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Mechanical Design and data-Enabled Predictive control of a Planar Soft Robot

ieee ROBOTICS AND AUTOMATION LETTERS 2024年第9期9卷 7923-7930页

作者： Wang, Huanqing Zhang, Kaixiang Lee, Kyungjoon Mei, Yu Zhu, Keyi Srivastava, Vaibhav Sheng, Jun Li, Zhaojian Michigan State Univ Dept Mech Engn E Lansing MI 48824 USA Univ Calif Riverside Dept Mech Engn Riverside CA 92521 USA Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA

Soft robots offer a unique combination of flexibility, adaptability, and safety, making them well-suited for a diverse range of applications. However, the inherent complexity of soft robots poses great challenges in their modeling and control. In this letter, we present the mechanical design and data-driven control of a pneumatic-driven soft planar robot. Specifically, we employ a data-enabled predictive control (DeePC) strategy that directly utilizes system input/output data to achieve safe and optimal control, eliminating the need for tedious system identification or modeling. In addition, a dimension reduction technique is introduced into the DeePC framework, resulting in significantly enhanced computational efficiency with minimal to no degradation in control performance. Comparative experiments are conducted to validate the efficacy of DeePC in the control of the fabricated soft robot.

关键词： Soft robotics Robots Fabrics Computational modeling Aerospace electronics Pneumatic systems Predictive control Modeling control and learning for soft robots soft sensors and actuators data-driven control predictive control

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DDPG-based path planning approach for autonomous driving 12

DDPG-based path planning approach for autonomous driving

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Li, Yimin Chen, Yanfang Li, Tianru Lao, Jingtao Li, Xuefang Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen 518000 Peoples R China

ISBN: (纸本)9798350321050

The present work develops a DDPG-based path planning algorithm that integrates the artificial potential field method into reinforcement learning to learn and generate an obstacle-free path quickly and autonomously. The vehicle kinematic model is adopted to describe the motion of autonomous vehicles, and the potential field function of obstacles, road boundaries as well as reference waypoints are considered to construct rewards of reinforcement learning, which enables the vehicle to realize the tradeoff between avoiding obstacles, preventing driving off the road and following the reference route. In contrast to the existent path planning algorithms, the proposed approach is able to learn autonomously in different driving environments, which is more suitable to autonomous vehicles. Moreover, simulations are provided to further demonstrate the effectiveness and adaptability of the proposed algorithm.

关键词： reinforcement learning artificial intelligence autonomous driving DDPG path planning

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Prediction of uterine fibroid outcomes based on DenseNet and multiple MRI sequences 12

Prediction of uterine fibroid outcomes based on DenseNet and...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Chen, Yuan Huang, Deqing Qin, Na Yin, Zijie You, Yiting Southwest Jiaotong Univ Inst Syst Sci & Technol Chengdu 610031 Peoples R China

ISBN: (纸本)9798350321050

High-intensity focused ultrasound (HIFU) is a surgical method commonly used to treat uterine fibroids, but not all patients are suitable for this treatment. Therefore, effective postoperative outcome prediction can improve surgical success rate. In this study, a data-driven prediction model for HIFU treatment outcome was developed based on patients' MRI sequences and DenseNet network. We used N4 bias correction, SimpleITK intensity normalization, image registration and other preprocessing methods to improve image quality. We also introduced data augmentation and transfer learning methods to solve the problem of small dataset and integrated multimodal information through feature fusion method. In conclusion, the fusion model's Accuracy was 0.769, AUC value was 0.806, which was better than the prediction accuracy of single sequence, and the experimental results showed that the model had good predictive performance.

关键词： Uterine Fibroid Transfer learning DenseNet data Fusion Efficacy Prediction

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Adaptive Iterative learning Optimal control for Linear Multi-Agent systems 13

Adaptive Iterative Learning Optimal Control for Linear Multi...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Chang, Duhui Geng, Yan School of Science Xi'an Polytechnic University Xi'an710048 China

ISBN: (纸本)9798350361674

In order to improve tracking performance of a class of linear discrete time-invariant multi-agent systems, an adaptive optimal iterative learning control strategy is designed. To design the control protocol, a parameter adaptive algorithm is constructed to estimate the Markov parameters of the system. Then, virtual leader is used instead of expected trajectory. By optimizing the index function and incorporating estimated parameters into the learning process. An adaptive iterative learning optimal control strategy is constructed. Through rigorous analysis, the parameter estimation error is bounded, the tracking error is convergent, and the proof process is independent of system parameters. Finally, numerical simulations validate the effectiveness of this designed control strategy. © 2024 ieee.

