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5th International Conference on Cognitive Systems and Signal processing, ICCSIP 2020

作者： Zhuang, Hanyang Wang, Chunxiang Qian, Yuhan Yang, Ming University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai200240 China Guangxi Key Laboratory of Automobile Components and Vehicle Technology Guangxi University of Science and Technology Liuzhou545006 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9789811623356

The development of autonomous driving technology proposes higher requirements of the fidelity of the high-definition maps (HD maps). The construction of HD map based on orthophotos generated from panoramic images is the state-of-the-art approach. However, in this process, the dynamic obstacles and shadows on the road captured by the panoramic camera has significant impact on reducing the map quality. Moreover, the GNSS signal may inevitably unavailable or have jitter error, leading to the unsatisfactory of the orthophoto mosaic. Therefore, an approach is proposed to tackle these problems of HD map construction. The semantic segmentation of the panoramic images is firstly implemented to extract the dynamic obstacles such as vehicles and the road segments. Then the shadows on the road segments are removed through GAN networks to generate clean orthophotos. Afterwards, the clean orthophotos are used for feature extraction and image registration based on the road segmentation to provide finer pose estimations. Finally, the GNSS data, odometer data, and estimated poses are combined to optimize the vehicle pose for orthophoto mosaic. The experimental results illustrate that the proposed approach can improve the HD map construction accuracy under the congested environment. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Image registration

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Temperature prediction of generator carbon brush based on LSTM neural network

Temperature prediction of generator carbon brush based on LS...

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2021 China automation Congress, CAC 2021

作者： Zhang, Qing Jiao, Tingyang Ma, Yinxing Zhang, Youmin Wu, Xiaohui Wu, Pengyue Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an710048 China The Faculty of Computing Science The University of Alberta EdmontonABT6G 2R3 Canada Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi 'an University of Technology Xi'an710048 China Department of Mechanical Industrial and Aerospace Engineering Concordia University MontrealQCH3G 1M8 Canada Huaneng Weihai Power Generation Co. Ltd Weihai264200 China Xi 'an Thermal Power Research Institute Co. Ltd Xi'an710048 China

ISBN: (纸本)9781665426473

In order to improve the prediction accuracy of the carbon brush temperature trend of the generator, analyzing the operating state of the generator more accurately, and reduce other accidents like the unplanned outage caused by carbon brush failure of the generator in the power plant. A multi-step temperature prediction model based on the LSTM (Long Short-Term Memory) neural network is proposed. The data comes from real operating carbon brush temperature of Weihai Power Plant in ten days. Then the temperature prediction model is established to achieve an accurate prediction of the carbon brush temperature in the future day. The prediction error is stable within 0.4 °C. By comparing the predicted results of the BP model and Elman model, the error between the predicted results of each model and the actual temperature data is analyzed. By comparing the indicators and analyzing the actual curves, the results show that LSTM neural network has higher accuracy in predicting the temperature of generator carbon brush. This method is of reference significance for the accurate analysis of the operation state of the generator and the load analysis of the excitation system. © 2021 IEEE

关键词： Long short-term memory

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Robust Optimization for Quantum Reinforcement Learning control using Partial Observations

arXiv

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arXiv 2022年

作者： Jiang, Chen Pan, Yu Wu, Zheng-Guang Gao, Qing Dong, Daoyi State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou310027 China The School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China The School of Engineering and Information Technology University of New South Wales CanberraACT2600 Australia

The current quantum reinforcement learning control models often assume that the quantum states are known a priori for control optimization. However, full observation of quantum state is experimentally infeasible due to the exponential scaling of the number of required quantum measurements on the number of qubits. In this paper, we investigate a robust reinforcement learning method using partial observations to overcome this difficulty. This control scheme is compatible with near-term quantum devices, where the noise is prevalent and predetermining the dynamics of quantum state is practically impossible. We show that this simplified control scheme can achieve similar or even better performance when compared to the conventional methods relying on full observation. We demonstrate the effectiveness of this scheme on examples of quantum state control and quantum approximate optimization algorithm. It has been shown that high-fidelity state control can be achieved even if the noise amplitude is at the same level as the control amplitude. Besides, an acceptable level of optimization accuracy can be achieved for QAOA with noisy control Hamiltonian. This robust control optimization model can be trained to compensate the uncertainties in practical quantum computing. © 2022, CC BY.

