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International Conference on Applied Mechanics and Mechanical Engineering

作者： Zheng Iijun Cheng Xinmei Chen, Shanshan Zhejiang Univ Technol Zhijiang Coll Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9780878492459

In order to solve the electro-hydraulic system position tracking control problem, which caused by the nonlinear system friction torque disturbance, a model-free algorithm for the friction torque adaptive identification and compensation was put forward. The algorithm is based on the application mathematics knowledge and matching & following principle. It can accommodate to all situations with the friction torque (force) variety. The simulation result indicates that the algorithm can restrains the interference of the friction torque (force) effectively, and the system's low speed character and tracking performance were been improved.

关键词： Friction torque Adaptive Identification Compensation model-free algorithm

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Provably Sample-Efficient model-free algorithm for MDPs with Peak Constraints

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JOURNAL OF MACHINE LEARNING RESEARCH 2023年第1期24卷 1-25页

作者： Bai, Qinbo Aggarwal, Vaneet Gattami, Ather Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA Purdue Univ Sch IE & ECE W Lafayette IN 47907 USA AI Sweden Stockholm Sweden

In the optimization of dynamic systems, the variables typically have constraints. Such problems can be modeled as a Constrained Markov Decision Process (CMDP). This pa-per considers the peak Constrained Markov Decision Process (PCMDP), where the agent chooses the policy to maximize total reward in the finite horizon as well as satisfy con-straints at each epoch with probability 1. We propose a model-free algorithm that converts PCMDP problem to an unconstrained problem and a Q-learning based approach is ap-plied. We define the concept of probably approximately correct (PAC) to the proposed PCMDP problem. The proposed algorithm is proved to achieve an (epsilon, p)-PAC policy when the episode K >= ohm(I2H6SAl epsilon 2 ), where S and A are the number of states and actions, respec-tively. H is the number of epochs per episode. I is the number of constraint functions, and l = log(SAT p ). We note that this is the first result on PAC kind of analysis for PCMDP with peak constraints, where the transition dynamics are not known apriori. We demonstrate the proposed algorithm on an energy harvesting problem and a single machine scheduling problem, where it performs close to the theoretical upper bound of the studied optimization problem.

关键词： Markov Decision Process model-free algorithm Peak Constraints Rein-forcement Learning

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Provably sample-efficient model-free algorithm for MDPs with peak constraints

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 2579-2603页

作者： Qinbo Bai Vaneet Aggarwal Ather Gattami School of Electrical and Computer Engineering Purdue University West Lafayette IN School of IE and ECE Purdue University West Lafayette IN AI Sweden Stockholm Sweden

In the optimization of dynamic systems, the variables typically have constraints. Such problems can be modeled as a Constrained Markov Decision Process (CMDP). This paper considers the peak Constrained Markov Decision Process (PCMDP), where the agent chooses the policy to maximize total reward in the finite horizon as well as satisfy constraints at each epoch with probability 1. We propose a model-free algorithm that converts PCMDP problem to an unconstrained problem and a Q-learning based approach is applied. We define the concept of probably approximately correct (PAC) to the proposed PCMDP problem. The proposed algorithm is proved to achieve an (ε, p)-PAC policy when the episode $K\geq\Omega(\frac{I^2H^6SA\ell}{\epsilon^2})$, where S and A are the number of states and actions, respectively. H is the number of epochs per episode. I is the number of constraint functions, and $\ell=\log(\frac{SAT}{p})$. We note that this is the first result on PAC kind of analysis for PCMDP with peak constraints, where the transition dynamics are not known apriori. We demonstrate the proposed algorithm on an energy harvesting problem and a single machine scheduling problem, where it performs close to the theoretical upper bound of the studied optimization problem.

关键词： Markov decision process model-free algorithm peak constraints reinforcement learning

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Grasping Deformable Objects in Industry Application: A Comprehensive Review of Robotic Manipulation

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IEEE ACCESS 2025年 13卷 33403-33423页

作者： Wang, Yuanyang Mahyuddin, Muhammad Nasiruddin Univ Sains Malaysia Sch Elect & Elect Engn Nibong Tebal 14300 Pulau Pinang Malaysia

Grasping deformable objects remains a challenging operational task for robots in diverse industrial applications. Different characteristics of deformable objects to be gripped need to be considered in the mechanical design of the gripper. Mechanical grippers often rely on sensors and appropriate control strategies to grasp deformable objects. This study classifies deformable objects, grippers and gripper manufacturers, and their corresponding gripping strategies. In the study of control strategies, model-based algorithm control strategies are often ineffective as often the objects to be gripped are unknown in terms of its rigidity and other morphological characteristics. In contrast, model-free algorithms do not need parametric information of the objects as only input-output signal is required. This allows the model-free controlled grippers adapt to diverse and unstructured environments. Finally, the advantages and disadvantages of current deformable object-grasping techniques are discussed and summarized. The challenges and future directions of robots grasping deformable objects are pointed out.

