A Run-to-Run Control Strategy to Adjust Basal Insulin Infusion Rates in Type 1 Diabetes

Maintaining good glycemic control is a daily challenge for people with type 1 diabetes. Insulin requirements are changing constantly due to many factors, such as levels of stress and physical activity. The basal insulin requirement also has a circadian rhythm, adding another level of complexity. Automating the adjustment of insulin dosing would result in improved glycemic control, as well as an improved quality of life by significantly reducing the burden on the patient. Building on our previous success of using run-to-run control for prandial insulin dosing (a strategy adapted from the chemical process industry), we show how this same framework can be used to adjust basal infusion profiles. We present a mathematical model of insulin–glucose dynamics which we augment in order to capture the circadian variation in insulin requirements. Using this model, we show that the run-to-run framework can also be successfully applied to adjust basal insulin dosing.     

基于模糊控制的自主机器人路径规划策略研究

结合人类的经验及模糊控制理论研究移动机器人的局部路径规划问题,采用步长的转向角控制的方法模拟驾车减速的自然过程,同时采用了虚拟目标点的方法处理局部路径规划中经常出现的陷阱问题．仿真结果验证了所提方法的有效性和可行性．     

Reinforcement Learning Behavioral Control for Nonlinear Autonomous System

Behavior-based autonomous systems rely on human intelligence to resolve multi-mission conflicts by designing mission priority rules and nonlinear controllers. In this work, a novel two-layer reinforcement learning behavioral control (RLBC) method is proposed to reduce such dependence by trial-and-error learning. Specifically, in the upper layer, a reinforcement learning mission supervisor (RLMS) is designed to learn the optimal mission priority. Compared with existing mission supervisors, the RLMS improves the dynamic performance of mission priority adjustment by maximizing cumulative rewards and reducing hardware storage demand when using neural networks. In the lower layer, a reinforcement learning controller (RLC) is designed to learn the optimal control policy. Compared with existing behavioral controllers, the RLC reduces the control cost of mission priority adjustment by balancing control performance and consumption. All error signals are proved to be semi-globally uniformly ultimately bounded (SGUUB). Simulation results show that the number of mission priority adjustment and the control cost are significantly reduced compared to some existing mission supervisors and behavioral controllers, respectively.      

融合类人驾驶行为的无人驾驶深度强化学习方法

现有无人车辆的驾驶策略过于依赖感知-控制映射过程的“正确性”,而忽视了人类驾驶汽车 时所遵循的驾驶逻辑。该研究基于深度确定性策略梯度算法,提出了一种具备类人驾驶行为的端到端 无人驾驶控制策略。通过施加规则约束对智能体连续行为的影响,建立了能够输出符合类人驾驶连续 有序行为的类人驾驶端到端控制网络,对策略输出采用了后验反馈方式,降低了控制策略的危险行为 输出率。针对训练过程中出现的稀疏灾难性事件,提出了一种更符合控制策略优化期望的连续奖励函 数,提高了算法训练的稳定性。不同仿真环境下的实验结果表明,改进后的奖励塑造方式在评价稀疏 灾难性事件时,对目标函数优化期望的近似程度提高了 85.57%,训练效率比传统深度确定性策略梯度 算法提高了 21%,任务成功率提高了 19%,任务执行效率提高了 15.45%,验证了该方法在控制效率和 平顺性方面具备明显优势,显著减少了碰撞事故。     

基于深度递归强化学习的无人自主驾驶策略研究

提出了一种基于递深度递归强化学习的自动驾驶策略模型学习方法,并在TORCS虚拟驾驶引擎进行仿真验真。针对Actor-Critic框架过估计和更新缓慢的问题,结合clipped double DQN,通过取最小估计值的方法缓解过估计的情况。为了获取多时刻状态输入以帮助智能体更好的决策,结合递归神经网络,设计出包含LSTM结构的Actor策略网络的Critic评价网络。在TORCS平台仿真实验表明,所提算法相对与传统DDPG算法能有效提高训练效率。     

PD型模糊学习控制及其在可重复轨迹跟踪问题中的应用

针对可重复轨迹跟踪问题,提出了一种PD型模糊学习算法.该算法集成两种控制:作为基础的PD型模糊逻辑算法和改善系统性能的学习算法.模糊学习控制在模糊控制基础上引入迭代学习算法,使得模糊PD控制器可以精确地跟踪可重复轨迹以及消除周期性扰动.本文在能量函数和泛函分析的基础上,通过严格的推导表明PD型模糊学习算法可达到:1)系统跟踪误差一致收敛到零;2)学习控制序列几乎处处收敛到理想的控制信号.     

