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What will robots be like in the future?

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National Science Review 2019年第5期6卷 1059-1061页

作者： Yanfeng Lu Weijie Zhao the State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

Robots are changing our lives:sweeping robots patrol our living rooms; interactive robots accompany our children; industrial robots assemble vehicles; rescue robots search and save lives in catastrophes; medical robots perform surgeries in hospitals.To better understand robots’challenges and impact, National Science Review (NSR) interviewed Professor Toshio Fukuda, who is one of the world’s most representative robotics experts and has developed a number of bionic robots and micro/nano-robots.

关键词： NSR China What will robots be like in the future

Sliding Mode control for Flexible-link Manipulators Based on Adaptive Neural Networks

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International Journal of Automation and computing 2018年第2期15卷 239-248页

作者： Hong-Jun Yang Min Tan State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

This paper mainly focuses on designing a sliding mode boundary controller for a single flexible-link manipulator based on adaptive radial basis function （RBF） neural network. The flexible manipulator in this paper is considered to be an Euler-Bernoulli beam. We first obtain a partial differential equation （PDE） model of single-link flexible manipulator by using Hamiltons approach. To improve the control robustness, the system uncertainties including modeling uncertainties and external disturbances are compensated by an adaptive neural approximator. Then, a sliding mode control method is designed to drive the joint to a desired position and rapidly suppress vibration on the beam. The stability of the closed-loop system is validated by using Lyapunov＇s method based on infinite dimensional model, avoiding problems such as control spillovers caused by traditional finite dimensional truncated models. This novel controller only requires measuring the boundary information, which facilitates implementation in engineering practice. Favorable performance of the closed-loop system is demonstrated by numerical simulations.

关键词： Sliding mode control adaptive control neural network flexible manipulator partial differential equation （PDE）.

Precise planar motion measurement of a swimming multi-joint robotic fish

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Science China(Information Sciences) 2016年第9期59卷 183-197页

作者： Jun YUAN Junzhi YU Zhengxing WU Min TAN State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

This paper presents a method for planar motion measurement of a swimming multi-joint robotic fish. The motion of the robotic fish is captured via image sequences and a proposed tracking scheme is employed to continuously detect and track the robotic fish. The tracking scheme initially acquires a rough scope of the robotic fish and thereafter precisely locates it. Historical motion information is utilized to determine the rough scope, which can speed up the tracking process and avoid possible ambient interference. A combination of adaptive bilateral filtering and k-means clustering is then applied to segment out color markers accurately. The pose of the robotic fish is calculated in accordance with the centers of these markers. Further, we address the problem of time synchronization between the on-board motion control system of the robotic fish and the motion measurement system. To the best of our knowledge, this problem has not been tackled in previous research on robotic fish. With information about both the multi-link structure and motion law of the robotic fish, we convert the problem to a nonlinear optimization problem, which we then solve using the particle swarm optimization(PSO) algorithm. Further, smoothing splines are adopted to fit curves of poses versus time, in order to obtain a continuous motion state and alleviate the impact of noise. Velocity is acquired via a temporal derivative operation. The results of experiments conducted verify the efficacy of the proposed method.

关键词： motion measurement robotic fish time synchronization visual tracking localization

Policy iteration optimal tracking control for chaotic systems by using an adaptive dynamic programming approach

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Chinese Physics B 2015年第3期24卷 87-94页

作者：魏庆来刘德荣徐延才 The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

A policy iteration algorithm of adaptive dynamic programming（ADP） is developed to solve the optimal tracking control for a class of discrete-time chaotic systems. By system transformations, the optimal tracking problem is transformed into an optimal regulation one. The policy iteration algorithm for discrete-time chaotic systems is first described. Then,the convergence and admissibility properties of the developed policy iteration algorithm are presented, which show that the transformed chaotic system can be stabilized under an arbitrary iterative control law and the iterative performance index function simultaneously converges to the optimum. By implementing the policy iteration algorithm via neural networks,the developed optimal tracking control scheme for chaotic systems is verified by a simulation.

关键词： adaptive critic designs,adaptive dynamic programming,approximate dynamic programming,neuro-dynamic programming

Feature Selection and Feature Learning for High-dimensional Batch Reinforcement Learning: A Survey

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International Journal of Automation and computing 2015年第3期12卷 229-242页

作者： De-Rong Liu Hong-Liang Li Ding Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

Tremendous amount of data are being generated and saved in many complex engineering and social systems every day.It is significant and feasible to utilize the big data to make better decisions by machine learning techniques. In this paper, we focus on batch reinforcement learning(RL) algorithms for discounted Markov decision processes(MDPs) with large discrete or continuous state spaces, aiming to learn the best possible policy given a fixed amount of training data. The batch RL algorithms with handcrafted feature representations work well for low-dimensional MDPs. However, for many real-world RL tasks which often involve high-dimensional state spaces, it is difficult and even infeasible to use feature engineering methods to design features for value function approximation. To cope with high-dimensional RL problems, the desire to obtain data-driven features has led to a lot of works in incorporating feature selection and feature learning into traditional batch RL algorithms. In this paper, we provide a comprehensive survey on automatic feature selection and unsupervised feature learning for high-dimensional batch RL. Moreover, we present recent theoretical developments on applying statistical learning to establish finite-sample error bounds for batch RL algorithms based on weighted Lpnorms. Finally, we derive some future directions in the research of RL algorithms, theories and applications.