关键词： Contrastive learning

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learning Stable Models for Prediction and control

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ieee TRANSACTIONS ON ROBOTICS 2023年第3期39卷 2255-2275页

作者： Mamakoukas, Giorgos Abraham, Ian Murphey, Todd D. Northwestern Univ Dept Mech Engn Evanston IL 60208 USA Machine Learning & Controls Mot Boston MA USA Yale Univ Mech Engn New Haven CT USA

In this article, we demonstrate the benefits of imposing stability on data-driven Koopman operators. The data-driven identification of stable Koopman operators (DISKO) is implemented using an algorithm [1] that computes the nearest stable matrix solution to a least-squares reconstruction error. As a first result, we derive a formula that describes the prediction error of Koopman representations for an arbitrary number of time steps, and which shows that stability constraints can improve the predictive accuracy over long horizons. As a second result, we determine formal conditions on basis functions of Koopman operators needed to satisfy the stability properties of an underlying nonlinear system. As a third result, we derive formal conditions for constructing Lyapunov functions for nonlinear systems out of stable data-driven Koopman operators, which we use to verify stabilizing control from data. Finally, we demonstrate the benefits of DISKO in prediction and control with simulations using a pendulum and a quadrotor and experiments with a pusher-slider system. The paper is complemented with a video: https://***/view/learning-stable-koopman.

关键词： Stability analysis Nonlinear dynamical systems Numerical stability Mathematical models Analytical models Robots Predictive models control Lyapunov functions data-driven control Koopman operator stability

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Convergence Analysis of Dual Quantized Iterative learning control with Channel Fading 12

Convergence Analysis of Dual Quantized Iterative Learning Co...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Liu, Taojun Shen, Dong Renmin Univ China Sch Math Beijing 100872 Peoples R China

ISBN: (纸本)9798350321050

In the field of stochastic Iteration learning control (ILC) people tend to treat channel fading and data quantization as two separate random disturbances. In this paper, we give an analysis of the convergence of quantized ILC in the presence of channel fading. In addition, we design a dual quantized ILC, which can further reduce the deviation before and after channel fading while ensuring convergence. In the dual quantized ILC, two separate quantizers are set before and after the channel fading. The first quantizer is designed to relieve the transmission pressure;the second quantizer is designed to reduce the error caused by the channel fading. Here we use the multiplicative fading factor to describe the channel fading. We adjust the quantization interval of the quantizer to ensure that the dual quantized ILC has a greater probability of smaller errors than the quantized ILC. Illustrative simulations are provided to verify the theoretical results.

关键词： Iterative learning control Quantized Channel Fading

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Fixed-time Nonsingular Terminal Sliding Mode control for Vehicle Platoon with Disturbance Observer 12

Fixed-time Nonsingular Terminal Sliding Mode Control for Veh...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Wang, Yan-Bo Li, Yu-Ling Liu, Cheng-Lin Jiangnan Univ Inst Automat Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Jiangsu Peoples R China

ISBN: (纸本)9798350321050

This article investigates a fixed-time nonsingular terminal sliding mode control (FNTSMC) method with disturbance observer (DO). Using the vehicle's acceleration information, a fixed-time DO is designed to estimate the external uncertain disturbance of the system. Considering the nonzero initial error, a modified constant time headway strategy is proposed to overcome the influence of the nonzero initial error on the vehicle controller. In addition, a new terminal sliding mode control method is introduced to avoid the nonsingular problem and ensure that the vehicle spacing error converges in a fixed time. Finally, a numerical simulation is presented to show the proposed method's effectiveness.

