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21st Workshop on Information Technologies and systems, WITS 2011

作者： Zhang, Jie Yang, Yanwu Qin, Rui Zeng, Daniel Li, Xin State Key Lab. of Intelligent Control and Management of Complex Systems Chinese Academy of Sciences Beijing 100190 China Department of Management Information Systems University of Arizona United States Department of Information Systems City University of Hong Kong Kowloon Tong Hong Kong

With serious advertising budget constraints, advertisers have to adjust their daily budget according to the performance of advertisements in real time. Thus we can leave precious budgets to better opportunities in the future, and avoid the surge of ineffective clicks for unnecessary costs. However, advertisers usually have no sufficient knowledge and time for real-time advertising operations in search auctions. We formulate the budget adjustment problem as a state-action decision process in the reinforcement learning (RL) framework. Considering dynamics of marketing environments and some distinctive features of search auctions, we extend continuous reinforcement learning to fit the budget decision scenarios. The market utility is defined as discounted total clicks to get during the remaining period of an advertising schedule. We conduct experiments to validate and evaluate our strategy of budget adjustment with real world data from search advertising campaigns. Experimental results showed that our strategy outperforms the two other baseline strategies.

关键词： Marketing

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A review of cooperative path planning of an unmanned aerial vehicle group

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Frontiers of Information Technology & Electronic Engineering 2020年第12期21卷 1671-1694页

作者： Hao ZHANG Bin XIN Li-hua DOU Jie CHEN Kaoru HIROTA School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing 100081China Department of Computational Intelligence and Systems Science Tokyo Institute of TechnologyTokyo 1528550Japan

As a cutting-edge branch of unmanned aerial vehicle(UAV)technology,the cooperation of a group of UAVs has attracted increasing attention from both civil and military sectors,due to its remarkable merits in functionality and flexibility for accomplishing complex extensive tasks,e.g.,search and rescue,fire-fighting,reconnaissance,and *** path planning(CPP)is a key problem for a UAV group in executing tasks *** this paper,an attempt is made to perform a comprehensive review of the research on CPP for UAV ***,a generalized optimization framework of CPP problems is proposed from the viewpoint of three key elements,i.e.,task,UAV group,and environment,as a basis for a comprehensive classification of different types of CPP *** following the proposed framework,a taxonomy for the classification of existing CPP problems is proposed to describe different kinds of CPPs in a unified ***,a review and a statistical analysis are presented based on the taxonomy,emphasizing the coordinative elements in the existing CPP *** addition,a collection of challenging CPP problems are provided to highlight future research directions.

关键词： Unmanned aerial vehicle group Cooperation Path planning Optimization problem

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decision-Making and Path Planning for Highway Autonomous Driving Based on Spatio-Temporal Lane-Change Gaps

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IEEE systems Journal 2022年第2期16卷 3249-3259页

作者： Feng, Zhiqi Song, Wenjie Fu, Mengyin Yang, Yi Wang, Meiling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing100081 China

Safe and efficient decision-making and path planning is a challenging problem for autonomous driving in highway because of numerous dynamic vehicles around the ego-vehicle. For ego-vehicle driving on structured roads, behavioral decisions tend to pay more attention to the relative distribution of its surrounding vehicles, while path planning based on the decision requires more detailed consideration of vehicle model and lane curvature. Therefore, these two modules are separated in our article and executed successively in different coordinate systems. Specifically, we create a decision topology for lane changing or following decision based on spatio-temporal lane-change gaps in relative moving coordinate, which consists of the node representing the gap and the edges representing the connectivity between two gaps. Combining the decision topology with the behaviors of surrounding dynamic vehicles, a local cost map is generated and the target decision gap is selected. Once the decision is done, a smooth and safe trajectory from the ego-vehicle to the target decision gap, that satisfies the constraints of acceleration and curvature, is obtained in the geodetic coordinate through polynomial trajectory generation. The real-time performance and effectiveness of this method were verified in the dynamic high-speed interaction scenarios. © 2007-2012 IEEE.

关键词： decision making

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decision-making and Planning Framework with Prediction-Guided Strategy Tree Search Algorithm for Uncontrolled Intersections 25

Decision-making and Planning Framework with Prediction-Guide...

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25th IEEE International Conference on intelligent Transportation systems, ITSC 2022

作者： Zhang, Ting Fu, Mengyin Song, Wenjie Yang, Yi Wang, Meiling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing 100081 China Nanjing University of Science and Technology Nanjing 210014 China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468817

Uncontrolled intersections are important and challenging traffic scenarios for autonomous vehicles. Vehicles not only need to avoid collisions with dynamic vehicles instantaneously but also predict their behavior then make long-term decisions in reaction. To solve this problem, we propose a cooperative framework composed of a Primary Driver (PD) for motion planning and a Subordinate Driver (SD) for decision-making. SD is essentially the combination of a prediction module and a high-level behavior planner, which develops a prediction-guided strategy tree to determine the optimal action sequence. Especially, under the guidance of the prediction results, the tree branches are evaluated in security metrics, then get trimmed in action and observation space to reduce the dimensional complexity. With the assistance of SD, PD works as a collision checker and a low-level motion planner to generate a safe and smooth trajectory. We use the INTERACTION dataset to validate our method and achieve more than 90% success rate with efficiency improvement in various situations. © 2022 IEEE.

关键词： Uncertainty decision making Prediction algorithms Planning Trajectory complexity theory Behavioral sciences

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Active disturbance rejection control of servo platform using improved nonlinear control law

Active disturbance rejection control of servo platform using...