关键词： Intelligent control,reinforcement learning,adaptive dynamic programming,feature selection,feature learning,big data.

The DAO to Metacontrol for Metasystems in Metaverses:The System of Parallel control systems for Knowledge Automation and control Intelligence in CPSS

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1899-1908页

作者： Fei-Yue Wang The state key laboratory for management and control of complex systems Chinese Academy of SciencesBeijing100190Chi the Macao Institute of systems Engineering Macao University of Science and TechnologyMacao999078China

An investigation and outline of Metacontrol and Decontrol in Metaverses for control intelligence and knowledge automation are presented.Prescriptive control with prescriptive knowledge and parallel philosophy is proposed as the starting point for the new control philosophy and technology,especially for computational control of metasystems in cyberphysical-social systems.We argue that circular causality,the generalized feedback mechanism for complex and purposive systems,should be adapted as the fundamental principle for control and management of metasystems with metacomplexity in metaverses.Particularly,an interdisciplinary approach is suggested for Metacontrol and Decontrol as a new form of intelligent control based on five control metaverses:MetaVerses,MultiVerses,InterVerses,TransVerse,and DeepVerses.

关键词： Metacontrol Metasystems Metaverses CPSS DAO Decontrol Prescriptive control Parallel control Computational control Knowledge Automation control Intelligence Parallel Intelligence

Deep reinforcement learning for optimal denial-of-service attacks scheduling

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Science China(Information Sciences) 2022年第6期65卷 98-106页

作者： Fangyuan HOU Jian SUN Qiuling YANG Zhonghua PANG state key laboratory of Intelligent control and Decision of complex systems Beijing Institute of Technology key laboratory of Fieldbus Technology and Automation of Beijing North China University of Technology

We consider an optimal denial-of-service(DoS) attack scheduling problem of N independent linear time-invariant processes, where sensors have limited computational capability. Sensors transmit measurements to the remote estimator via a communication channel that is exposed to DoS attackers. However,due to limited energy, an attacker can only attack a subset of sensors at each time step. To maximally degrade the estimation performance, a DoS attacker needs to determine which sensors to attack at each time step. In this context, a deep reinforcement learning(DRL) algorithm, which combines Q-learning with a deep neural network, is introduced to solve the Markov decision process(MDP). The DoS attack scheduling optimization problem is formulated as an MDP that is solved by the DRL algorithm. A numerical example is provided to illustrate the efficiency of the optimal DoS attack scheduling scheme using the DRL algorithm.

关键词： optimal denial-of-service attack scheduling optimization limited energy deep reinforcement learning

PLS-CCA Heterogeneous Features Fusion-based Low-resolution Human Detection Method for Outdoor Video Surveillance

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International Journal of Automation and computing 2017年第2期14卷 136-146页

作者： Hong-Kai Chen Xiao-Guang Zhao Shi-Ying Sun Min Tan State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

In this paper, we focus on low-resolution human detection and propose a partial least squares-canonical correlation analysis （PLS-CCA） for outdoor video surveillance. The analysis relies on heterogeneous features fusion-based human detection method. The proposed method can not only explore the relation between two individual heterogeneous features as much as possible, but also can robustly describe the visual appearance of humans with complementary information. Compared with some other methods, the experimental results show that the proposed method is effective and has a high accuracy, precision, recall rate and area under curve （AUC） value at the same time, and offers a discriminative and stable recognition performance.

关键词： Low-resolution human detection partial least squares canonical correlation analysis heterogeneous features outdoorvideo surveillance.

Design and attitude control of a novel robotic jellyfish capable of 3D motion

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Science China(Information Sciences) 2019年第9期62卷 182-184页

作者： Junzhi YU Xiangbin LI Lei PANG Zhengxing WU State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences

Aquatic creatures such as fish, cetaceans, and jellyfish could inspire innovative designs to improve the ways that manmade systems operate in and interact with aquatic environments [1, 2]. Jellyfish in nature use jet propulsion to move through the water, which have been proven to be one of the most energetically efficient swimmers on the planet [3]. Researchers are making an integrated effort to develop smart actuators to fabricate var-

关键词： bar Design and attitude control of a novel robotic jellyfish capable of 3D motion