自主探测系统的Bayes递推估计策略

探测系统被动地接爱外界的测量信息是不够的,因此方式获得的数据是不完备的,由此揄得出的结论在一定程度上具有不确定性,是不能接受的,为获得可靠的信息,常采用多传感器的数据融合或装备自主传感器的方法,文中将Bayes递推估计策略应用于自主探测系统,使其能最逼真的再再所关心的探测对象。     

迭代学习的高速伺服系统位置控制策略

针对高速运动伺服控制系统的精确位宣跟踪控制问题,在A型迭代学习控制算法的基础上,提出了一种基于比例一预期迭代学习控制的位置控制策略,并将其应用于建立的交流永磁同步电机模型,最后利用Matlab进行仿真研究。结果表明,此控制策略仅需少量对象信息就能满足系统的性能要求。它与常见迭代学习控制算法相比,具有位置控制精度高、学习收敛速度快等优点。     

基于协作增强式学习的自主式微直升机控制

自主式微直升机为了获得完成任务的行为，可以通过在任务环境中飞行来学习控制规则，由于增强式学习不需要精确的环境模型，而是采用逐次逼近的机理，所以微直升机需要很长的计算开销，难以满足实时性要求，另外由于微直升机尺寸的限制，不能安装功能很强的传感器来获得完全的环境信息，所以微直升机必须通过其他智能体协作来获得环境信息，本文利用高档台式机强大的计算和扩展功能，将其作为一个协作智能体与微直升机协作来完成增强式学习，仿真实验结果和理论分析证明这种方法的有效性，最后，给出我们今后的研究重点。     

Deep Learning Based Process Analytics Model for Predicting Type 2 Diabetes Mellitus

Process analytics is one of the popular research domains that advanced in the recent years. Process analytics encompasses identification, monitoring, and improvement of the processes through knowledge extraction from historical data. The evolution of Artificial Intelligence (AI)-enabled Electronic Health Records (EHRs) revolutionized the medical practice. Type 2 Diabetes Mellitus (T2DM) is a syndrome characterized by the lack of insulin secretion. If not diagnosed and managed at early stages, it may produce severe outcomes and at times, death too. Chronic Kidney Disease (CKD) and Coronary Heart Disease (CHD) are the most common, long-term and life-threatening diseases caused by T2DM. Therefore, it becomes inevitable to predict the risks of CKD and CHD in T2DM patients. The current research article presents automated Deep Learning (DL)-based Deep Neural Network (DNN) with Adagrad Optimization Algorithm i.e., DNN-AGOA model to predict CKD and CHD risks in T2DM patients. The paper proposes a risk prediction model for T2DM patients who may develop CKD or CHD. This model helps in alarming both T2DM patients and clinicians in advance. At first, the proposed DNN-AGOA model performs data preprocessing to improve the quality of data and make it compatible for further processing. Besides, a Deep Neural Network (DNN) is employed for feature extraction, after which sigmoid function is used for classification. Further, Adagrad optimizer is applied to improve the performance of DNN model. For experimental validation, benchmark medical datasets were used and the results were validated under several dimensions. The proposed model achieved a maximum precision of 93.99%, recall of 94.63%, specificity of 73.34%, accuracy of 92.58%, and F-score of 94.22%. The results attained through experimentation established that the proposed DNN-AGOA model has good prediction capability over other methods.     

