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arXiv 2021年

作者： Wang, Changhao Kuang, Zhian Zhang, Xiang Tomizuka, Masayoshi Department of Mechanical Engineering University of California BerkeleyCA94720 United States Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin150001 China

Smooth behaviors are preferable for many contact-rich manipulation tasks. Impedance control arises as an effective way to regulate robot movements by mimicking a mass-spring-damping system. Consequently, the robot behavior can be determined by the impedance gains. However, tuning the impedance gains for different tasks is tricky, especially for unstructured environments. Moreover, online adapting the optimal gains to meet the time-varying performance index is even more challenging. In this paper, we present Safe Online Gain Optimization for Variable Impedance control (Safe OnGO-VIC). By reformulating the dynamics of impedance control as a control-affine system, in which the impedance gains are the inputs, we provide a novel perspective to understand variable impedance control. Additionally, we innovatively formulate an optimization problem with online collected force information to obtain the optimal impedance gains in real-time. Safety constraints are also embedded in the proposed framework to avoid unwanted collisions. We experimentally validated the proposed algorithm on three manipulation tasks. Comparison results with a constant gain baseline and an adaptive control method prove that the proposed algorithm is effective and generalizable to different scenarios. Experiment videos are available at https://***/research/***. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Collision Avoidance control with Motion Information for Multiple Tractor-trailer Vehicles

Collision Avoidance Control with Motion Information for Mult...

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第40届中国控制会议

作者： Wenxue Zhang Du?an M.Stipanovi? Di Zhou Department of Control Science and Engineering Harbin Institute of Technology Department of Industrial and Enterprise Systems Engineering and the Coordinated Science Laboratory University of Illinois at Urbana-Champaign

In this paper, a closed-form feedback control strategy is developed to guarantee the safety maneuvers for N tractortrailer vehicles with on-axle hitching structures, which resolves the non-cooperative collision avoidance problem between selfsegments and the cooperative one among multiple vehicles. Motion information of the vehicle chains is introduced to make the comprehensive assessments for the collision risk. This allows the multi-agent system under the proposed control laws to make the real-time decision for acquiring the smoother motion trajectories and better dynamic performance. Lyapunov-based stability analysis is provided to prove that the system states can converge to the desired points or remain within bounded position errors with respect to reference trajectories without collisions. A simulation is also presented to illustrate the effectiveness of the proposed control laws.

关键词： Tractor-trailer vehicle Multi-agent system Self collisions Obstacle collisions Motion information

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Deep-operator-network-based Mars entry parametric bank angle profile optimization

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Chinese Journal of Aeronautics 2025年

作者： Bo TANG Yanning GUO Youmin GONG Jie MEI Weiren WU School of Aerospace Science Harbin Institute of Technology Shenzhen 518055 China Department of Control Science and Engineering Harbin Institute of Technology Harbin 150001 China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen 518055 China Lunar Exploration Program and Space Engineering Center Beijing 100190 China

Rapid and reliable onboard optimization of bank angle profiles is crucial for mitigating uncertainties during Mars atmospheric entry. This paper presents a neural-network-accelerated methodology for optimizing parametric bank angle profiles in Mars atmospheric entry missions. The methodology includes a universal approach to handling path constraints and a reliable solution method based on the Particle Swarm Optimization (PSO) algorithm. For illustrative purposes, a mission with the objective of maximizing terminal altitude is considered. The original entry optimization problem is converted into optimizing three coefficients for the bank angle profiles with terminal constraints by formulating a parametric Mars entry bank angle profile and constraint handling methods. The parameter optimization problem is addressed using the PSO algorithm, with reliability enhanced by increasing the PSO swarm size. To improve computational efficiency, an enhanced Deep Operator Network (DeepONet) is used as a dynamics solver to predict terminal states under various bank angle profiles rapidly. Numerical simulations demonstrate that the proposed methodology ensures reliable convergence with a sufficiently large PSO swarm while maintaining high computational efficiency facilitated by the neural-network-based dynamics solver. Compared to the existing methodologies, this methodology offers a streamlined process, the reduced sensitivity to initial guesses, and the improved computational efficiency.

