的未知环境增加了局部极值和执行器饱和发生的概率.对此,本文提出了虚拟角速度跟踪的避障策略.首先,基于简易障碍物的几何模型构造虚拟的避障引导角,并利用李雅普诺夫方法设计角速度控制律,通过受限制的虚拟角速度跟踪来实现避障控制.然后,引入方位因子改进距离型权值分配器,强化轨迹附近障碍物的影响以降低局部极值发生的概率.最后,对于不完全可测的复杂障碍物,根据历史探测信息建立以边界点为中心的简易障碍物模型.仿真结果表明,该策略能够避让低速动态障碍物及U型复杂障碍物,并且可实现抗饱和控制.     

Adaptive ILC for tracking non-repetitive reference trajectory of 2-D FMM under random boundary condition

Almost all of the existing research achievements in Iterative Learning Control (ILC) hitherto have been focused on One-Dimensional (1-D) dynamical systems. Few ILC researches are related to Two-Dimensional Fornasini Marchesina Model (2-D FMM). In this paper, an adaptive ILC approach is proposed for 2-D FMM system with non-repetitive reference trajectory under random boundary condition. The proposed adaptive ILC algorithm learns the coefficient matrices of the system and updates the control input iteratively. As the times of iteration goes to infinity, the ILC tracking error outside the boundary tends to zero and all system signals keep bounded in the whole ILC process. Illustrative examples are provided to verify the validity of the proposed adaptive ILC algorithm.     

Multi-object trajectory tracking

The majority of existing tracking algorithms are based on the maximum a posteriori solution of a probabilistic framework using a Hidden Markov Model, where the distribution of the object state at the current time instance is estimated based on current and previous observations. However, this approach is prone to errors caused by distractions such as occlusions, background clutters and multi-object confusions. In this paper, we propose a multiple object tracking algorithm that seeks the optimal state sequence that maximizes the joint multi-object state-observation probability. We call this algorithm trajectory tracking since it estimates the state sequence or “trajectory” instead of the current state. The algorithm is capable of tracking unknown time-varying number of multiple objects. We also introduce a novel observation model which is composed of the original image, the foreground mask given by background subtraction and the object detection map generated by an object detector. The image provides the object appearance information. The foreground mask enables the likelihood computation to consider the multi-object configuration in its entirety. The detection map consists of pixel-wise object detection scores, which drives the tracking algorithm to perform joint inference on both the number of objects and their configurations efficiently. The proposed algorithm has been implemented and tested extensively in a complete CCTV video surveillance system to monitor entries and detect tailgating and piggy-backing violations at access points for over six months. The system achieved 98.3% precision in event classification. The violation detection rate is 90.4% and the detection precision is 85.2%. The results clearly demonstrate the advantages of the proposed detection based trajectory tracking framework.     

Formation tracking of multi-vehicle systems in unknown environments using a multi-region control scheme

In this paper, a multi-region control scheme is proposed for a formation of nonholonomic vehicles to track a reference trajectory while avoiding collisions and preserving network connectivity in unknown environments. The proposed control scheme defines three regions, safe region, dangerous region and transition region. In different regions, priority is given to different control objectives. In safe region where trajectory tracking holds the priority, the proposed control scheme guarantees bounded tracking of the reference trajectory for each vehicle. In dangerous region where avoidance control is the main objective, a new bounded potential function is designed to characterise constraints of obstacle and inter-vehicle collision avoidance as well as connectivity maintenance. By introducing a series of transition functions, smooth switching between trajectory tracking and avoidance control is achieved in transition region. It has been proved that each vehicle can track its reference trajectory while satisfying the constraints simultaneously with a bounded controller which means that the proposed control scheme satisfies input constraints by properly tuning parameters. Simulation results demonstrate the effectiveness of the proposed method.     

On optimal constrained trajectory planning in 3D environments

A novel approach to generating acceleration-based optimal smooth piecewise trajectories is proposed. Given two configurations (position and orientation) in 3D, we search for the minimal energy trajectory that minimizes the integral of the squared acceleration, opposed to curvature, which is widely investigated. The variation in both components of acceleration: tangential (forces on gas pedal or brakes) and normal (forces that tend to drive a car on the road while making a turn) controls the smoothness of generated trajectories. In the optimization process, our objective is to search for the trajectory along which a free moving robot is able to accelerate (decelerate) to a safe speed in an optimal way. A numerical iterative procedure is devised for computing the optimal piecewise trajectory as a solution of a constrained boundary value problem. The resulting trajectories are not only smooth but also safe with optimal velocity (acceleration) profiles and therefore suitable for robot motion planning applications. Experimental results demonstrate this fact.     