关键词： Grippers Robots Grasping Adaptation models Manipulators Force Robot sensing systems Deformation Sensors Accuracy Deformable object object gripping gripper model-based algorithm model-free algorithm

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model-free Event-Triggered Consensus algorithm for Multiagent Systems Using Reinforcement Learning Method

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第8期52卷 5212-5221页

作者： Long, Mingkang Su, Housheng Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

In this article, we study the consensus issues of multiagent systems (MASs) without any information of the system model by using the reinforcement learning (RL) method and event-based control strategy. First, we design an adaptive event-based consensus control protocol using the local sampled state information so that the consensus errors of all agents are uniformly ultimately bounded. The validity of the above event-triggered adaptive control protocol is confirmed by excluding the Zeno behavior within finite time. Then, based on the RL approach, we present a model-free algorithm to get the feedback gain matrix, and accomplish constructing the adaptive event-triggered control strategy without the knowledge of model information. Distinct with the existing related works, this RL-based event-triggered adaptive control algorithm only relies on the local sampled state information, irrelevant to any model information or global network information. Finally, we provide some examples to demonstrate the validity of the above adaptive event-based consensus algorithm.

关键词： Adaptation models Consensus algorithm Heuristic algorithms Protocols Computational modeling Multi-agent systems Adaptive control Consensus event triggered model-free algorithm multiagent systems (MASs) reinforcement learning (RL)

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Development of a model-free Hamiltonian Tracking Optimal Control algorithm

Development of a Model-free Hamiltonian Tracking Optimal Con...

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作者： Lee, Jinkun PennState University Libraries

学位级别：Doctor of Philosophy

In this study, a novel algorithm has been developed to solve a trajectory optimization problem of a model-free black box dynamical system. The proposed algorithm does not need an explicit dynamic model of the system but computes partial derivatives of the dynamic function numerically from the time series data of observation to estimate the adjoint variable and the Hamiltonian. The additional necessary conditions for optimality, constant Hamiltonian over time span, are used as the tracking condition to find an optimal trajectory. A candidate optimal trajectory is searched by the Legendre transformation which interprets the geometric information of the current control trajectory on the Lagrangian surface. The implication of this approach is the elimination of the need for the dynamic model or the system identification process as we only derive necessary partial derivatives out of current observations. This enables us to find a near optimal trajectory quickly without the explicit dynamic model or the full system identification process. The estimated Hamiltonian approach is verified first with several problems whose dynamic models are known. After then, the model-free algorithm is applied for several problems where the dynamics are still unclear. First case is real world applications where the observation data is obtained by experiments or from historical record. These applications include a recent hot manufacturing process called Field Assisted Sintering Technology (FAST) and a socio-economic policy problem of water usage management by price controls. In this case, approximated dynamic models based on collected empirical data are used for the simulated iterations to validate the effectiveness of the proposed algorithm. The proposed algorithm only use the observation output and shows iterative candidate searching history which converges toward an exact solution or a certain trajectory with decreasing total cost. Second case is a simulated feedback control algorithm call

关键词： Optimal control Hamiltonian Tracking model-free algorithm

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Cooperative Adaptive model-free Control With model-free Estimation and Online Gain Tuning

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第9期52卷 8642-8654页

作者： Safaei, Ali McGill Univ Dept Mech Engn Montreal PQ H3A 0G4 Canada

In this article, a distributed adaptive model-free control algorithm is proposed for consensus and formation-tracking problems in a network of agents with completely unknown nonlinear dynamic systems. The specification of the communication graph in the network is incorporated in the adaptive laws for estimation of the unknown linear and nonlinear terms, and in the online updating of the elements in the main controller gain matrix. The decentralized control signal at each agent in the network requires information about the states of the leader agent, as well as the desired formation variables of the agents in a local coordinate frame. These two sets of variables are provided at each agent by utilizing two recently proposed distributed observers. It is shown that only a spanning-tree rooted at the leader agent is enough for the convergence and stability of the proposed cooperative control and observer algorithms. Two simulation studies are provided to evaluate the performance of the proposed algorithm in comparison with two state-of-the-art distributed model-free control algorithms. With lower control effort as well as fewer offline gain tuning, the same level of consensus errors is achieved. Finally, the application of the proposed solution is studied in the formation-tracking control of a team of autonomous aerial mobile robots via simulation results.

