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检索条件"主题词=model free adaptive control"

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model free adaptive control scheme based on Elman neural network prediction

Model free adaptive control scheme based on Elman neural net...

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第三十九届中国控制会议

作者： LIAO Yong-wen MA Yi-ming CHEN Bao-wei LI Geng-da ZENG De-liang School of control and computer engineering North China Electric Power University Guodian New Energy Technology Research Institute Co. Ltd

model free adaptive control（MFAC） is a typical data-driven control method. This method only uses the input and output data of the system to design the controller and gets rid of the dependence of the controller design on the model. However,this control method is difficult to control large delay and multi disturbance objects. In order to solve these problems, this paper proposes an anti disturbance model free adaptive controller based on Elman neural network prediction, which uses neural network to predict the system output, reduces the calculation amount of the algorithm, and speeds up the convergence speed;in addition, when solving the control variable, the disturbance factor is added to make the controller act in advance and improve the anti disturbance ability. This control method is applied to the coordinated control system of drum furnace, which shows the feasibility and effectiveness of this control method.

关键词： model free adaptive control Elman network coordination system

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model free adaptive control Algorithm based on ReOSELM for Autonomous Driving Vehicles

Model Free Adaptive Control Algorithm based on ReOSELM for A...

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第40届中国控制会议

作者： Xiaofei Zhang Hongbin Ma Zhichao Wang Mingyu Fan Bolin Zhao School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent control and Decision of Complex Systems Beijing Institute of Technology The College of Computer Science and Artificial Intelligence Wenzhou University

Different road conditions and dynamic environment bring significant challenges to the control system of autonomous driving vehicle（ADV）. As is known, historical data collected from ADV contains valuable information about control systems,therefore, it is a promising thing to study adaptive control algorithms that have certain learning ability. In order to improve the control performance of ADV and the efficiency in data usage, in this paper, a model free adaptive control algorithm based on regularized online sequential extreme learning machine（ReOSELM） is introduced, it is difficult to analyze the algorithm based on neural network, and the system stability by improved update algorithm of ReOSELM is proved. Simulation results indicate that the proposed algorithm is effective in improving control precision when ADV is turning, and experimental results on an autonomous driving vehicle show that this algorithm is effective in real environment.

关键词： data-driven control autonomous driving vehicle model free adaptive control regularized online sequential extreme learning machine

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Enhanced model free adaptive control by Integrating with Lazy Learning

Enhanced Model Free Adaptive Control by Integrating with Laz...

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第24届中国控制与决策会议

作者： Yuanming Zhu 1 , Zhongsheng Hou 2 Advanced control Systems Lab, School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100044

An enhanced model free adaptive control （EMFAC） algorithm is presented for a class of nonlinear system by integrating lazy learning technique to achieve adaptive adjustment of penalty parameter. In proposed EMFAC, penalty parameter is queried from database which consists of the historical information vector, rather than set by try and error method. The main feature of proposed method is that, controller is designed merely by the measured input-output data of controlled system without any explicit or implicit use of the physical model. Its effectiveness is further verified by simulation results

关键词： Data Driven control model free adaptive control Lazy Learning Modular Design Method

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A Statistical Analysis of model free adaptive control with Measurement Disturbance

A Statistical Analysis of Model Free Adaptive Control with M...

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第二十九届中国控制会议

作者： BU Xuhui,HOU Zhongsheng,JIN Shangtai Advanced control Systems Lab.,Beijing Jiaotong University,Beijing 100044,P.R.China

This work is considered with the robustness of model free adaptive control（MFAC）systems with measurement *** using statistical analysis,it is possible to investigate the influence of the measurement disturbance with statistical *** to suppress the measurement disturbance,a modified MFAC algorithm with a decreasing gain is also *** rigorous analysis,it is shown that the modified algorithm has better robustness to measurement *** analysis is supported by simulations.

关键词： model free adaptive control Measurement Disturbance Statistical Analysis Robustness

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model-free adaptive control Based on Local Dynamic Linearization

Model-Free Adaptive Control Based on Local Dynamic Lineariza...

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第30届中国控制与决策会议

作者： Ronghu Chi Shuhua Zhang Yu Hui College of Automation and Electronics Engineering Qingdao University of Science and Technology

Linearization technique is essential for the controller design and analysis of complex nonlinear plants. At least, the linear affine form of the original nonlinear process is required. However, the traditional linearization methods are approximation-based with unmodeled dynamics involved. In this work, a new local dynamic linearization method is proposed using differential mean-value theorem, which can be estimated by using the I/O data only. Then, a new model-free adaptive control is proposed by using the principle of optimality, where the controller design and analysis is data-driven without using any model information. The effectiveness of the proposed method is demonstrated by the simulation results.

关键词： model free adaptive control Nonlinear discrete-time systems Local dynamic linearization Data-driven control

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Full Form Dynamic Linearization Based Data-driven MFAC for a Class of Discrete-time Nonlinear Systems

Full Form Dynamic Linearization Based Data-driven MFAC for a...

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2011 Chinese control and Decision Conference（CCDC）

作者： Yongqiang Li is with the Advanced control Systems Lab,School of Electronics and Information Engineering,Beijing Jiaotong University,Beijing 100044,China.Zhongsheng Hou is with the Advanced control Systems Lab,School of Electronics and Information Engineering,Beijing Jiaotong University,Beijing 100044,China.Xiangbin Liu is with the Advanced control Systems Lab,School of Electronics and Information Engineering,Beijing Jiaotong University,Beijing 100044,China.

In this paper,we propose a data-driven model free adaptive control （MFAC） scheme based on Full Form Dynamic Linearization （FFDL） for nonlinear ***,using FFDL technique,we transform a nonlinear system into an equivalent linear time-varying system with Pseudo-Partial Derivative vector as time-varying ***,we construct an indirect adaptive control system for the linear time-varying ***,we analyze the stability and the tracking error of the MFAC scheme,show that all the signals in the closed-loop are bounded provided the parameter variations are small and present the dynamic equation of the tracking error.

关键词： Data-driven control model free adaptive control Linear Time-varying System

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A Data-Driven adaptive Iterative Learning Predictive control for a Class of Discrete-time Nonlinear Systems

A Data-Driven Adaptive Iterative Learning Predictive Control...

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第二十九届中国控制会议

作者： SUN Heqing,HOU Zhongsheng Advanced control Systems Laboratory,School of Electronics and Information Engineering,Beijing Jiaotong University,Beijing 100044,P.R.China

On the basis of dynamic linearization method along the iteration axis,a novel data-driven adaptive iterative learning predictive control（AILPC）is presented for a class of general repeatable discrete-time nonlinear *** highlight of the algorithm is that the controller design only depends on the I/O data of the dynamical system without using any priori knowledge of the *** monotonic convergence and effectiveness of the AILPC algorithm are proven and verified through rigorous analyses,numerical example and freeway traffic flow control application.

关键词： Data-driven control Iterative Learning control Predictive control model free adaptive control

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