Iterative Learning Control Algorithm with a Fixed Step

Iterative Learning Control (ILC) captures interests of many scholars because of its capability of high precision control implement without identifying plant mathematical models, and it is widely applied in control engineering. Presently, most ILC algorithms still follow the original ideas of ARIMOTO, in which the iterative-learning-rate is composed by the control error with its derivative and integral values. This kind of algorithms will result in inevitable problems such as huge computation, big storage capacity for algorithm data, and also weak robust. In order to resolve these problems, an improved iterative learning control algorithm with fixed step is proposed here which breaks the primary thought of ARIMOTO. In this algorithm, the control step is set only according to the value of the control error, which could enormously reduce the computation and storage size demanded, also improve the robust of the algorithm by not using the differential coefficient of the iterative learning error. In this paper, the convergence conditions of this proposed fixed step iterative learning algorithm is theoretically analyzed and testified. Then the algorithm is tested through simulation researches on a time-variant object with randomly set disturbance through calculation of step threshold value, algorithm robustness testing,and evaluation of the relation between convergence speed and step size. Finally the algorithm is validated on a valve-serving-cylinder system of a joint robot with time-variant parameters. Experiment results demonstrate the stability of the algorithm and also the relationship between step value and convergence rate. Both simulation and experiment testify the feasibility and validity of the new algorithm proposed here. And it is worth to noticing that this algorithm is simple but with strong robust after improvements, which provides new ideas to the research of iterative learning control algorithms.     

Apparent wind load effects on the tether of a kite power generator

This paper is devoted to analytical modeling of apparent wind load effects on a tether constraining a power kite performing a fast crosswind motion. The refined crosswind motion law generalized for accounting the kite control is derived in the case of equilibrium motion of the kite. The paper also presents the study of the effect of tether sag based on the mathematical model of a gently sloping tether line.     

25 m射电望远镜俯仰系统的全过程建模

最优控制可有效提高大射电望远镜的指向精度,但其控制器设计需要精确的被控对象模型。为此,文中依据其有限元模型,建立了俯仰系统从电机到副面运动的全过程模型。首先,应用模态坐标法建立了大齿轮和主反射面模型,体现了结构主体刚柔混合特性;然后,应用Euler-Bernoulli梁方程式推导了副面撑杆和副面模型,反映了结构末端柔性;最后,通过仿真分析验证了该建模方法的准确性和有效性。与目前已有的射电望远镜建模方法相比,该方法的优点在于在天线设计阶段就可模拟各部分的实际运转状况,可提前发现不足。     

Calibrating whole building energy models: An evidence-based methodology

This paper reviews existing case studies and methods for calibrating whole building energy models to measured data. This research describes a systematic, evidence-based methodology for the calibration of these models. Under this methodology, parameter values in the final calibrated model reference the source of information used to make changes to the initial model. Thus, the final model is based solely on evidence. Version control software stores a complete record of the calibration process, and the evidence on which the final model is based. Future users can review the changes made throughout the calibration process along with the supporting evidence. In addition to the evidence-based methodology, this paper also describes a new zoning process that represents the real building more closely than the typical core and four perimeter zone approach. Though the methodology is intended to apply to detailed calibration studies with high resolution measured data, the primary aspects of the methodology (evidence-based approach, version control, and zone-typing) are independent of the available measured data.     

提高弱电网中光伏并网逆变器稳定性的控制方法

随着电网中分布电源的增加,电网呈现出了弱电网特性。电网阻抗使得逆变器与电网之间存在相互作用,对逆变器的稳定性产生影响,威胁着系统的安全稳定运行。从相位角度分析了电网阻抗对系统稳定性的影响,电网阻抗的增加会导致系统相位降低,继而使系统进入不稳定状态。为增强系统在电网阻抗变化过程中的鲁棒性,提出一种基于神经网络的逆变器变增益控制方法。该方法采用一定的控制环节补偿系统相位,采用径向基函数神经网络建模的方法实时调整系统的开环截止频率,使相位裕度始终保持最大值。仿真结果验证了本文的理论分析。     

