Decentralized robust tracking control for uncertain robots

To realize decentralized robust tracking control for robots with uncertain parameters, a new design method is proposed. A robust tracking controller designed by this method consists of two parts: a feedforward controller and a feedback robust controller. A feedforward control is first applied and error dynamics are introduced. For each joint error subsystem a robust controller is designed in two steps: first, a nominal controller is designed for the nominal plant to achieve desired tracking performance, then a robust compensator is added to restrain the influence of the perturbation, that is the difference of the real plant from the nominal plant. The controller designed by the proposed method is a linear time-invariant one. It is shown that robust stability and robust tracking property can be achieved by applying the controller with a sufficiently wide frequency bandwidth. An important feature of the method is that the controller parameters can be tuned on-line easily.List of symbols _max(B), _min(B) the maximum and minimum eigenvalues, respectively, of a symmetric positive definite matrix B - -      

基于扰动观测器的无人水面船鲁棒轨迹跟踪

为实现三自由度无人水面船(USV)在风浪流引起的海洋环境干扰下的轨迹跟踪,设计一种非线性扰动观测器(NDO)和动态面控制(DSC)的鲁棒控制器。首先,提出一种新的NDO来在线估计并补偿外界环境干扰。与传统干扰观测器不同,该扰动观测器具有有限时间收敛的特点;然后,在运动学回路中利用反演法设计虚拟控制律来镇定跟踪误差,并其通过一阶低通滤波器避免了由对虚拟控制求导而带来的计算复杂性,同时通过NDO对未知海洋干扰进行实时补偿,利用Lyapunov稳定性定理证明了USV闭环系统的误差信号半全局最终一致有界(SGUUB)。最终,仿真实验证明了所设计控制策略的有效性。     

基于干扰观测器的飞行仿真转台完全跟踪控制

针对飞行仿真转台不断提升的性能要求及其多速率采样的工作特点,提出了一种新的鲁棒完全跟踪控制策略。针对完全跟踪前馈控制依赖于系统精确数学模型的问题,通过引入干扰观测器来消除模型摄动和外部扰动,使得被控对象的特性在给定的频段内始终趋近于标称模型,充分保证了PTC的作用。仿真结果表明即使结合简单的PID闭环控制器,系统的跟踪性能也获得了很大的提升。给出PTC和DOB的具体设计方法,并对方法的有效性进行了对比仿真验证。结果表明,与已有方法相比,所提控制方法对模型摄动和力矩扰动的抑制能力更强,跟踪性能更好。     

Self-triggered adaptive prescribed-time tracking control for stochastic nonlinear systems

This paper proposes a self-triggered (ST) adaptive prescribed-time tracking control method for a class of stochastic nonlinear systems. Different from the existing results, an improved ST mechanism is proposed by adding a judgment condition to reduce the negative effect of excessive design interval on system performance. Based on the one-to-one mapping and backstepping technique, an adaptive prescribed-time tracking control method is proposed, which can make the error converge to the predefined precision set within the predetermined time. Simultaneously, applying the Lyapunov stability method, the boundedness of all signals in the closed-loop system can be ensured. Finally, a detailed simulation example is provided to show the effectiveness of the proposed control strategy.     

Adaptive fixed-time tracking control for stochastic pure-feedback nonlinear systems

The article discusses the adaptive fixed-time control problems for the stochastic pure-feedback nonlinear systems. Different from the existing results, the priori information of unknown virtual control coefficients (UVCC) is no longer needed in this article, which is realized by emplying the bound estimation method and well-defined smooth functions. A novel semi-global practical fixed-time stability criterion for the stochastic nonlinear systems is presented. Correspondingly, a new construction of Lyapunov function is proposed for the nonlinear stochastic system by adding the lower bounds of the UVCC. Based on the fuzzy logical system and fixed time stability theorem, a novel adaptive fuzzy fixed-time tracking control algorithm for stochastic nonlinear system is raised firstly. By theoretical analysis, we can conclude that the whole variables of the controlled system are bounded almost surely and the output can track the desired reference signal to a very small compact set within a predefined fixed-time interval. Finally, the raised method is illustrated by two simulation examples.     

