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第三十四届中国控制会议

作者： Yin Yu Zhen Li Xiangdong Liu Bing Liu School of Automation Beijing Institute of Technology Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology

ISBN: (纸本)9781467374439

The state estimation of general nonlinear systems is investigated through a parameter-dependent approach. This paper adopts the polytopic approximation for the nonlinear systems, which facilitates the application of analytical approaches for resultant convex-bounded linear systems into the given nonlinear systems. The design of a high performance H2 state estimator is firstly determined by solving a convex optimization problem constrained by relaxed linear matrix inequalities(LMIs). The tensor product(TP) model transformation is then adopted to obtain the polytopic linearization. Specifically, A new process is presented to correct the result of the TP model transformation such that a necessary condition to solve the state estimation problem is assured. Finally, a numerical simulation is performed to illustrate the design process and verify the performance.

关键词： Nonlinear state estimation,parameter-dependent H2 filter,linear matrix inequalities(LMIs),tensor product(TP) model transformation

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A Dynamic EMG-torque Model of Elbow Based on Neural Networks

A Dynamic EMG-torque Model of Elbow Based on Neural Networks

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Liang Peng Zeng-Guang Hou Weiqun Wang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781424492695

In this paper, a dynamic EMG-torque model of the elbow joint is developed based on ANN, and two novel test methods are proposed to validate its generalization performance. A time-delay neural network (TDNN) model is built and proved to have less risk of overfitting than the most-used multilayer feedfoward neural network (MFNN) model for dynamic EMG-torque modeling. Both EMG and kinematic features are included in the input of ANN, but the zero-EMG test shows that the trained ANN is part of the inverse joint dynamics rather than the EMG-torque model, and some random samples for ANN training are added to overcome this problem. The single-muscle test shows that an inappropriate choice of the motion type may cause the model to estimate wrong torque directions. After tuning and testing, the root mean square error (RMSE) across all subjects is 0.60±0.20 N.m.

关键词： EMG-torque Model Neural Networks RMSE Neural network ROOT-MEAN-SQUARE ERRORS Artificial neural networks Elbow Elbow Joint random sample Test Method

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Collision-free and kinematically feasible path planning along a reference path for autonomous vehicle

Collision-free and kinematically feasible path planning alon...

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IEEE Symposium on Intelligent Vehicle

作者： Mengyin Fu Kai Zhang Yi Yang Hao Zhu Meiling Wang Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing P.R.China

For the local path planning problem of autonomous vehicle in a complicated environment, a method combining cubic hermite spline curves with the kinematic model of autonomous vehicle is developed. And a novel algorithm for obstacle avoidance, called navigation circle, is proposed to take the road structure into account, which is a practical method for real-time path planning. In the new method, one of the trajectory generated by cubic hermite spline curves or navigation circle is optimized through the kinematic model of autonomous vehicle to get the kinematically feasible trajectory. The optimization is actually a numerical forward propagation and is easy to implement. The simulation experiment is conducted on the Robot Operating System (ROS) platform, which is based on replaying the data of the real world obtained from sensors or other modules on autonomous vehicle. Satisfactory simulation results verify the validity and the efficiency of the proposed method as well as the planner's capability to navigate in a realistic scenario.

关键词： Trajectory Vehicles Splines (mathematics) Navigation Mobile robots Planning

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Modeling the stroke process in table tennis robot using neural network

Modeling the stroke process in table tennis robot using neur...

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IEEE International Conference on Robotics and Biomimetics

作者： Kun Zhang Zaojun Fang Jianran Liu State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781467396769

To hit incoming balls back to a desired position, it is a key factor for table tennis robot to get racket parameters accurately. For modeling the stroke process, a novel model is built based on multiple neural networks. The input data for neural networks are the ball velocity differences during the stroke, and racket parameters are the output data. To reduce the influences from the invalid data, a neural network based on each empirical data is established. The training data are clustered based on the empirical data. The way of choosing a neural network to compute the racket parameters depends on the comparison between the new coming data and the empirical data. Moreover, a novel way based on a binocular vision system to verify the stroke model is proposed. Experimental results have showed that the stroke model created via the proposed method is applicable and the verification method is effective.

关键词： modeling stroke process binocular

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Online Reinforcement Learning by Bayesian Inference

Online Reinforcement Learning by Bayesian Inference

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International Joint Conference on Neural Networks

作者： Zhongpu Xia Dongbin Zhao The State Key Laboratory of Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781479919611

Policy evaluation has long been one of the core issues of the online reinforcement learning, especially in the continuous state domain. In this paper, the issue is addressed by employing Gaussian processes to represent the action value function from the probability perspective. By modeling the return as a stochastic variable, the action value function can sequentially update according to observed variables such as state and reward by Bayesian inference during the policy evaluation. The update rule shows that it is a temporal difference learning method with the learning rate determined by the uncertainty of a collected sample. Incorporating the policy evaluation method with the e-greedy action selection method, we propose an online reinforcement learning algorithm referred as to Bayesian-SARSA. It is tested on some benchmark problems and the empirical results verifies its effectiveness.

关键词： Online Reinforcement Learning Bayesian Inference core issues

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Adaptive Dynamic Programming for Infinite Horizon Optimal Robust Guaranteed Cost control of a Class of Uncertain Nonlinear systems

Adaptive Dynamic Programming for Infinite Horizon Optimal Ro...