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International Conference on intelligent control and Information Processing (ICICIP)

作者： Hang Zhang State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

ISBN: (纸本)9781479936519

A new kind of nonlinear control law is proposed for the purpose of improving servo platform tracking performance under condition of rapid movement. On the basis of linear ADRC, an arctangent function is selected, and smoothly extended to an anti-S curve. The new method retains the feature of estimating and compensating the internal un-modeled dynamics and external disturbances. Simulation and comparison tests show that system using new control method has faster and more accurate tracking results.

关键词： PD control Servomotors Tuning Mathematical model Observers Hardware

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Motion Planning of Six-DOF Arm Robot Based on Improved DDPG Algorithm

Motion Planning of Six-DOF Arm Robot Based on Improved DDPG ...

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第三十九届中国控制会议

作者： Zhuang Li Hongbin Ma Yazhe Ding Chen Wang Ying Jin School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

This paper presents an improved deep deterministic policy gradient algorithm based on a six-DOF(six multi-degree-offreedom) arm robot. First, we build a robot model based on the DH(Denavit-Hartenberg) parameters of the UR5 arm robot. Then,we improved the experience pool of the traditional DDPG(deep deterministic policy gradient) algorithm by adding a success experience pool and a collision experience pool. Next, the reward function is improved to increase the degree of successful reward and the penalty of collision. Finally, the training is divided into segments, the front three axes are trained first, and then the six axes. The simulation results in ROS(Robot Operating System) show that the improved DDPG algorithm can effectively solve the problem that the six-DOF arm robot moves too far in the configuration space. The trained model can reach the target area in five steps. Compared with the traditional DDPG algorithm, the improved DDPG algorithm has fewer training episodes,but achieves better results.

关键词： Motion Planning Improved DDPG Six-DOF Arm Robot ROS

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CFSeRec:A Contrastive Framework for Sequential Recommendation

CFSeRec:A Contrastive Framework for Sequential Recommendatio...

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第42届中国控制会议

作者： Tong Wang Yaping Dai Shuai Shao School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

In sequential recommender systems,the main problems are the long-tailed distribution of data and noise interference.A Contrastive Framework for Sequential Recommendation(CFSeRec) is proposed to solve these two problems *** shuffling and adversarial attack data augmentation methods are used in the framework to improve the quality and quantity of training data,so that the long-tailed problem is *** the application of projection head method,the sequence representation becomes more general and robust,rather than just adapted to the task of contrastive ***,the impact of noise on sequence recommender systems is effectively *** on four public datasets show that CFSeRec achieves state-of-the-art performance in the metrics of hit ratio and normalized discounted cumulative gain,when comparing to the seven previous frameworks.

关键词：

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Path Tracking control of Tracked Paver Based on Improved Pure Pursuit Algorithm

Path Tracking Control of Tracked Paver Based on Improved Pur...

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第40届中国控制会议

作者： Shuai Wang Shanshan Fu Bin Li Shoukun Wang State Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology

This paper investigates the high-precision path tracking control of tracked paver combined with global satellite navigation *** the paver is performing paving operations,it requires high path tracking accuracy and good vehicle ***,considering the influence of road curvature on path tracking accuracy and vehicle stability,and the situation that the vehicle can not move quickly to the expectation path when the lateral position of the vehicle deviates from the expectation path,this paper proposes a lateral path tracking control method based on improved Pure Pursuit *** control method is verified through *** experimental results show that the maximum lateral tracking error of the improved algorithm is 0.04 m,which is 55.56% lower than that of the original algorithm,and the average lateral tracking error is 0.02 m,which is60% lower than that of the original *** purpose of high-precision path tracking of the paver is realized.

关键词： Tracked paver Path tracking Road curvature Pure Pursuit algorithm

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Two-Phase Hybrid Fuzzy Clustering Approach Using Membership Fusion

Two-Phase Hybrid Fuzzy Clustering Approach Using Membership ...

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第34届中国控制与决策会议

作者： Tian Gao Qinglin Wang Yaping Dai School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

In order to solve the problem that the clustering number in Fuzzy C-Means(FCM) needs to be set manually in advance,a two-phase hybrid fuzzy clustering approach using membership fusion(TPHFC) is *** the first phase,conventional FCM is used for *** the second phase,the results obtained by pre-clustering are fused according to the relationship between the membership of samples to different clusters and the membership threshold.A density-based clustering validity measurement is established for this *** proposed method obtains better clustering effect with setting fewer *** on synthetic datasets conforming to Gaussian distribution and UCI datasets demonstrate the effectiveness of the proposed clustering *** clustering number and clustering centers can be obtained adaptively.

关键词： Fuzzy Clustering Fuzzy C-Means Two-Stage Clustering Adaptive Parameter Adjustment

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Two Degree of Freedom Active Disturbance Rejection control Strategy for Time Delay systems

Two Degree of Freedom Active Disturbance Rejection Control S...

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第42届中国控制会议

作者： Xiyuan Shan Tao Cai School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

This paper is concerned with the controller design and the theoretical analysis for time-delay systems,a two degree of freedom(feedforward and feedback) control method is proposed,which combines advantages of the Smith predictor and the active disturbance rejection control(ADRC).The feedforward part of controller is used to track the set point,the feedback part of controller(ADRC) is used to suppress interferences and the Smith predictor is used to correct time *** proposed control design is easy to tune parameters and has been proved to effectively control systems with large time *** bounded input bounded output(BIBO) stability of closed-loop system is ***,numerical simulations show the effectiveness and practicality of the proposed design.

关键词： Time-delay Active Disturbance Rejection control(ADRC) Smith Predictor(SP)

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