自主微小型移动机器人的协作学习

自主微小型移动机器人的协作学习研究是多智能体机器人系统理论的主要研究方向。因为单个微小型移动机器人能力有限，所以机器人之间的协作在某些重要的基础工业和生物医学领域方面显得非常重要。该文介绍了几种用于协作学习的方法并且比较了它们之间的优点和缺点。最后，简要介绍了一些研究工作。     

体操机器人的模糊控制策略

提出了一种体操机器人模糊控制策略,它结合了无需模型和基于模型的模糊控制,无需模型的模糊控制器是为摇起体操机器人垢,它确保体操机器人的随着每次摆动而增加,基于模型的控制器是为平衡体操机器人设计的,它基于一个Ｔａｋａｇｉ－Ｓｕｇｅｎｏ模糊模型。     

Autonomous Learning Architecture for Environmental Mapping

Here we propose an architecture for an autonomous mobile agent that explores while mapping a two-dimensional environment. The map is a discretized model for the localization of obstacles, on top of which a harmonic potential field is computed. The potential field serves as a fundamental link between the modeled (discrete) space and the real (continuous) space where the agent operates. It indicates safe paths towards non-explored regions. Harmonic functions were originally used as global path planners in mobile robotics. In this paper, we extend its functionality to environment exploration. We demonstrate our idea through experimental results obtained using a Nomad 200 robot platform.     

一种基于遗传算法的移动机器人自定位方法

针对移动机器人定位研究中的位姿跟踪、全局定位和"绑架"三类问题,提出一种基于遗传算法的移动机器人自定位方法.设计基于位置相似度的种群适应度计算方法,利用实值编码方式实现种群的交叉、变异,有效提高算法的实时性.针对机器人定位过程中的"绑架"现象,在常规遗传算法的基础上引人种群发散算子,减小种群匮乏效应.在此基础上,利用机器人运动模型更新种群状态实现机器人的连续定位.在实际室内环境进行机器人定位实验,证实本文算法的有效性.     

一种提高学生自主学习能力的相贯线实践教学法

图学课程中相贯线部分内容是教学的重点也是难点。为改变多年来课堂理论教学的 单一模式,提出了一种提高学生自主学习能力的相贯线实践教学法。在运用自主开发的相贯线绘 制仪的基础上,制订了具体的实践教学大纲、计划及考评机制。由实践教学的初步实施表明,在 学习该难点内容时,大大激发了学生学习的积极性和学习兴趣。由于采用了学生自主选题、实物 实操教学,学生对这一难点的理解更为容易,印象也更加深刻。     

Learning to Perceive Objects for Autonomous Navigation

Current machine perception techniques that typically use segmentation followed by object recognition lack the required robustness to cope with the large variety of situations encountered in real-world navigation. Many existing techniques are brittle in the sense that even minor changes in the expected task environment (e.g., different lighting conditions, geometrical distortion, etc.) can severely degrade the performance of the system or even make it fail completely. In this paper we present a system that achieves robust performance by using local reinforcement learning to induce a highly adaptive mapping from input images to segmentation strategies for successful recognition. This is accomplished by using the confidence level of model matching as reinforcement to drive learning. Local reinforcement learning gives rises to better improvement in recognition performance. The system is verified through experiments on a large set of real images of traffic signs.     

A Teaching Strategy for Memory-Based Control

Combining different machine learning algorithms in the same system can produce benefits above and beyond what either method could achieve alone. This paper demonstrates that genetic algorithms can be used in conjunction with lazy learning to solve examples of a difficult class of delayed reinforcement learning problems better than either method alone. This class, the class of differential games, includes numerous important control problems that arise in robotics, planning, game playing, and other areas, and solutions for differential games suggest solution strategies for the general class of planning and control problems. We conducted a series of experiments applying three learning approaches – lazy Q-learning, k-nearest neighbor (k-NN), and a genetic algorithm – to a particular differential game called a pursuit game. Our experiments demonstrate that k-NN had great difficulty solving the problem, while a lazy version of Q-learning performed moderately well and the genetic algorithm performed even better. These results motivated the next step in the experiments, where we hypothesized k-NN was having difficulty because it did not have good examples – a common source of difficulty for lazy learning. Therefore, we used the genetic algorithm as a bootstrapping method for k-NN to create a system to provide these examples. Our experiments demonstrate that the resulting joint system learned to solve the pursuit games with a high degree of accuracy – outperforming either method alone – and with relatively small memory requirements.     

自治供电系统的一种混合控制

研究自治供电系统 (ASES)控制问题, 基于“状态空间区域划分”和“切换控制”原理, 提出一种混合型控制器. 仿真表明, 相比于近年来文献 [1]中提出的动态滑模变结构控制方案, 在保持系统基本性能前提下, 可有效克服该方案中不可避免的抖振与高频切换等严重问题.