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检索条件"任意字段=13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024"

共 716 条记录，以下是341-350 订阅

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Model-Free Adaptive control Design for Unknown Nonlinear systems with Input Constraints

Model-Free Adaptive Control Design for Unknown Nonlinear Sys...

引用

data driven control and learning systems (ddcls)

作者： Diandian Wang Shuangshuang Xiong Shuang Cao School of Automation Beijing Information Science and Technology University Beijing P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

A control scheme based on model-free adaptive control (MFAC) is presented for the input-constrained problem in this paper. Firstly, an equivalent linear system is established using the dynamic linearisation method. A novel dynamic compensator is applied in the controller design to overcome the input-constrained saturation problem. then on this basis, the relationship between the input constrainted before and after is also introduced to simplify the operation. Furthermore, the tracking error signal is shown to converge asymptotically in the stability analysis section. At last, the system is simulated to verify the effectiveness of the control algorithm.

关键词： Linear systems learning systems Adaptation models Heuristic algorithms control systems Stability analysis data models

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An Iterative learning control Method for a Class of Continuous-Time systems with Randomly Varying Trial Lengths

An Iterative Learning Control Method for a Class of Continuo...

引用

data driven control and learning systems (ddcls)

作者： Chaochen Yin Jian Liu School of Mechanical and Electrical Engineering Xidian University China Center for Complex Systems School of Mechano-electronic Engineering Xidian University China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper considers the iterative learning control (ILC) for a class of continuous-time systems with direct-though term, whose trial lengths are iteration-varying from trial to trial. A novel randomly varying trial model is proposed, in which we use a group of staircase functions to model the varying trial length. then, an intermittent-type randomly varying trial ILC update law is developed. By the lambda-norm technique, we analyze the convergence of the iterative learning process and give the convergence condition of the ILC process. Finally, a numerical example is given to demonstrate the validity of the main result.

关键词： learning systems control systems Numerical models Iterative learning control Convergence

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Adaptive Iterative learning control for High-Order Fully Actuated Nonlinear systems with Parametric Uncertainties

Adaptive Iterative Learning Control for High-Order Fully Act...

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data driven control and learning systems (ddcls)

作者： Junkai Wang Xiao-Dong Li Xuefang Li School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-Sen University Shenzhen P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In this paper, a novel adaptive iterative learning control (AILC) scheme is proposed for high-order fully actuated systems (HOFASs) with parametric uncertainties, where the convergence of the tracking error is analyzed under the framework of the composite energy function method. Moreover, by applying the backstepping, the proposed AILC method is extended to strict-feedback HOFASs (SFHOFASs), which widens the application scope of AILC significantly. Finally, two illustrative examples are presented to verify the effectiveness of the proposed AILC scheme.

关键词： learning systems Uncertainty Adaptive systems Backstepping Mean square error methods control systems Trajectory

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Distributed control of Multiple Quadruped Robots Based on Leader-Following Consensus Strategy

Distributed Control of Multiple Quadruped Robots Based on Le...

引用

data driven control and learning systems (ddcls)

作者： Ye Ren Haoyang Sun Li Wang Shida Liu School of Electronics & Control Engineering North China University of Technology Beijing

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper discusses the leader-follower consistency problem in multi-quadruped robot systems. Initially, the dynamics of quadruped robots are modeled as a first-order integral model. then, a PD-type distributed protocol is proposed as the controller for the following quadruped robots. Finally, the feasibility of this protocol is verified through a joint MATLAB and CoppeliaSim simulation. the simulation uses a multi-quadruped robot system consisting of one leading quadruped robot and three following quadruped robots. the simulation outcomes validate the efficacy of the proposed control protocol.

关键词： learning systems Adaptation models Protocols Decentralized control Distributed databases Mathematical models Formation control

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Markov Parameters Estimation and data-driven Iterative learning control for LDTV systems

Markov Parameters Estimation and Data-Driven Iterative Learn...

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data driven control and learning systems (ddcls)

作者： Chuyang Liu Xiaoe Ruan School of Science Chang'an University Xi'an China School of Mathematics and Statistics Xi'an Jiaotong University Xi'an China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In this paper, a kind of Markov parameters estimation and data-driven iterative learning control scheme is constructed for a class of linear discrete-time-varying systems. Firstly, a linear combination of the predicted system output error energy and estimation compensation energy is designed as the performance index function. through algebraic operations, the estimation formula for the Markov matrix that minimizes the performance index function is explicitly solved. Simultaneously, within the framework of norm-optimal iterative learning control where the controller is regarded as an optimization variable, the updating rule for control input is formulated. Substituting the Markov matrix with its estimation in the control input updating rule, a data-driven iterative learning control scheme is established. Numerical experiments demonstrate that both the estimation error of the system Markov parameters and the tracking error of the system output monotonically decrease along the iteration axis. Moreover, a smaller estimation ratio leads to a faster decrease in the estimation error, and a smaller learning ratio results in a faster decrease in the tracking error.