关键词： Mars entry Bank angle profile Particle swarm optimization Neural networks Operator learning

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An accurate unbalance prediction model for multi-stage disc-separated structure rotor with benchmark transformation

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Measurement 2025年 256卷

作者： Huilin Wu Weiwei Wei Chuanzhi Sun Qing Lu Limin Zou Jiubin Tan Center of Ultra-precision Optoelectronic Instrument Engineering Harbin Institute of Technology Harbin 150080 China Key Lab of Ultra-precision Intelligent Instrumentation Engineering (Harbin Institute of Technology) Ministry of Industry and Information Technology Harbin 150080 China The Department of Control Science and Engineering Harbin Institute of Technology Harbin 150001 China Beijing Power Machinery Institute Beijing 100074 China

Currently, there is a lack of a multi-stage rotor unbalance prediction model based on actual shafts, and the comprehensive influence of disc and blade errors results in unclear error propagation mechanisms. This prevents the establishment of an effective unbalance prediction model, thus hindering the precise prediction and regulation of unbalance. This paper proposes a multi-stage disc-separated rotor assembly unbalance prediction model based on the common axis of the engine’s front and rear shafts. The effects of disk positioning errors and blade assembly errors on unbalance are analyzed. Initially, an unbalance prediction model for a single blade of a single-stage rotor is established. Furthermore, based on the spatial vector summation principle, an unbalance propagation model for a single-stage disc-separated rotor with multiple arrayed blades is developed. Finally, by considering reference transformation and the gradual propagation of errors during the assembly process, a multi-stage disc-separated rotor unbalance propagation model is established, which is subsequently applied to the unbalance prediction and control of multi-stage disc-separated rotor assemblies. Experimental results show that, compared to direct assembly, the best unbalance result of a three-stage rotor after assembly control is improved by 61.8%, thereby achieving the regulation and optimization of unbalance in multi-stage disc-separated rotor assembly. This study proposed a multi-stage disc separation rotor unbalance prediction model including array blade assembly, which can provide guidance for unbalance prediction and assembly optimization in the assembly process of multi-stage disc separation structure rotors of aircraft engines, and has important application value for high-precision rotor unbalance control.

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A Novel Hybrid Algorithm Based on Lion Swarm Optimization and Differential Evolution Algorithm

SSRN

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SSRN 2022年

作者： Liu, Junfeng Wu, Yun Xu, Jian Liu, Xinggao Yang, Zhe State Key Laboratory of Industrial Control Technology Control Department Zhejiang University Hangzhou310027 China College of Science Jiujiang University Jiujiang332005 China Jiehua Holdings Co. Ltd Haining314419 China School of Information and Electrical Engineering Harbin Institute of Technology at Weihai Weihai264209 China

Lion swarm optimization (LSO) algorithm, a novel and efficient meta-heuristic optimization technique, has received considerable attention and also has been applied to solve science and engineering optimization problems for outstanding performance. However, like some other typical swarm optimization algorithms, LSO may also trap individuals into local regions, resulting in premature convergence. Furthermore, how to balance contradictions between exploration and exploitation is also a critical issue that must be addressed in the LSO study. Hence, to address the aforementioned problems, a novel LSO variant algorithm called "hybrid lion swarm optimization (LSO) with differential evolution (DE)", referred to as HLSOD, is proposed in this paper. First, LSO and DE are improved, respectively. For LSO, a nonhomogeneous cuckoo search strategy based on quantum mechanism is designed to enhance the exploitation capability. In contrast, a crossover strategy "crossover operator based on covariance matrix learning", which not only can alleviate the algorithm’s dependence upon the coordinate system to some extent but also enhances the algorithm’s ability to tackle problems with high correlation between the variables, is employed to efficiently maintain the population diversity. For DE, the mutation strategy "DE/current-to-pbest" with optional external archive, which can use historical data to provide information of progress direction, is introduced as the mutation operator. The crossover strategy "crossover operator based on covariance matrix learning" is used for the crossover operator. Moreover, the adaptive parameter settings, which can automatically update the control parameters to appropriate values and avoid a user’s prior knowledge of the relationship between the parameter settings and the optimization problems’ characteristics, are applied for the parameter settings of the mutation and crossover operators. Thus, it is helpful not only for balancing contradictions between exp

关键词： Covariance matrix

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A Dueling-Double-Deep Q-Network controller for Magnetic Levitation Ball System

A Dueling-Double-Deep Q-Network Controller for Magnetic Levi...

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第三十九届中国控制会议

作者： Yefeng Yang Xiaojun Ban Xianlin Huang Chenghao Shan Center for Control Theory and Guidance Technology Harbin Institute of Technology Institute of Marine Navigation Technology Harbin Engineering University

Deep reinforcement learning technique has attracted extensive attention in the field of control with the rapid development of artifical intelligence. Model-free control methods perform more effcient than traditional model-based control method for complex nonlinear system since they don’t require accurate models. In this study, by using the Dueling-Double-DQN（Dueling-DDQN, 3-DQN） algorithm, a self-learning controller is developed for the magnetic levitation ball system. The numerical simulation demonstrates the effectiveness of our controller. Moreover, we compare the above controller with the DQN-based self-learning controller. Numerical simulation shows that the attraction domain of the Dueling-DDQN controller is larger that the latter.

