Model Free Adaptive Control with Control Input Deadzone

In this work, a novel higher-order model-free adaptive control scheme is presented based on a dynamic linearization approach for a class of discrete-time single input and single output (SISO) nonlinear systems. The control scheme consists of an adaptive control law, a parameter estimation law, and a reset mechanism. The design and analysis of the proposed control approach depends merely on the measured input and output data of the controlled plant. The control performance is improved by using more information of control input and output error measured from previous sampling time instants. Rigorous mathematical analysis is developed to show the bounded input and bounded output (BIBO) stability of the closed-loop system. Two simulation comparisons show the effectiveness of the proposed control scheme.

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Model-Free Adaptive Control Algorithm with Data Dropout Compensation

Mathematical Problems in Engineering ◽

10.1155/2012/329186 ◽

2012 ◽

Vol 2012 ◽

pp. 1-14 ◽

Cited By ~ 11

Author(s):

Xuhui Bu ◽

Fashan Yu ◽

Zhongsheng Hou ◽

Hongwei Zhang

Keyword(s):

Adaptive Control ◽

Measurement Data ◽

Dropout Rate ◽

Linearization Method ◽

Control Input ◽

Model Free ◽

Output Performance ◽

Data Dropout ◽

System Output ◽

Adaptive Control Algorithm

The convergence of model-free adaptive control (MFAC) algorithm can be guaranteed when the system is subject to measurement data dropout. The system output convergent speed gets slower as dropout rate increases. This paper proposes a MFAC algorithm with data compensation. The missing data is first estimated using the dynamical linearization method, and then the estimated value is introduced to update control input. The convergence analysis of the proposed MFAC algorithm is given, and the effectiveness is also validated by simulations. It is shown that the proposed algorithm can compensate the effect of the data dropout, and the better output performance can be obtained.

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Modified Model-Free Adaptive Control Method Applied to Vibration Control of an Elastic Crane

Volume 6: 15th International Conference on Multibody Systems, Nonlinear Dynamics, and Control ◽

10.1115/detc2019-97654 ◽

2019 ◽

Author(s):

Hoang Anh Pham ◽

Dirk Söffker

Keyword(s):

Adaptive Control ◽

Control Method ◽

Structural Information ◽

Control Strategies ◽

Control Input ◽

Tracking Errors ◽

Adaptive Controllers ◽

Control Approach ◽

Model Free ◽

Controlled Systems

Abstract Model-free adaptive control (MFAC) is a data-driven control approach receiving increased attention in the last years. Different model-free-based control strategies are proposed to design adaptive controllers when mathematical models of the controlled systems should not be used or are not available. Using only measurements (I/O data) from the system, a feedback controller is generated without the need of any structural information about the controlled plant. In this contribution an improved MFAC is discussed for control of unknown multivariable flexible systems. The main improvement in control input calculation is based on the consideration of output tracking errors and its variations. A new updated control input algorithm is developed. The novel idea is firstly applied for controlling vibrations of a MIMO ship-mounted crane. The control efficiency is verified via numerical simulations. The simulation results demonstrate that vibrations of the elastic boom and the payload of the crane can be reduced significantly and better control performance is obtained when using the proposed controller compared to standard model-free adaptive and PI controllers.

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Model free adaptive control scheme based on Elman neural network prediction

2020 39th Chinese Control Conference (CCC) ◽

10.23919/ccc50068.2020.9188399 ◽

2020 ◽

Author(s):

Yong-wen LIAO ◽

Yi-ming MA ◽

Bao-wei CHEN ◽

Geng-da LI ◽

De-liang ZENG

Keyword(s):

Neural Network ◽

Adaptive Control ◽

Elman Neural Network ◽

Model Free ◽

Control Scheme ◽

Neural Network Prediction ◽

Network Prediction ◽

Adaptive Control Scheme

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Model-Free Adaptive Control Based on Neural Network Observer for the Chaotic Power Supply System

2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) ◽

10.1109/ddcls49620.2020.9275230 ◽

2020 ◽

Author(s):

Ao Bai ◽

Yanhong Luo ◽

Huaguang Zhang

Keyword(s):

Neural Network ◽

Adaptive Control ◽

Power Supply ◽

Power Supply System ◽

Supply System ◽

Model Free ◽

Neural Network Observer

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Model Free Adaptive Control for Thrust Cylinder of Water Well Drilling Rig

2020 Chinese Automation Congress (CAC) ◽

10.1109/cac51589.2020.9326868 ◽

2020 ◽

Author(s):

Yao Wenlong ◽

Qi Guanhua ◽

Yang ke ◽

Chi Ronghu ◽

Yang Dejing

Keyword(s):

Adaptive Control ◽

Well Drilling ◽

Drilling Rig ◽

Model Free ◽

Water Well

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An Unmanned Vehicle Trajectory Tracking Method based on Improved Model-free Adaptive Control Algorithm

2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) ◽

10.1109/ddcls49620.2020.9275050 ◽

2020 ◽

Author(s):

Dongdong Yuan ◽

Yankai Wang

Keyword(s):

Adaptive Control ◽

Trajectory Tracking ◽

Control Algorithm ◽

Tracking Method ◽

Unmanned Vehicle ◽

Model Free ◽

Vehicle Trajectory ◽

Improved Model ◽

Adaptive Control Algorithm

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Anti-noise model-free adaptive control and its application in the circulating fluidized bed boiler

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ◽

10.1177/0959651820979376 ◽

2020 ◽

pp. 095965182097937

Author(s):

Na Dong ◽

Wenjin Lv ◽

Shuo Zhu ◽

Donghui Li

Keyword(s):

Adaptive Control ◽

Complete Convergence ◽

Circulating Fluidized Bed ◽

Control Method ◽

Noise Model ◽

Good Resistance ◽

Industrial Systems ◽

Noise Interference ◽

Model Free ◽

Tracking Differentiator

Model-free adaptive control has been developed greatly since it was proposed. Up to now, model-free adaptive control theory has become mature and tends to be an effective solution for complex unmodeled industrial systems. In practical industrial processes, most control systems are inevitably accompanied by noise that will result in indelible error and may further cause inaccurate feedback to the output. In order to solve this kind of problem with model-free technique, this article incorporates an improved tracking differentiator into model-free adaptive control. After that, the anti-noise model-free adaptive control method with complete convergence analysis is proposed. Meanwhile, numerical simulation proves that the improved control method can quickly track a given signal with good resistance to noise interference. Finally, the effectiveness and practicability of the proposed algorithm are verified by experiments through the control of drum water level of circulating fluidized.

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