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2024 IEEE International Conference on systems, Man, and cybernetics, SMC 2024

作者： Miao, Zibo Pan, Yu Gao, Qing School of Mechanical Engineering and Automation Adaptive Robotics Harbin Institute of Technology Guangdong Key Laboratory of Intelligent Morphing Mechanisms Shenzhen518055 China The Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China The School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

ISBN: (纸本)9781665410205

In this paper we extend our previous research on coherent observer-based pole placement approach to study the synthesis of robust decoherence-free (DF) modes for linear quantum passive systems, which is aimed at preservation of quantum information. In particular, DF modes can be generated by placing the poles on the imaginary axis via a coherent feedback design scheme, and these modes can further be simultaneously made robust against perturbations to the system parameters by minimizing the condition number associated with imaginary poles. We develop explicit algebraic conditions for the existence of such a coherent quantum controller, with the corresponding deign procedure provided. Examples are given to illustrate the process of tuning the DF modes towards perfect robustness via the proposed pole placement technique. © 2024 IEEE.

关键词： Poles

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Robust Real-Time Motion Retargeting via Neural Latent Prediction

Robust Real-Time Motion Retargeting via Neural Latent Predic...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Tiantian Wang Haodong Zhang Lu Chen Dongqi Wang Yue Wang Rong Xiong State Key Laboratory of Industrial Control and Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

Human-robot motion retargeting is a crucial approach for fast learning motion skills. Achieving real-time retargeting demands high levels of synchronization and accuracy. Even though existing retargeting methods have swift calculation, they still cause time-delay effect on the synchronous retargeting. To mitigate this issue, this paper proposes a motion retargeting method guided by prediction, which effectively reduces the adverse impact of time-delay. The proposed pipeline contains motion retargeting in spatial-temporal graph-based structure and motion prediction in the latent space. The motion sequence retargeting builds mapping and paired data from human poses to corresponding robot configurations for training prediction model, and generated robot motion satisfies limit and self-collision constrains. The controller guided by prediction imports future robot joint motion to achieve advanced trajectory tracking, thereby compensating for delay time spent on calculation and tracking. Experimental results show that our method outperforms other methods in terms of synchronization and similarity. Furthermore, our method exhibits fault-tolerant capability in scenarios involving the loss of human information input.

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An MPC-based Optimal Motion control Framework for Pendulum-driven Spherical Robots

arXiv

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arXiv 2023年

作者： Hu, Tao Guan, Xiaoqing Wang, Yixu Liu, Yifan Zhang, Bixuan Lin, Boyu Wang, You Li, Guang State Key Laboratory of Industrial Control Technology Institute of Cyber Systems and Control Zhejiang University Hangzhou China

Motion control is essential for all autonomous mobile robots, and even more so for spherical robots. Due to the uniqueness of the spherical robot, its motion control must not only ensure accurate tracking of the target commands, but also minimize fluctuations in the robot’s attitude and motors’ current while tracking. In this paper, model predictive control (MPC) is applied to the control of spherical robots and an MPC-based motion control framework is designed. There are two controllers in the framework, an optimal velocity controller ESO-MPC which combines extend states observers (ESO) and MPC, and an optimal orientation controller that uses multilayer perceptron (MLP) to generate accurate trajectories and MPC with changing weights to achieve optimal control. Finally, the performance of individual controllers and the whole control framework are verified by physical experiments. The experimental results show that the MPC-based motion control framework proposed in this work is much better than PID in terms of rapidity and accuracy, and has great advantages over sliding mode controller (SMC) for overshoot, attitude stability, current stability and energy consumption. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion control

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Whole-body Dynamic Collision Avoidance with Time-varying control Barrier Functions

arXiv

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arXiv 2023年

作者： Huang, Jihao Chi, Xuemin Liu, Zhitao Su, Hongye The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