Automatic traffic surveillance system for vehicle tracking and classification

This paper presents an automatic traffic surveillance system to estimate important traffic parameters from video sequences using only one camera. Different from traditional methods that can classify vehicles to only cars and noncars, the proposed method has a good ability to categorize vehicles into more specific classes by introducing a new "linearity" feature in vehicle representation. In addition, the proposed system can well tackle the problem of vehicle occlusions caused by shadows, which often lead to the failure of further vehicle counting and classification. This problem is solved by a novel line-based shadow algorithm that uses a set of lines to eliminate all unwanted shadows. The used lines are devised from the information of lane-dividing lines. Therefore, an automatic scheme to detect lane-dividing lines is also proposed. The found lane-dividing lines can also provide important information for feature normalization, which can make the vehicle size more invariant, and thus much enhance the accuracy of vehicle classification. Once all features are extracted, an optimal classifier is then designed to robustly categorize vehicles into different classes. When recognizing a vehicle, the designed classifier can collect different evidences from its trajectories and the database to make an optimal decision for vehicle classification. Since more evidences are used, more robustness of classification can be achieved. Experimental results show that the proposed method is more robust, accurate, and powerful than other traditional methods, which utilize only the vehicle size and a single frame for vehicle classification.     

Relative discriminant coefficient based multi-cue fusion for robust object tracking

In visual tracking, integrating multiple cues will increase the reliability and robustness of the tracking system in situations where no single cue is reliable. In this paper, a novel multi-cue based tracking method is presented under the particle filter framework. Considering both practical distance and Bhattacharyya distance between particles and the target, a parameter called relative discriminant coefficient (RDC) is designed to measure the tracking ability for different features. Multi-cue fusion is carried out in a reweighing manner based on this parameter. Experimental results demonstrate the high robustness and effectiveness of our method in handling appearance changes, cluttered background, illumination changes and occlusions.     

机器人的神经网络鲁棒轨迹跟踪控制

考虑了一类具有外界扰动和参数不确定性机器人系统的轨迹跟踪鲁棒控制问题。提出了两种控制方法：第一种应用输入输出线性化方法以及Lyapunov函数法,推导出鲁棒输出跟踪控制器。所获得的控制器可确保系统输出按指数规律跟踪期望输出,同时相应闭环系统的状态一致最终有界。第二种方法在第一种控制方法的基础上,利用一个RBF神经网络自适应学习系统不确定性的未知上界,有效的克服了系统不确定性的影响,提高了控制精度。     

Modular design of adaptive robust controller for strict-feedback stochastic nonlinear systems

A modular approach of the estimation-based design in adaptive linear control systems has been extended to the adaptive robust control of strict-feedback stochastic nonlinear systems with additive standard Wiener noises and constant unknown parameters. By using It?’s differentiation rule, nonlinear damping and adaptive Backstepping procedure, the input-to-state stable controller of global stabilization in probability is developed, which guarantees that system states are bounded and the system has a robust stabilization. According to Swapping technique, we develop two filters and convert dynamic parametric models into static ones to which the gradient update law is designed. Transient performance of the system is estimated by the norm of error. Results of simulation show the effectiveness of the control algorithms. The modular design, which has a concise hierarchy, is more flexible and versatile than a Lyapunov-based algorithm. __________ Translated from Journal of University of Science and Technology of China, 2004, 34(4): 495–503 (in Chinese)     

Learning-based spatio-temporal vehicle tracking and indexing for transportation multimedia database systems

One key technology of intelligent transportation systems is the use of advanced sensor systems for on-line surveillance to gather detailed information on traffic conditions. Traffic video analysis can provide a wide range of useful information to traffic planners. In this context, the object-level indexing of video data can enable vehicle classification, traffic flow analysis, incident detection and analysis at intersections, vehicle tracking for traffic operations, and update of design warrants. In this paper, a learning-based automatic framework is proposed to support the multimedia data indexing and querying of spatio-temporal relationships of vehicle objects in a traffic video sequence. The spatio-temporal relationships of vehicle objects are captured via the proposed unsupervised image/video segmentation method and object tracking algorithm, and modeled using a multimedia augmented transition network model and multimedia input strings. An efficient and effective background learning and subtraction technique is employed to eliminate the complex background details in the traffic video frames. It substantially enhances the efficiency of the segmentation process and the accuracy of the segmentation results to enable more accurate video indexing and annotation. The paper uses four real-life traffic video sequences from several road intersections under different weather conditions in the study experiments. The results show that the proposed framework is effective in automating data collection and access for complex traffic situations.     

Analysis of stability and robust stability for stochastic hybrid systems with impulsive effects

In this article, the problems of stability and robust stability analysis are investigated for a class of Markovian switching stochastic systems, which has impulses at switching instants. The switching parameters considered form a continuous-time discrete-state homogeneous Markov process. Multiple Lyapunov techniques are used to derive sufficient conditions for stability in probability of the overall system. The conditions are in linear matrix inequalities form, and can be used to solve stabilization synthesis problems. The results are extended to the design of a robust-stabilized state-feedback controller as well. A numerical example shows the effectiveness of the proposed approach. __________ Translated from Control and Decision, 2008, 23(5): 555–559 [译自: 控制与决策]     

Adaptive robust output feedback control for attitude tracking of quadrotor unmanned aerial vehicles

