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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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Distributed Optimization in Sensor Network for Scalable Multi-Robot Relative state Estimation

arXiv

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arXiv 2023年

作者： Wu, Tianyue Gao, Fei State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China

Distance measurements demonstrate distinctive scalability when used for relative state estimation in large-scale multi-robot systems. Despite the attractiveness of distance measurements, multi-robot relative state estimation based on distance measurements raises a tricky optimization problem, especially in the context of large-scale systems. Motivated by this, we aim to develop specialized computational techniques that enable robust and efficient estimation when deploying distance measurements at scale. We first reveal the commonality between the estimation problem and the one that finds realization of a sensor network, from which we draw crucial lesson to inspire the proposed methods. However, solving the latter problem in large-scale (still) requires distributed optimization schemes with scalability natures, efficient computational procedures, and fast convergence rates. Towards this goal, we propose a complementary pair of distributed computational techniques with the classical block coordinate descent (BCD) algorithm as a unified backbone. In the first method, we treat Burer-Monteiro factorization as a rank-restricted heuristic for rank-constrained semidefinite programming (SDP), where a specialized BCD-type algorithm that analytically solve each block update subproblem is employed. Although this method enables robust and (extremely) fast recovery of estimates from initial guesses, it inevitably fails as the initialization becomes disorganized. We therefore propose the second method, derived from a convex formulation named anchored edge-based semidefinite programming (ESDP), to complement it, at the expense of a certain loss of efficiency. This formulation is structurally decomposable so that BCD can be naturally employed, where each subproblem is convex and (again) solved exactly. Since in both methods BCD seeks to solve the subproblem exactly, fewer iterations, as well as the number of communication rounds, are expected. Extensive evaluation on 2D and 3D probl

关键词： Scalability

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Adaptive Model Prediction control-Based Multi-Terrain Trajectory Tracking Framework for Mobile Spherical Robots

arXiv

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arXiv 2023年

作者： Liu, Yifan Hu, Tao Guan, Xiaoqing Wang, Yixu Zhang, Bixuan Wang, You Li, Guang State Key Laboratory of Industrial Control Technology Institute of Cyber Systems and Control Zhejiang University Hangzhou310027 China

Owing to uncertainties in both kinematics and dynamics, the current trajectory tracking framework for mobile robots like spherical robots cannot function effectively on multiple terrains, especially uneven and unknown ones. Since this is a prerequisite for robots to execute tasks in the wild, we enhance our previous hierarchical trajectory tracking framework to handle this issue. First, a modified adaptive RBF neural network (RBFNN) is proposed to represent all uncertainties in kinodynamics. Then the Lyapunov function is utilized to design its adaptive law, and a variable step-size algorithm is employed in the weights update procedure to accelerate convergence and improve stability. Hence, a new adaptive model prediction control-based instruction planner (VAN-MPC) is proposed. Without modifying the bottom controllers, we finally develop the multi-terrain trajectory tracking framework by employing the new instruction planner VAN-MPC. The practical experiments demonstrate its effectiveness and robustness. Copyright © 2023, The Authors. All rights reserved.

关键词： Mobile robots

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A Conflict-Reducing Path Planning Algorithm in Automated Warehouses 18

A Conflict-Reducing Path Planning Algorithm in Automated War...

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18th IEEE International Conference on Networking, Sensing and control, ICNSC 2021

作者： Feng, Ligen Chen, Xinyu Zhang, Tingqi Wu, Weimin Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Hangzhou China

ISBN: (纸本)9781665440486

With the development of modern logistics industry, automated warehouses are becoming more and more widely used. An automated guided vehicle (AGV) is often used to carry goods in warehouse. Under specific limitations of the road layout, AGVs are prone to conflict when moving. According to the layout characteristics of automated warehouses, this paper proposes a conflict-reducing path planning algorithm, which is an improvement of A-star algorithm. The conflict cost and turn cost are introduced to the algorithm to reduce the conflict probability and the number of turns of paths. Through simulation comparison, it is verified that the algorithm can reduce the probability of conflicts among AGVs and improve the overall efficiency of multi-AGV systems. © 2021 IEEE.

关键词： Motion planning

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