Recently, there has been increasing attention in robot research towards the whole-body collision avoidance. In this paper, we propose a safety-critical controller that utilizes time-varying control barrier functions (time varying CBFs) constructed by Robo-centric Euclidean Signed Distance Field (RC-ESDF) to achieve dynamic collision avoidance. The RC-ESDF is constructed in the robot body frame and solely relies on the robot’s shape, eliminating the need for real-time updates to save computational resources. Additionally, we design two control Lyapunov functions (CLFs) to ensure that the robot can reach its destination. To enable real-time application, our safety-critical controller which incorporates CLFs and CBFs as constraints is formulated as a quadratic program (QP) optimization problem. We conducted numerical simulations on two different dynamics of an L-shaped robot to verify the effectiveness of our proposed approach. Copyright © 2023, The Authors. All rights reserved.

关键词： controllers

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Autonomous Trajectory Planning Based on Bézier Curve with Curvature Constraints and Piecewise-Jerk Speed-Time Optimization

Autonomous Trajectory Planning Based on Bézier Curve with C...

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IEEE International Conference on Robotics and Biomimetics

作者： You Wang Ziyi Zou Ziang Zhang Xiaoqing Guan Boyu Lin Xiang Li Guang Li State Key Laboratory of Industrial Control Technology Institute of Cyber Systems and Control Zhejiang University Hangzhou China

In this paper, we propose an efficient trajectory planning algorithm with path smoothing based on the Bézier curve with curvature constraints and piecewise-jerk speed-time optimization. We use hybrid A* to generate a rough path and construct a safe corridor by inflating the path. After that, we formulate the smooth problem as a nonlinear programming(NLP) with piecewise Bézier curves. Since the curvature constraints for Bézier curves are difficult, we employ quartic Bézier Curves with special forms and compute the closed-form solution for the maximum curvature to simplify the representation of the maximum curvature. By using the special Bézier curves, we realize the gear shifts and easily guarantee the security, continuity, and feasibility of the path. Meanwhile, we add time variables based on PJSO, improving the quality of trajectory within an acceptable increase in time, making the allocation of time and speed better. Simulation and real-world experiments with a car-like robot in various environments confirm that our algorithm can generate a smooth, feasible, and high-quality trajectory for robots.

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Exact Output Regulation for the Receiver-Side Buck Converter of Electric Vehicle Dynamic Wireless Charging System

Exact Output Regulation for the Receiver-Side Buck Converter...

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Annual Conference of industrial Electronics Society

作者： Mengting Zhang Zhitao Liu Hongye Su State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

Dynamic wireless charging (DWC) of electric vehicles (EVs) is a promising technology that can promote the widespread of EVs. However, the output power fluctuation occurs due to the varying mutual inductance between the receiver coil and the transmitter coils caused by the EV motion, which would lead to control performance deterioration and even instability of the system. To mitigate the power fluctuation, this paper proposes a control strategy based on exact output regulation (EOR) theory for the receiver-side buck converter of the DWC system to compensate the disturbance arising from the mutual inductance fluctuation. The mutual inductance fluctuation can be approximated as a sinusoidal signal and the FEA analysis informanation is used to obtain an predefined exosystem that can characterize the dynamics of the mutual inductance fluctuation. A state feedback controller combined with a state observer is designed to achieve a constant output voltage of the DWC system by compensating for such disturbance. The simulation results verify the effectiveness and superiority of the proposed control strategy as compared with the traditional control methods.

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A Time-delay Approach for Extremum Seeking of Nonlinear Static Maps:

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IFAC-PapersOnLine 2023年第1期56卷 258-263页

作者： Yang Zhu Emilia Fridman Institute of Cyber-Systems and Control State Key Laboratory of Industrial Control Technology Zhejiang University China School of Electrical Engineering Tel-Aviv University Israel

This paper develops a constructive time-delay approach to averaging for gradient-based extremum seeking (ES) control of nonlinear static maps of non-quadratic form. Under the assumption that some prior knowledge of the nonlinear map with its derivatives is available, for the first time, we derive a quantitative analysis for ES close-loop systems with upper bounds on the tuning parameter that preserves the exponential stability and on the convergence error of extremum seeking. By transforming the ES system into a time-delay neutral type system with distributed delays, the developed method gives an accurate perturbed system of ES without employing any approximate calculation, and suggests a direct Lyapunov-Krasovskii approach in the form of linear matrix inequalities (LMIs), for the transformed time-delay plant to derive efficient stability conditions.