Neural Network–Based Intelligent Compaction Analyzer for Estimating Compaction Quality of Hot Asphalt Mixes

Continuous real-time estimating of compaction quality during the construction of a hot mix asphalt (HMA) pavement is addressed in this paper. The densification of asphalt pavements during construction usually is accomplished by using vibratory compactors. During compaction, the compactor and the asphalt mat form a coupled system whose dynamics are influenced by the changing stiffness of the mat. The measured vibrations of the compactor along with process parameters such as lift thickness, mix type, mix temperature, and compaction pressure can be used to predict the asphalt mat density. Contrary to existing techniques in the literature in which a model is developed to fit experimental data and to predict mat density, a neural network-based approach is adopted that is model-free and uses pattern-recognition techniques to estimate density. The neural network is designed to read the entire frequency spectrum of roller vibrations and to classify these vibrations into different levels. The intelligent asphalt compaction analyzer (IACA) is then trained to convert these vibration levels into a “number” indicative of the asphalt mat density at a given location. This two-step process eliminates the need for regression analysis and produces more accurate density measurements than those reported elsewhere in the literature. Compaction studies of HMA mixes on a stiff subgrade indicate that the changes in the vibration characteristics of the roller are attributable to an increased compaction of the HMA base. The results also show that, with the neural network working as a classifier, the IACA can estimate the density continuously, and in real time, with accuracy levels adequate for quality control in the field.     

Memory capacity, selective control, and value-directed remembering in children with and without attention-deficit/hyperactivity disorder (ADHD).

Objective: The ability to select what is important to remember, to attend to this information, and to recall high-value items leads to the efficient use of memory. The present study examined how children with and without attention-deficit/hyperactivity disorder (ADHD) performed on an incentive-based selectivity task in which to-be-remembered items were worth different point values. Method: Participants were 6–9 year old children with ADHD (n = 57) and without ADHD (n = 59). Using a selectivity task, participants studied words paired with point values and were asked to maximize their score, which was the overall value of the items they recalled. This task allows for measures of memory capacity and the ability to selectively remember high-value items. Results: Although there were no significant between-groups differences in the number of words recalled (memory capacity), children with ADHD were less selective than children in the control group in terms of the value of the items they recalled (control of memory). All children recalled more high-value items than low-value items and showed some learning with task experience, but children with ADHD Combined type did not efficiently maximize memory performance (as measured by a selectivity index) relative to children with ADHD Inattentive type and healthy controls, who did not differ significantly from one another. Conclusions: Children with ADHD Combined type exhibit impairments in the strategic and efficient encoding and recall of high-value items. The findings have implications for theories of memory dysfunction in childhood ADHD and the key role of metacognition, cognitive control, and value-directed remembering when considering the strategic use of memory. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Preschool executive control on the Shape School task: Measurement considerations and utility.

Executive functions (EF) necessary for purposeful goal-directed activities undergo rapid change and development during the preschool years. However, of the few psychometrically valid measures of EF suitable for use with preschoolers, information on task sensitivity and predictive validity is scant. The neurodevelopmental correlates of early executive difficulties are also largely unknown. In this study, the discriminant and predictive validity of the recently developed Shape School task (Espy, Bull, Martin, & Stroup, 2006) was examined with data from a regional sample of 209 preschool children at age 4 years. A 2-tiered measurement approach was used, with task completion examined in addition to efficiency. Children's performance was also examined in relation to functioning in a range of neurodevelopmental domains. The Shape School task showed some usefulness in capturing expected differences between at-risk and typically developing children. Performance loaded heavily on language and global cognitive abilities. However, several other factors were also implicated, including attention, motor skills, and ocular control. In addition, task completion and efficiency scores appeared to reflect different aspects of performance, and their associations with neurodevelopmental function and later academic achievement on the Woodcock-Johnson Tests of Achievement at age 6 years also differed. Implications for the application of the Shape School task are discussed. (PsycINFO Database Record (c) 2011 APA, all rights reserved)