关键词： Reinforcement learning Magnetic levitation ball system Dueling-Double-DQN Model-free control

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Reliable Unknown Input Observer for Continuous-Time Linear Systems

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IFAC-PapersOnLine 2022年第6期55卷 248-253页

作者： Nacim Meslem Ahmad Hably Tarek Raïssi Zhenhua Wang Univ. Grenoble Alpes CNRS Grenoble INP GIPSA-lab 38000 Grenoble France Conservatoire National des Arts et Métiers 292 Rue Saint-Martin 75141 Paris France Department of Control Science and Engineering Harbin Institute of Technology Harbin 150001 P. R. China

This work proposes a set-valued extension of the classical unknown input observers for linear continuous-time systems without using set-by-step set-membership computations or imposing the positivity property of the dynamics of the estimation error. In fact, based on an explicit set-integration method of the estimation error, the framing and convergence properties of the proposed set-valued unknown input observer are demonstrated. Moreover, thanks to the proposed design approach, robust threshold on the residual can be established a priori . Simulation results are reported to highlight the effectiveness of the proposed state estimator in the presence of unknown inputs.

关键词： State estimation continuous-time linear systems unknown input observer interval observer set-membership estimation reachability analysis

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Joint Routing and Charging Problem of Electric Vehicles with Incentive-Aware Customers Considering Spatio-Temporal Charging Prices

SSRN

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SSRN 2022年

作者： Yao, Canqi Chen, Shibo Salazar, Mauro Yang, Zaiyue School of Mechatronics Engineering Harbin Institute of Technology Harbin150000 China Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering The Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen518055 China Control Systems Technology Group Department of Mechanical Engineering Eindhoven University of Technology EindhovenMB 5600 Netherlands

This paper investigates the scheduling problem of a fleet of electric vehicles, providing mobility as a service to a set of time-specified customers, where the operator needs to solve the routing and charging problem jointly for each EV. Hereby we consider incentive-aware customers and propose that the operator offers monetary incentives to customers in exchange for time flexibility. In this way, the fleet operator can achieve a routing and charging schedule with lower costs, whilst the customers receive monetary compensation for their flexibility. Specifically, we first propose a bi-level optimization model whereby the fleet operator optimizes the routing and charging schedule accounting for the spatio-temporal varying charging price, jointly with a monetary incentive to reimburse the delivery time flexibility experienced by the customers. Concurrently the customers choose their own time flexibility by minimizing their own cost. Second, we cope with the computational burden coming from this nonlinear bi-level optimization model with an accurate reformulation approach consisting of the KKT optimality conditions, a Big-M-based linearization method, and the zero duality gap of convex optimization problems. This way, we convert the proposed problem into a single-level optimization problem, which can be solved by a strengthened generalized Benders decomposition method holding a faster convergence rate than the generalized Benders decomposition method. To evaluate the effectiveness of the proposed mathematical model, we carry out numerous simulation experiments by using the VRP-REP data of Belgium. The numerical results showcase that the proposed mathematical model can reduce the delivery fees for the customers together with the cost of operation incurred by the fleet operator. © 2022, The Authors. All rights reserved.

关键词： Sales

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Joint Routing and Charging Problem of Electric Vehicles with Incentive-aware Customers Considering Spatio-temporal Charging Prices

arXiv

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arXiv 2022年

关键词： Sales

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A method for pulsar searching: Combining a two-dimensional autocorrelation profile map and a deep convolutional neural network

arXiv

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arXiv 2021年

作者： Wang, Long-Qi Jin, Jing Liu, Lu Shen, Yi Harbin Institute of Technology Department of Control Science and Engineering Room 502 Main Building 92 West Dazhi Street Nangang District Harbin150001 China

In pulsar astronomy, detecting effective pulsar signals among numerous pulsar candidates is an important research topic. Starting from space X-ray pulsar signals, the two-dimensional autocorrelation profile map (2D-APM) feature modelling method, which utilizes epoch folding of the autocorrelation function of X-ray signals and expands the time-domain information of the periodic axis, is proposed. A uniform setting criterion regarding the time resolution of the periodic axis addresses pulsar signals without any prior information. Compared with the traditional profile, the model has a strong anti-noise ability, a greater abundance of information and consistent characteristics. The new feature is simulated with double Gaussian components, and the characteristic distribution of the model is revealed to be closely related to the distance between the double peaks of the profile. Next, a deep convolutional neural network (DCNN) is built, named Inception-ResNet. According to the order of the peak separation and number of arriving photons, 30 data sets based on the Poisson process are simulated to construct the training set, and the observation data of PSRs B0531+21, B0540-69 and B1509-58 from the Rossi X-ray Timing Explorer (RXTE) are selected to generate the test set. The number of training sets and the number of test sets are 30,000 and 5,400, respectively. After achieving convergence stability, more than 99% of the pulsar signals are recognized, and more than 99% of the interference is successfully rejected, which verifies the high degree of agreement between the network and the feature model and the high potential of the proposed method in searching for pulsars. Copyright © 2021, The Authors. All rights reserved.

关键词： Pulsars

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