关键词： Extremum Seeking Time-delay Nonlinear Static Non-quadratic

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Principal Trotter observation error with truncated commutators

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Physical Review A 2024年第6期110卷 062614-062614页

作者： Langyu Li Institute of Cyber-system and Control College of Control Science and Engineering Zhejiang University Hangzhou Zhejiang 310027 China and State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou Zhejiang 310027 China

Hamiltonian simulation is one of the most promising applications of quantum computers, and the product formula is one of the most important methods for this purpose. Previous related work has mainly focused on the worst-case, average-case, or actual-case scenarios. In this work we consider the simulation error under a fixed observable. Specifically, errors that commute with this observable become less significant. To illustrate this point, we define the observation error as the expectation under the observable and provide a commutativity-based upper bound using the Baker-Campbell-Hausdorff formula. Furthermore, we observe that the evolution order plays a critical role in determining the observation error. By leveraging a simulated annealing algorithm, we design an evolution order optimization algorithm, to identify an order that enhances commutativity with the observable, where the observation error indicated by this upper bound can be significantly compressed. In the experiment with the Heisenberg model, the observation bound compresses the Trotter number by nearly half compared to recent commutator bounds. The experiment on the hydrogen molecule Hamiltonian demonstrates that optimizing the order can lead to nearly half the reduction in the Trotter number.

关键词： Hamiltonians

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Accelerated Distributed Aggregative Optimization

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IEEE Transactions on Automatic control 2025年

作者： Liu, Jiaxu Chen, Song Cai, Shengze Xu, Chao Chu, Jian Zhejiang University School of Mathematical Sciences Zhejiang Hangzhou310027 China Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang Hangzhou310027 China

This paper delves into the investigation of a distributed aggregative optimization problem within a network. In this scenario, each agent possesses its own local cost function, which relies not only on the local state variable but also on an aggregated function of state variables from all agents. To expedite the optimization process, we amalgamate the heavy ball and Nesterov's accelerated method with distributed aggregative gradient tracking, resulting in the proposal of two innovative algorithms, aimed at resolving the distributed aggregative optimization problem. Our analysis demonstrates that the proposed algorithms can converge to an optimal solution at a global linear convergence rate when the objective function is strongly convex with the Lipschitz-continuous gradient, and when the parameters (e.g., step size and momentum coefficients) are chosen within specific ranges. Additionally, we present several numerical experiments to verify the effectiveness, robustness and superiority of our proposed algorithms. © 2025 IEEE.

关键词： Optimization algorithms

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Learning adaptive manipulation of objects with revolute joint: A case study on varied cabinet doors opening

arXiv

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arXiv 2023年

作者： Yu, Hongxiang Guo, Dashun Zhou, Zhongxiang Wang, Yue Xiong, Rong The State Key Laboratory of Industrial Control and Technology The Institute of Cyber-Systems and Control Zhejiang University Hangzhou310058 China

This paper introduces a learning-based framework for robot adaptive manipulating the object with a revolute joint in unstructured environments. We concentrate our discussion on various cabinet door opening tasks. To improve the performance of Deep Reinforcement Learning in this scene, we analytically provide an efficient sampling manner utilizing the constraints of the objects. To open various kinds of doors, we add encoded environment parameters that define the various environments to the input of out policy. To transfer the policy into the real world, we train an adaptation module in simulation and fine-tune the adaptation module to cut down the impact of the policy-unaware environment parameters. We design a series of experiments to validate the efficacy of our framework. Additionally, we testify to the model's performance in the real world compared to the traditional door opening method. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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