关键词： learning systems Estimation error Parameter estimation Optimization methods control systems Matrices Performance analysis

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Event-Triggered Iterative learning control for First-Order MIMO Discrete Nonlinear Hyperbolic Distributed Parameter systems

Event-Triggered Iterative Learning Control for First-Order M...

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data driven control and learning systems (ddcls)

作者： Xuhui Wang Xisheng Dai School of Automation Guangxi University of Science and Technology Liuzhou China Institute of Intelligent Systems and Control Guangxi University of Science and Technology Liuzhou China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper studies the iterative learning control issue for a class of MIMO discrete nonlinear hyperbolic distributed parameter system with event-triggered strategy. the purpose of the event-triggered mechanism is to reduce the number of input updates in the iterative learning process. the event-triggered condition is about the error information of two previous adjacent iterative batches. then an event-triggered iterative learning control algorithm is proposed. Rigorous mathematical analysis is shown, tracking error can converge to zero along the direction of iterative axis. Simulation results illustrates the effectiveness of the proposed event-triggered iterative learning control algorithm.

关键词： learning systems Delay effects Simulation Mathematical analysis Distributed databases control systems Distributed parameter systems

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Bipartite Consensus Problems of Heterogeneous Multi-Agent systems with Cooperative and Antagonistic Interactions

Bipartite Consensus Problems of Heterogeneous Multi-Agent Sy...

引用

data driven control and learning systems (ddcls)

作者： Yi Zhang Jinchao Li Mingjun Du Shaopeng Pang Peng Ji School of Information and Automation Engineering Qilu University of Technology (Shandong Academy of Science) Jinan Shandong Province P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this article explores the distributed control issue within a heterogeneous signed multi-agent system, comprising both first-order and second-order integrator agents. It devises consensus protocols for an undirected communication topology within structurally balanced networks. through the application of graph theory and Lyapunov methods, it establishes the necessary conditions for achieving consensus in networks characterized by both cooperative and competitive interactions. the theoretical results are corroborated by simulation experiments.

关键词： learning systems Network topology Decentralized control Graph theory Topology Consensus protocol Complexity theory

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Event-Triggered Model-Free Adaptive Sliding Mode control for a Class of Nonlinear systems

Event-Triggered Model-Free Adaptive Sliding Mode Control for...

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data driven control and learning systems (ddcls)

作者： Ning Zhang Yugang Niu Xinyu Lv Bei Chen The Key Laboratory of Smart Manufacturing in Energy Chemical Process (East China University of Science and Technology) Ministry of Education Shanghai China The School of Electric and Electronic Engineering Shanghai University of Engineering Science Shanghai China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this work proposes an event-triggered model-free adaptive sliding mode control (SMC) strategy for a class of discrete-time nonlinear networked control systems. Under the data-driven framework, a nonlinear dynamic linearization model is obtained via the time-varying pseudo-partial derivative (PPD). then, an event-triggered mechanism is designed to reduce the transmission of system signal without relying on any model information. In addition, based on the tracking error, an event-triggered sliding mode controller is designed to drive systems state into a specified sliding surface and achieve the output track objective. Finally, the availability of the proposed data-driven event-triggered SMC scheme is verified by a numerical simulation.

关键词： learning systems Adaptation models Adaptive systems Networked control systems Numerical simulation data models Numerical models

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Finite-Time Annular Domain Stability Analysis of Switched Positive Linear systems

Finite-Time Annular Domain Stability Analysis of Switched Po...

引用

data driven control and learning systems (ddcls)

作者： Zhiguo Yan Zhenyu Tang Baolong Zhu School of Information and Automation Qilu University of Technology (Shandong Academy of Sciences) Jinan P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this article investigates the issue of finite-time annular domain stability (FTADS) of switched positive linear systems (SPLSs), particularly focusing on the incorporation of the concept of average dwell time (ADT). By using the multiple linear copositive Lyapunov function (MLCLF), a sufficient condition for FTADS of SPLSs is derived. the derived condition is formulated as a linear programming problem, facilitating numerical verification. the proposed result is substantiated through two numerical examples.

关键词： Linear systems learning systems Sufficient conditions Uncertainty Switches Linear programming Stability analysis

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Model-Free Adaptive Cloud Elastic control for Small Nonlinear Agent systems

Model-Free Adaptive Cloud Elastic Control for Small Nonlinea...

引用

data driven control and learning systems (ddcls)

作者： Lin Zhu Dongsheng Yang Weiwei Che College of Information Science and Engineering Northeastern University Shenyang Liaoning PR China School of Automation Qingdao University Shandong Qingdao PR China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In this paper, for unknown small nonlinear multi-agent(SNMA) systems without large computing capabilities, cloud computing technology is used to calculate control commands and achieve multi-agent consensus control. At the same time, considering the security problem of the network channel between the cloud and the agent, a model-free adaptive cloud elastic control(MFACEC) strategy was proposed. Under the MFACEC control strategy, each agent whose model is unknown is able to complete consensus tracking. Finally, simulation results show the effectiveness of the algorithm.

关键词： learning systems Cloud computing Adaptation models Adaptive systems Computational modeling Simulation Consensus control

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