This work proposes a new adaptive robust output feedback control method for attitude reference tracking of a quadrotor unmanned aerial vehicle without using the angular velocity measurements. By using the K-filters well known in the adaptive control community, the necessity of velocity measurements or estimating is avoided. The attitude system model is transformed into a second-order model where the angular velocity measurements are not involved. However, the model includes mismatched uncertainties which should be estimated and compensated by the disturbance observers (DOBs). The controller is designed in a backstepping manner, and the dynamic surface technique is adopted to avoid the explosion of the controller complexity. For each Euler angle axis, the prescribed performance control technique is adopted to ensure a prescribed performance, the lumped disturbance is compensated by a DOB, and furthermore an adaptive law is introduced to adaptively update the corresponding uncertain inertia parameter which affects the control performance significantly. The control performance of the overall control system is analyzed rigorously from the viewpoint of input-to-state practical stability. In addition, it is shown how the adaptive laws contribute to improving the control performance. And simulation examples are provided to demonstrate the performance of the proposed method.     

基于古诺模型的电力产消者鲁棒随机市场交易研究

随着用户侧分布式能源的爆发式增长,具有源荷双重属性的产消者逐渐增多,并且开始作为新兴主体参与到电力市场中。针对含产消者的寡头电力市场出清问题,提出了一种基于古诺模型的产消者市场交易模型,并进一步采用鲁棒和随机混合方法处理产消者内部可再生能源出力的不确定性,提出了产消者鲁棒随机市场交易模型。最后,在IEEE-39节点系统上验证了所提模型和方法的有效性,并对产消者的市场力进行了评估。     

基于鲁棒随机模型预测控制的园区综合能源系统两阶段优化

可再生能源发电与负荷的不确定性给园区综合能源系统的安全准确调度带来了巨大挑战,为了提高系统调度的准确度和运行经济性,提出了一种基于鲁棒随机模型预测控制的两阶段优化调度策略.该模型考虑到园区综合能源系统中负荷预测误差分布的规律性和可再生能源出力波动的概率分布难以准确刻画的特点,分别利用随机优化和鲁棒优化处理负荷侧和发电侧...     

家庭智能用电的随机&可调节鲁棒优化混合调度策略研究

针对包含光伏及储能系统的家庭用户,考虑光伏发电的预测距离运行点越远精度越低的特点,以及家庭用户用电行为具有不确定性的特点,建立了分布式电源、家庭负荷及用户用电行为不确定性的模型。结合随机优化与可调节鲁棒优化的优点,提出了一种家庭智能用电的随机与可调节鲁棒混合调度策略,以系统运行成本最小为目标,保证用户用电自由与舒适度,同时提高光伏的本地消纳水平。基于工程博弈论思想和改进粒子群算法,将优化模型转换为混合整数线性规划问题进行求解。通过算例仿真,验证了该调度策略的有效性。     

An error correction algorithm for stochastic production costing

A theoretical approach is presented for calculating the error involved in the solution of stochastic production costing problems using the method of cumulants. It is proposed to use this calculated error in an algorithm for obtaining an improved stochastic approximation of the equivalent-load-duration curve. The results show that of the three series that can be used for stochastic approximation, namely, the beta, the Gram-Charlier, and the Laguerre, the beta gives the best results. All three methods, however, give unacceptable errors in the approximation of the expected unserved energy. Another method is proposed that approximates the error between the beta curve and the actual curve using the Fourier-Chebyshev expansion, which is used to develop a better stochastic approximation. Results obtained using practical system data indicate that this method gives much better results than the three methods discussed above     

An adaptive non-linear estimate for radar target tracking

The detection of targets with a radar system is inherently a non-linear stochastic process. Thus the application of non-linear estimation becomes inevitable. A review of four approaches—the extended Kalman filter (EKF), the Gaussian second-order filter (GSF), the iterative filter (IF) and the adaptive filter (AF)—is given in this paper. The superiority of AF in tracking manoeuvring targets is quite clear in comparison with the other filters.     

A stochastic framework for reactive power procurement market,based on nodal price model

A new modified nodal pricing method is presented in this paper to calculate reactive power price. This article tries to solve the problems of traditional nodal pricing methods to be able to consider opportunity and availability costs for the generators. A modified model of generators reactive power production cost has been used in OPF objective function considering normal and contingency conditions. Availability cost would be estimated considering the volume of network demand from the generators in contingency states through probability calculations. This cost would be paid to all the units that possibly will needed to produce reactive power in contingency states. Purvey of required availability of generation units to deal with emergency conditions, encouraging providers to invest in critical areas in the network and fair distribution of reactive power production costs over several producers are some advantages of the proposed method. The proposed method is tested on IEEE 24 bus RTS and is compared with the uniform pricing method.