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American control Conference

作者： Ding Wang Derong Liu Hongliang Li Hongwen Ma The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781479917730

In this paper, an infinite horizon optimal robust guaranteed cost control scheme of a class of continuous-time uncertain nonlinear systems is established based on adaptive dynamic programming. The main idea lies in that the optimal robust guaranteed cost control problem can be transformed into an optimal control problem. Actually, the optimal cost function of the nominal system is nothing but the optimal guaranteed cost of the original uncertain system. A critic neural network is constructed to help solving the modified Hamilton-Jacobi-Bellman equation corresponding to the nominal system. Then, an additional stabilizing term is introduced to reinforce the updating process of the weight vector and reduce the requirement of an initial stabilizing control. An example is provided to illustrate the effectiveness of the present control approach.

关键词： Cost function Robustness Nonlinear systems Optimal control Feedback control Biological neural networks

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Analysis and Forecasting of Urban Traffic Condition Based on Categorical Data

Analysis and Forecasting of Urban Traffic Condition Based on...

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第七届全国平行管理会议

作者： Yuan-yuan Chen Yisheng Lv State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190China

Urban traffic prediction is a critical component in intelligent transportation systems for both citizens and traffic management *** is beneficial to know current and future traffic conditions prior a trip or a route for *** it is also very helpful for proactive traffic management for transportation administrative *** this paper,we apply classification techniques to forecast traffic conditions based on categorical data collected from open web *** this end,we first collect traffic condition data from AMAP which is a web map,navigation and location based services provider in *** we primarily analyze AMAP data with trend analysis and power spectrum ***,we employ random walk,na(i)ve Bayes,decision tree and support vector machine methods to forecast traffic conditions in the future based on historical and current *** results demonstrate that it is feasible to make forecast on traffic conditions with reasonable accuracy.

关键词： urban traffic prediction traffic condition intelligent transportation systems route planning

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MRF based text binarization in complex images using stroke feature

MRF based text binarization in complex images using stroke f...

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International Conference on Document Analysis and Recognition

作者： Yanna Wang Cunzhao Shi Baihua Xiao Chunheng Wang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences (CASIA) Beijing China

ISBN: (纸本)9781479918065

This paper presents a novel binarization technique for text images based on Markov Random Field (MRF) framework. We regard stroke as an obvious feature of text to produce clustering result, which will be optimized by MRF model combining color, texture, context features to get the final binarization. The main innovations of our method are: (1) the integrated image is split into sub-images on which we can automatically acquire seed pixels of foreground and background using stroke feature; and (2) diverse weights are attached to seed pixels according to their location information, then highly confident cluster centers of sub-image can be acquired by gathering weighted seeds. The experimental results show that our method is robust and accurate on both video and scene images.

关键词： Image color analysis Image edge detection Robustness

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Sliding mode control based guidance law with impact angle constraint

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Chinese Journal of Aeronautics 2014年第1期27卷 145-152页

作者： Zhao Yao Sheng Yongzhi Liu Xiangdong School of Automation Beijing Institute of Technology Key Laboratory for Intelligent Control & Decision of Complex Systems Beijing Institute of Technology

The terminal guidance problem for an unpowered lifting reentry vehicle against a sta- tionary target is considered. In addition to attacking the target with high accuracy, the vehicle is also expected to achieve a desired impact angle. In this paper, a sliding mode control （SMC）-based guidance law is developed to satisfy the terminal angle constraint. Firstly, a specific sliding mode function is designed, and the terminal requirements can be achieved by enforcing both the sliding mode function and its derivative to zero at the end of the flight. Then, a backstepping approach is used to ensure the finite-time reaching phase of the sliding mode and the analytic expression of the control effort can be obtained. The trajectories generated by this method only depend on the initial and terminal conditions of the terminal phase and the instantaneous states of the vehicle. In order to test the performance of the proposed guidance law in practical application, numerical simulations are carried out by taking all the aerodynamic parameters into consideration. The effec- tiveness of the proposed guidance law is verified by the simulation results in various scenarios.

关键词： Backstepping Impact angle Sliding mode control Terminal guidance Unpowered lifting reentryvehicle

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High-order sliding mode attitude controller design for reentry flight

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Journal of systems Engineering and Electronics 2014年第5期25卷 848-858页

作者： Liang Wang Yongzhi Sheng Xiangdong Liu School of Automation Beijing Institute of Technology Key Laboratory for Intelligent Control & Decision of Complex Systems Beijing Institute of Technology

A novel high-order sliding mode control strategy is proposed for the attitude control problem of reentry vehicles in the presence of parametric uncertainties and external disturbances, which results in the robust and accurate tracking of the aerodynamic angle commands with the finite time convergence. The proposed control strategy is developed on the basis of integral sliding mode philosophy, which combines conventional sliding mode control and a linear quadratic regulator over a finite time interval with a free-final-state and allows the finite-time establishment of a high-order sliding mode. Firstly, a second-order sliding mode attitude controller is designed in the proposed high-order siding mode control framework. Then, to address the control chattering problem, a virtual control is introduced in the control design and hence a third-order sliding mode attitude controller is developed, leading to the chattering reduction as well as the control accuracy improvement. Finally, simulation examples are given to illustrate the effectiveness of the theoretical results.

关键词： reentry vehicle attitude control high-order sliding mode control integral sliding mode.

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