Neural network-based flight control systems: Present and future

As the first review in this field, this paper presents an in-depth mathematical view of Intelligent Flight Control Systems (IFCSs), particularly those based on artificial neural networks. The rapid evolution of IFCSs in the last two decades in both the methodological and technical aspects necessitates a comprehensive view of them to better demonstrate the current stage and the crucial remaining steps towards developing a truly intelligent flight management unit. To this end, in this paper, we will provide a detailed mathematical view of Neural Network (NN)-based flight control systems and the challenging problems that still remain. The paper will cover both the model-based and model-free IFCSs. The model-based methods consist of the basic feedback error learning scheme, the pseudocontrol strategy, and the neural backstepping method. Besides, different approaches to analyze the closed-loop stability in IFCSs, their requirements, and their limitations will be discussed in detail. Various supplementary features, which can be integrated with a basic IFCS such as the fault-tolerance capability, the consideration of system constraints, and the combination of NNs with other robust and adaptive elements like disturbance observers, would be covered, as well. On the other hand, concerning model-free flight controllers, both the indirect and direct adaptive control systems including indirect adaptive control using NN-based system identification, the approximate dynamic programming using NN, and the reinforcement learning-based adaptive optimal control will be carefully addressed. Finally, by demonstrating a well-organized view of the current stage in the development of IFCSs, the challenging issues, which are critical to be addressed in the future, are thoroughly identified. As a result, this paper can be considered as a comprehensive road map for all researchers interested in the design and development of intelligent control systems, particularly in the field of aerospace applications.     

Learning to Perceive and Act by Trial and Error

This article considers adaptive control architectures that integrate active sensory-motor systems with decision systems based on reinforcement learning. One unavoidable consequence of active perception is that the agent's internal representation often confounds external world states. We call this phoenomenon perceptual aliasingand show that it destabilizes existing reinforcement learning algorithms with respect to the optimal decision policy. We then describe a new decision system that overcomes these difficulties for a restricted class of decision problems. The system incorporates a perceptual subcycle within the overall decision cycle and uses a modified learning algorithm to suppress the effects of perceptual aliasing. The result is a control architecture that learns not only how to solve a task but also where to focus its visual attention in order to collect necessary sensory information.     

Enhancement of Flexural Design of Horizontally Curved Prestressed Bridges

In this paper, flexural behavior of horizontally curved prestressed (posttensioned) box bridges is studied by using three-dimensional and refined finite-element modeling and analysis. Bridge length, section geometry, and material properties are the same in all the models, while angle of curvature varies from 0 to 90°. The results of analysis show that in curved bridges, stress distribution is significantly different in comparison to straight bridges. Also, the level of stresses at some locations of section width is considerably high. It is proposed to vary the distribution of the prestressing tendons across section width in order to optimize the bridge capacity. Results show that by proper redistribution of prestressing in section width, significant reduction in resultant stress is possible.     

滁州市农贸市场超长预应力楼盖的设计分析

滁州市农贸市场超长预应力楼盖温度应力有限元分析及设计原则。     

Multiagent learning using a variable learning rate

Learning to act in a multiagent environment is a difficult problem since the normal definition of an optimal policy no longer applies. The optimal policy at any moment depends on the policies of the other agents. This creates a situation of learning a moving target. Previous learning algorithms have one of two shortcomings depending on their approach. They either converge to a policy that may not be optimal against the specific opponents' policies, or they may not converge at all. In this article we examine this learning problem in the framework of stochastic games. We look at a number of previous learning algorithms showing how they fail at one of the above criteria. We then contribute a new reinforcement learning technique using a variable learning rate to overcome these shortcomings. Specifically, we introduce the WoLF principle, “Win or Learn Fast”, for varying the learning rate. We examine this technique theoretically, proving convergence in self-play on a restricted class of iterated matrix games. We also present empirical results on a variety of more general stochastic games, in situations of self-play and otherwise, demonstrating the wide applicability of this method.     

Neural Network Feedback Control: Work at UTA’s Automation and Robotics Research Institute

This is an outline of research in neural networks for feedback control done since the mid 1990s at the Automation and Robotics Research Institute (ARRI) of The University of Texas at Arlington (UTA). It shows how the developments of Intelligent Control Systems based on neural networks have followed three main generations. This statement provides a short, broad-brush perspective on the development of intelligent neural feedback controllers.     

商业创新模式下的建筑设计服务

购物中心作为一种现代的零售商业模式,从上世纪90年代进入中国后,已为一般民众厂为接受。文章首先简要地介绍了美国购物中心的开发历史和类型,尤其是近年来出现的生活方式中心的特点和发展趋势。对于购物中心在中国的开发、规划设计中经常遇到的一些内容,如融资方式,主力商店和专卖店等进行了讨论。最后对于建筑师在这新的商业模式下如何与设计团队一起提供专业服务方面根据自身的经验提出了自己的看法。     

Impact of specification changes on chloride-induced corrosion service life of bridge decks

A model was developed to determine the time to first repair and to subsequently rehabilitate concrete bridge decks exposed to chloride deicer salts. Said model incorporates the statistical nature of factors affecting the corrosion process. The time to first repair and rehabilitate was predicted for 10 bridge decks built in Virginia between 1981 and 1994.The model was validated using historical service life data for 129 bridge decks built in Virginia between 1968 and 1972. The time for rehabilitation predicted for the newer set of bridge decks was approximately 13 years longer than the normalized time for rehabilitation projected for the older bridge decks. The increase in time for rehabilitation for the newer set of bridge decks was attributed to a reduction in the specified maximum water/cement ratio and increase in clear cover depth. The probabilistic model is shown to be an advancement over the deterministic model currently in use.     

An actor-critic algorithm for constrained Markov decision processes

An actor-critic type reinforcement learning algorithm is proposed and analyzed for constrained controlled Markov decision processes. The analysis uses multiscale stochastic approximation theory and the envelope theorem' of mathematical economics.