Accurate ball trajectory tracking and 3D visualization for computer-assisted sports broadcast

The application of computer-aided controversial plays resolution in sport events significantly benefits organizers, referees and audience. Nowadays, especially in ball sports, very accurate technological solutions can be found. The main drawback of these systems is the need of complex and expensive hardware which makes them not affordable for less-known regional/traditional sports events. The lack of competitive systems with reduced hardware/software complexity and requirements motivates this research. Visual Analytics technologies permit system detecting the ball trajectory, solving with precision possible controversial plays. Ball is extracted from the video scene exploiting its shape features and velocity vector properties. Afterwards, its relative position to border line is calculated based on polynomial approximations. In order to enhance user visual experience, real-time rendering technologies are introduced to obtain virtual 3D reconstruction in quasi real-time. Comparing to other set ups, the main contribution of this work lays on the utilization of an unique camera per border line to extract 3D bounce point information. In addition, the system has no camera location/orientation limit, provided that line view is not occluded. Testing of the system has been done in real world scenarios, comparing the system output with referees’ judgment. Visual results of the system have been broadcasted during Basque Pelota matches.     

Efficient real-time trajectory tracking

Moving objects databases (MOD) manage trajectory information of vehicles, animals, and other mobile objects. A crucial problem is how to efficiently track an object’s trajectory in real-time, in particular if the trajectory data is sensed at the mobile object and thus has to be communicated over a wireless network. We propose a family of tracking protocols that allow trading the communication cost and the amount of trajectory data stored at a MOD off against the spatial accuracy. With each of these protocols, the MOD manages a simplified trajectory that does not deviate by more than a certain accuracy bound from the actual movement. Moreover, the different protocols enable several trade-offs between computational costs, communication cost, and the reduction in the trajectory data: Connection-Preserving Dead Reckoning minimizes the communication cost using dead reckoning, a technique originally designed for tracking an object’s current position. Generic Remote Trajectory Simplification (GRTS) further separates between tracking of the current position and simplification of the past trajectory and can be realized with different line simplification algorithms. For both protocols, we discuss how to bound the space consumption and computing time at the moving object and thereby present an effective compression technique to optimize the reduction performance of real-time line simplification in general. Our evaluations with hundreds of real GPS traces show that a realization of GRTS with a simple simplification heuristic reaches 85–90% of the best possible reduction rate, given by retrospective offline simplification. A realization with the optimal line simplification algorithm by Imai and Iri even reaches more than 97% of the best possible reduction rate.     

A novel trajectory planning-based adaptive control method for 3-D overhead cranes

A three-dimensional (3-D) overhead crane is a complicated nonlinear underactuated mechanical system, for which high-speed positioning and anti-sway control are the kernel objective. Existing trajectory-based methods for 3-D overhead cranes focus on combining efficient and smooth trajectories with anti-sway tracking controllers without regard for payload motion; moreover, the exact value of plant parameters is required for accurate compensation during the control process. Motivated by these facts, we present a two-step design tracking strategy which consists of a trajectory planning stage and an adaptive tracking control design stage for 3-D overhead cranes. As shown by Lyapunov techniques and Barbalat's Lemma, the proposed controller guarantees asymptotic swing elimination and trolley positioning result in the presence of system uncertainties including unknown parameters and external disturbances. Simulation results also showed the applicability of the proposed method with good robustness against parameter uncertainties and external disturbances.     

无人直升机基于视觉的静止点目标跟踪*

针对带有摄像机的直升机进行静止点目标跟踪的情况,提出了一种层级控制器。该控制器共有三个回路：内回路采用四个独立的PD控制器控制直升机的高度和姿态;中间回路利用两个Mamdani型模糊控制器控制直升机的位置;外回路利用视觉反馈获得直升机下一步的期望位置,其不需要已知摄像机的内参数和平移外参数以及目标点的坐标,只需已知粗略标定的旋转外参数。仿真结果表明了该控制器的可行性。     

一种机器人未知环境下动态目标跟踪交互多模滤波算法

为了解决机器人同时定位、地图构建和目标跟踪问题,提出了一种基于交互多模滤波(interacting multiple model filter, IMM)的方法.该方法将机器人状态、目标状态和环境特征状态作为整体来构成系统状态向量并利用全关联扩展式卡尔曼滤波算法对系统状态进行估计,由此随着迭代估计的进行,系统各对象状态之间将产生足够的相关性,这种相关性能够正确反映各对象状态估计间的依赖关系,因此提高了目标跟踪的准确性.该方法进一步和传统的IMM滤波算法相结合,从而解决了目标运动模式未知性问题,IMM方法的采用使系统在完成目标追踪的同时还能对其运动模态进行估计,进而提高了该算法对于机动目标的跟踪能力.仿真实验验证了该方法对机器人和目标的运动轨迹以及目标运动模态进行估计的准确性和有效性.     