关键词： Robust control

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fMRI-based Brain Disease Diagnosis: A Graph Network Approach

TechRxiv

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TechRxiv 2023年

作者： Chen, Qi-Qi Gao, Yubing Tong, Wei Li, Ping Wu, Edmond Q. The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Nanjing University of Science and Technology Jiangsu210008 China The School of Biological Science and Medical Engineering Beihang University Beijing100191 China The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai200240 China The Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

As a non-invasive brain imaging technology, functional magnetic resonance imaging (fMRI) provides a basic tool for brain functional network modeling and brain disease diagnosis. Problems, such as large number of parameters, low training efficiency, and poor interpretability, are encountered in mainstream models because of the high complexity of fMRI and brain networks. To solve these problems, a novel structure feature combined graph neural network (SFC-GNN) with a low number of parameters is proposed. In particular, SFC-GNN is composed of 1) the graph convolution layer of the brain region perception and 2) the node pooling layer of the graph structure feature (GSF). It also receives the sparse brain graph modeled by each subject’s fMRI as input. Especially, the GSF layer can select brain regions that are important for classification, thereby localizing all active regions related to brain disease. Moreover, a group network is constructed according to the correlation among subjects, and SFC-GNN can be extended further to a node classification model to achieve better diagnosis performance. The proposed method has been validated on the ABIDE and ADNI datasets, thereby showing the effectiveness of our proposed method in various experiments. © 2023, CC BY.

关键词： Magnetic resonance imaging

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An Integrated Neural Unmanned System for Multi-Domain Perception and Decision-Making

An Integrated Neural Unmanned System for Multi-Domain Percep...

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IEEE International Conference on Robotics and Biomimetics

作者： Qiming Liu Neng Xu Jinpeng Zhang Hesheng Wang Department of Automation Shanghai Jiao Tong University Shanghai China SJTU Paris Elite Institute of Technology Shanghai Jiao Tong University Shanghai China Second Academy of China Aerospace Science and Industry Corporation (CASIC) Beijing China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665481106

Most recent intelligent systems study the perception or policy modules independently but ignore the inter-connection between them, which results in low information efficiency and high network redundancy, in the end, leads to the incomplete utilization of network potential and poor system-level end-to-end intelligence. This paper proposes an integrated end-to-end network that adapts to multi-domain inputs and multi-function outputs. “Integration” is defined in this paper in mainly three aspects: network structure level, optimization level, and functional level. The integrated network architecture is capable of fusing multiple raw sensory inputs and outputting multiple perceptions and decisions simultaneously. We first implement the integrated system in a virtual simulation environ-ment and investigate the domain transformation methods to real world. Comprehensive experiment results in both virtual and real environments show that our proposed system can behave normally under exploration, target-driven navigation and target shooting tasks.

关键词： Training Point cloud compression Target tracking Navigation Robot kinematics Redundancy Robot sensing systems

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Large-scale Multiobjective Optimization via Problem Decomposition and Reformulation

Large-scale Multiobjective Optimization via Problem Decompos...

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Congress on Evolutionary Computation

作者： Lianghao Li Cheng He Ran Cheng Linqiang Pan Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

Large-scale multiobjective optimization problems (LSMOPs) are challenging for existing approaches due to the complexity of objective functions and the massive volume of decision space. Some large-scale multiobjective evolutionary algorithms (LSMOEAs) have recently been proposed, which have shown their effectiveness in solving some benchmarks and real-world applications. They merely focus on handling the massive volume of decision space and ignore the complexity of LSMOPs in terms of objective functions. The complexity issue is also important since the complexity grows along with the increment in the number of decision variables. Our previous study proposed a framework to accelerate evolutionary large-scale multiobjective optimization via problem reformulation for handling large-scale decision variables. Here, we investigate the effectiveness of LSMOF combined with decomposition-based MOEA (MOEA/D), aiming to handle the complexity of LSMOPs in both the decision and objective spaces. Specifically, MOEA/D is embedded in LSMOF via two different strategies, and the proposed algorithm is tested on various benchmark LSMOPs. Experimental results indicate the encouraging performance improvement benefited from the solution of the complexity issue in large-scale multiobjective optimization.

关键词： Sociology Evolutionary computation Benchmark testing Linear programming Complexity theory Statistics Optimization

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Error-free approximation of explicit linear MPC through lattice piecewise affine expression

arXiv

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arXiv 2021年

作者： Xu, Jun Lou, Yunjiang De Schutter, Bart Xiong, Zhenhua School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Delft Center for Systems and Control Delft University of Technology Netherlands State Key Laboratory of Mechanical Systems and Vibration Shanghai Jiao Tong University Shanghai China

In this paper, the disjunctive and conjunctive lattice piecewise affine (PWA) approximations of explicit linear model predictive control (MPC) are proposed. The training data are generated uniformly in the domain of interest, consisting of the state samples and corresponding affine control laws, based on which the lattice PWA approximations are constructed. Re-sampling of data is also proposed to guarantee that the lattice PWA approximations are identical to the explicit MPC control law in the unique order (UO) regions containing the sample points as interior points. Additionally, if all the distinct affine functions have been sampled, the disjunctive lattice PWA approximation constitutes a lower bound while the conjunctive lattice PWA approximation formulates an upper bound of the original optimal control law. The equivalence of the two lattice PWA approximations then guarantees that the approximations are error-free in the domain of interest, which is tested through a statistical guarantee. The complexity of the entire procedure is analyzed, which is polynomial with respect to the number of samples. The performance of the proposed approximation strategy is tested through two simulation examples, and the results show that with a moderate number of sample points, we can construct lattice PWA approximations that are equivalent to the optimal control law of the explicit linear MPC. © 2021, CC0.