关键词： Heuristic algorithms Nonlinear dynamical systems Adaptation models Estimation Adaptive systems Tuning Protocols Adaptive control autonomous mobile robots cooperative control model-free algorithm online gain tuning

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An optimal control algorithm toward unknown constrained nonlinear systems based on the sequential sampling and updating of surrogate model

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ISA TRANSACTIONS 2024年 153卷 117-132页

作者： Qiao, Ping Liu, Xin Zhang, Qi Xu, Bing Suzhou Univ Sci & Technol Sch Mech Engn Suzhou 215101 Peoples R China Guizhou Xiaozhi Tongxie Technol Co Ltd Guiyang 550081 Peoples R China Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Wuhan 430074 Peoples R China

The application of optimal control theory in practical engineering is often limited by the modeling cost and complexity of the mathematical model of the controlled plant, and various constraints. To bridge the gap between the theory and practice, this paper proposes a model-free direct method based on the sequential sampling and updating of surrogate model, and extends the ability of direct method to solve model-free optimal control problems with general constraints. The algorithm selects sample points from the current actual trajectory data to update the surrogate model of controlled plant, and solve the optimal control problem of the constantly refined surrogate model until the result converges. The presented initial and subsequent sampling strategies eliminate the dependence on the model. Furthermore, the new stopping criteria ensure the overlap of final actual and planned trajectories. The several examples illustrate that the presented algorithm can obtain constrained solutions with greater accuracy and require fewer sample data.

关键词： Optimal control model-free algorithm Direct method Surrogate model Sequential sampling

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Data-Based H∞ Control for the Constrained-Input Nonlinear Systems and its Applications in Chaotic Circuit Systems

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2020年第8期67卷 2791-2802页

作者： Ren, Ling Zhang, Guoshan Mu, Chaoxu Tianjin Univ Sch Elect & Informat Engn Tianjin 370072 Peoples R China

In this paper, H-infinity control problem is investigated by off-policy integral reinforcement learning (IRL) method for the nonlinear systems with completely unknown dynamics, disturbances, and constrained-input. Firstly, according to a model-based policy iteration (PI) algorithm, a model-free algorithm is proposed based on the derived iterative equation, and the equivalence of model-based PI algorithm and model-free algorithm is proven. Then, the model-free algorithm is implemented by off-policy IRL technology to solve the Hamilton-Jacobi-Isaacs (HJI) equation with the collected system data by the least-square approach, where three neural networks (NNs) are constructed to approximate the value function, control and the disturbance. Finally, our proposed methods are applied to stabilize an autonomous third-order Chua's chaotic circuit system and a non-autonomous second-order memristive chaotic circuit system to illustrate the efficiency of the proposed method.

关键词： H-infinity control constrained-input off-policy integral reinforcement learning model-free algorithm neural networks chaotic circuit systems

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Wireless monitoring algorithm for wind turbine blades using Piezo-electric energy harvesters

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WIND ENERGY 2017年第3期20卷 551-565页

作者： Lim, Dong-Won Mantell, Susan C. Seiler, Peter J. Univ Minnesota Twin Cities Dept Mech Engn 111 Church St SE Minneapolis MN 55455 USA Univ Minnesota Twin Cities Dept Aerosp Engn & Mech Minneapolis MN 55455 USA Korea Atom Energy Res Inst Daejeon South Korea

Wind turbine blade failure can be catastrophic and lead to unexpected power interruptions. In this paper, a Structural Health Monitoring (SHM) algorithm is presented for wireless monitoring of wind turbine blades. The SHM algorithm utilizes accumulated strain energy data, such as would be acquired by piezoelectric materials. The SHM algorithm compares the accumulated strain energy at the same position on the three blades. This exploits the inherent triple redundancy of the blades and avoids the need for a structural model of the blade. The performance of the algorithm is evaluated using probabilistic metrics such as detection probability (True Positive) and false alarm rate (False Positive). The decision time is chosen to be sufficiently long that a particular damage level can be detected even in the presence of system sensor noise and wind variations. Finally, the proposed algorithm is evaluated with a case study of a utility-scale turbine. The noise level is based on measurements acquired from strain sensors mounted on the blades of a Clipper Liberty C96 turbine. Strain energy changes associated with damage from matrix cracking and delamination are simulated with a finite element model. The case study demonstrates that the proposed algorithm can detect damage with a high probability based on a decision time period of approximately 50-200days. Copyright (c) 2016 John Wiley & Sons, Ltd.

关键词： structural health monitoring wind turbine blade piezoelectric wireless sensor model-free algorithm probabilistic analysis thresholding composite material failure

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