Vision-based autonomous tracking and landing of a fully-actuated rotorcraft

Autonomous landing is a challenging phase of flight for an aerial vehicle, especially when attempting to land on a moving target. This paper presents vision-based tracking and landing of a fully-actuated tilt-augmented quadrotor on a moving target. A fully-actuated vehicle allows higher freedom in terms of control design and a larger flight envelope since the position and attitude states are decoupled. An adaptive control law is designed to track a moving target with only relative position information from a camera. Low-cost hardware is used, and experiments are carried out to validate the proposed methodology for targets moving at realistic speeds.     

Quaternion-based robust trajectory tracking control for uncertain quadrotors

This paper presents a robust nonlinear controller design approach for uncertain quadrotors to implement trajectory tracking missions. The quaternion representation is applied to describe the rotational dynamics in order to avoid the singularity problem existing in the Euler angle representation. A nonlinear robust controller is proposed, which consists of an attitude controller to stabilize the rotational motions and a position controller to control translational motions. The quadrotor dynamics involves uncertainties such as parameter uncertainties, nonlinearities, and external disturbances and their effects on closed-loop control system can be guaranteed to be restrained. Simulation results on the quadrotor demonstrate the effectiveness of the designed control approach.     

Maneuverability modeling and trajectory tracking for fish robot

This study develops a 6-DOF mathematical model for a robotic fish that considers surge, sway, heave, roll, pitch, and yaw. The model considers the conditions of a fish swimming in ocean current perturbations similar to the ocean current perturbations of the slender-body autonomous underwater vehicles. For swimming and turning behaviors, a nonlinear, dynamic, carangiform locomotion model is derived by using a planar four-link model. A 2-DOF barycenter mechanism is proposed to provide body stabilization and to serve as an actuating device for active control design. A barycenter control scheme is developed to change the center of gravity of the robot fish body by moving balancing masses along two axes. The projected torque on x and y axes propel pitch and roll angles to the desired settings. A Stabilizing controller, fish-tail mechanism, rigid body dynamics, and kinematics are incorporated to enable the fish robot to move in three dimensional space. Simulation results have demonstrated maneuverability and control system performance of the developed controller which is proposed to conduct path tracking of the robot fish as it swims under current perturbations.     

Mu-based trajectory tracking control for a quad-rotor UAV

In this paper, the design and application of a robust mu-synthesis-based controller for quad-rotor trajectory tracking are presented. The proposed design approach guarantees robust performance over a weakly nonlinear range of operation of the quad-rotor, which is a practical range that suits various applications. The controller considers different structured and unstructured uncertainties, such as unmodeled dynamics and perturbation in the parameters. The controller also provides robustness against external disturbances such as wind gusts and wind turbulence. The proposed controller is fixed and linear; therefore, it has a very low computational cost. Moreover, the controller meets all design specifications without tuning. To validate this control strategy, the proposed approach is compared to a linear quadratic regulator (LQR) controller using a high- fidelity quad-rotor simulation environment. In addition, the experimental results presented show the validity of the proposed control strategy.     

Optimization-based iterative learning for precise quadrocopter trajectory tracking

Current control systems regulate the behavior of dynamic systems by reacting to noise and unexpected disturbances as they occur. To improve the performance of such control systems, experience from iterative executions can be used to anticipate recurring disturbances and proactively compensate for them. This paper presents an algorithm that exploits data from previous repetitions in order to learn to precisely follow a predefined trajectory. We adapt the feed-forward input signal to the system with the goal of achieving high tracking performance—even under the presence of model errors and other recurring disturbances. The approach is based on a dynamics model that captures the essential features of the system and that explicitly takes system input and state constraints into account. We combine traditional optimal filtering methods with state-of-the-art optimization techniques in order to obtain an effective and computationally efficient learning strategy that updates the feed-forward input signal according to a customizable learning objective. It is possible to define a termination condition that stops an execution early if the deviation from the nominal trajectory exceeds a given bound. This allows for a safe learning that gradually extends the time horizon of the trajectory. We developed a framework for generating arbitrary flight trajectories and for applying the algorithm to highly maneuverable autonomous quadrotor vehicles in the ETH Flying Machine Arena testbed. Experimental results are discussed for selected trajectories and different learning algorithm parameters.