关键词： Vehicle Platoon Nonsingular Terminal Sliding Mode String Stability Fixed-time Disturbance Observer

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Differential Dynamic Programming for Finite-Horizon Multi-Player Non-Zero-Sum Differential Games of Nonlinear systems 12

Differential Dynamic Programming for Finite-Horizon Multi-Pl...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Zhang, Yuqi Zhang, Bin Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing 100876 Peoples R China

ISBN: (纸本)9798350321050

In this paper, an iterative algorithm based on differential dynamic programming (DDP) is developed to solve the finite-horizon multi-player non-zero-sum (NZS) games. By using the DDP, the coupled Hamilton-Jacobi (HJ) equations are expanded from partial differential forms to higher-order differential forms. By approximating the value functions and optimal control policies through several finite sets of basis functions, the DDP expansions are transformed into algebraic matrix equations in integral forms. Then a policy iteration (PI) algorithm is provided to solve the feedback Nash equilibrium of above NZS games. Finally, two simulation examples are given to demonstrate the feasibility of the developed algorithm.

关键词： Multi-player NZS games Feedback Nash equilibrium Coupled Hamilton-Jacobi equations Differential dynamic programming Finite-horizon optimal control Policy iteration

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Composite learning Fixed-Time control for Nonlinear Servo systems With State Constraints and Unknown Dynamics

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ieee TRANSACTIONS ON systems MAN CYBERNETICS-systems 2025年第3期55卷 2332-2342页

作者： Wang, Shubo Sun, Chuanbin Chen, Qiang He, Haoran Kunming Univ Sci & Technol Fac Mech & Elect Engn Kunming 650550 Peoples R China Kunming Univ Sci & Technol Yunnan Key Lab Intelligent Control & Applicat Kunming 650550 Peoples R China Qingdao Univ Sch Automat Qingdao 266071 Peoples R China Zhejiang Univ Technol Coll Informat Engn Data Driven Intelligent Syst Lab Hangzhou 310023 Peoples R China

Robot systems, due to their unique flexibility and economy, are widely used in modern industry and intelligent manufacturing. The parameters of the system are unknown, and traditional parameter estimation methods are difficult to achieve fixed time convergence, which leads to extremely position tracking control problem. In addition, the transient and steady-state performance of the robot system is difficult to specify in advance. In this article, a novel composite learning fixed-time (FxT) control strategy is proposed for the robotic systems to deal with these issues. The funnel control (FC) is utilized to transform the original error system into a new error dynamics with transient performance constraints. The two-phase nonsingular FxT sliding mode surface is constructed to avoid the singularity problem. Then, the filter operation is introduced to obtain the expression of parameter estimation error and is used to design the composite learning law. To achieve parameter estimation, a FxT composite learning law based on online historical data and regression extension is proposed, where the interval excitation (IE) is considered in the adaptive law. Finally, the designed adaption is incorporated into the nonsingular FxT sliding mode control to achieve tracking control. Moreover, the comparison of three different controllers is made to demonstrate the benefits of the developed control strategy.

关键词： Robots Convergence Parameter estimation Transient analysis Symmetric matrices Servomotors Vectors Electronic mail Adaptive control Trajectory tracking fixed-time (FxT) convergence funnel control (FC) parameter estimation sliding mode control (SMC)

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Gaussian Reinforcement learning: Optimal Tracking control for Uncertain Linear systems 13

Gaussian Reinforcement Learning: Optimal Tracking Control fo...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Gang, Yiqing Li, Jinna School of Information and Control Engineering Liaoning Petrochemical University Fushun113001 China

ISBN: (纸本)9798350361674

In this paper, a design method for PID control based on Gaussian reinforcement learning is proposed to find the optimal policy for linear quadratic tracking of an unknown system. Firstly, the mathematical model of the linear quadratic tracking problem with random disturbance is presented, and an analysis of the PID control structure is carried out. Secondly, a Gaussian model predictive control for the unknown model with random disturbances is provided. Finally, a Gaussian Q-learning method is given for the system based on this predictive model. The effectiveness of the proposed method is demonstrated through a numerical example. © 2024 ieee.

关键词： Optimal detection

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