关键词： Model predictive control

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Finite control Set Model Predictive Position control of PMSM System

Finite Control Set Model Predictive Position Control of PMSM...

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第33届中国控制与决策会议

作者： Zhiqiang Wang Xiuyun Zhang Jie Bian School of Automobile and Transportation Tianjin University of Technology and Education School of Automation and Electrical Engineering Tianjin University of Technology and Education Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education Yunnan Industrial technician college

Servo drivers used for position control require higher dynamic performance in speed *** order to improve the dynamic response speed and position tracking accuracy of permanent magnet synchronous motor(PMSM) system,this paper proposes a control strategy for servo system based on finite control ***,the mathematical model of the PMSM system is established;then the reference voltage vector is predicted,and a speed expectation correction method is adopted to take into account system stability and fast followability;then the difference between the reference voltage vector and the candidate voltage vector is used as the improved cost function,and the selection of the candidate voltage vector and the optimization of the cost function are performed,to eliminate the complexity caused by the tuning of the weight *** that,considering the actual user needs,the current limit is integrated into the selection of the candidate voltage vector to achieve the current ***,the simulation research prove that the proposed control strategy can meet the requirements of dynamic response speed and position tracking accuracy.

关键词： Finite control set model predictive control(FCS-MPC) position control PMSM

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Predictive Speed Synchronous control of Dual Permanent Magnet Motor System Based on Quadratic Cost Function

Predictive Speed Synchronous Control of Dual Permanent Magne...

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第33届中国控制与决策会议

作者： Xiuyun Zhang Zhiqiang Wang Jie Bian School of Automation and Electrical Engineering Tianjin University of Technology and Education Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education School of Automobile and Transportation Tianjin University of Technology and Education Yunnan Industrial technician college

In order to solve the problem of reliance on manual experience and long time consuming for the weight coefficient setting in the predictive speed synchronous control of the dual permanent magnet motor system,this paper proposes a finite control set model predictive control strategy based on quadratic cost ***,based on the idea of unified modeling,the motion equation and electrical equation of the driving motor,the mathematical model of the two-level voltage source inverter are combined to establish a unified ***,the offline solution algorithm is used based on Lyapunov stability analysis to realize the self-tuning of the weight coefficient matrix,ensuring the convergence of each error term in the consecutive period;After that,the adjusted weight coefficient matrix is used for the online rolling optimization process of the predictive speed synchronous controller,and the cost function optimization is finally *** considering the actual user needs,the current limit is added to the ***,the simulation research prove that the proposed control strategy has fast dynamic response speed and synchronous tracking accuracy.

关键词： Finite control set model predictive control(FCS-MPC) synchronous control permanent magnet synchronous motor(PMSM)

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Non-cascaded Predictive Speed Synchronous control of Dual Permanent Magnet Motor System without Weight Coefficient Tuning

Non-cascaded Predictive Speed Synchronous Control of Dual Pe...

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第33届中国控制与决策会议

作者： Xiuyun Zhang Zhiqiang Wang Xiaolin Li Jie Bian School of Automation and Electrical Engineering Tianjin University of Technology and Education Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education School of Automobile and Transportation Tianjin University of Technology and Education Yunnan Industrial technician college

In order to improve the synchronous tracking accuracy of the dual permanent magnet motor system,this paper proposes a control strategy for dual permanent magnet motor system based on finite control ***,based on the idea of unified modeling,the motion equation and electrical equation of the driving motor,the mathematical model of the two-level voltage source inverter are combined to establish a unified ***,the reference voltage vectors with current and speed information are *** order to solve the problem of weight coefficient tuning in the traditional cost function,and simplify the complexity of the algorithm,the improved cost function is determined according to the ratio of contour error and tracking error,then the candidate voltage vectors are selected and the cost function is ***,the simulation research proves that the proposed control strategy can meet the dynamic and steady-state demand and synchronous control accuracy without weight coefficient tuning.

关键词： Finite control set model predictive control(FCS-MPC) synchronous control permanent magnet synchronous motor(PMSM)

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