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model free adaptive control for a Class of Nonlinear Systems Using Quantized Information

ASIAN JOURNAL OF control 2018年第2期20卷 962-968页

作者： Bu, Xuhui Qiao, Yingxu Hou, Zhongsheng Yang, Junqi Henan Polytech Univ Sch Elect Engn & Automat Jiaozuo Peoples R China Henan Polytech Univ Coll Comp Sci & Technol Jiaozuo Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Adv Control Syst Lab Beijing Peoples R China

This paper considers the stability of model free adaptive control systems with quantized information. Two quantized model free adaptive control (QMFAC) algorithms are proposed by using different signal quantization schemes, and here the logarithmic quantizer is introduced to decode these signals with a number of quantization levels. By using the sector bound method, the stability conditions of proposed QMFAC algorithms can be given and the effect of quantization error for such systems can also be discussed. It is shown that the tracking error under the QMFAC algorithm with system output quantized signal is proved to converge to a bound, and the bound depending on quantization density and desired trajectory. Thus, the tracking error under the QMFAC algorithm with tracking error quantized signal converges to zero. Two illustrative examples are given to validate the theoretical results.

关键词： model free adaptive control Data quantization Stability Data driven design

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model free adaptive control with data dropouts

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第8期38卷 10709-10717页

作者： Hou, Zhongsheng Bu, Xuhui Beijing Jiaotong Univ Adv Control Syst Lab Beijing 100044 Peoples R China

It is well-known that robustness refers to the ability dealing with unknown uncertainties or unmodeled dynamics of the model-based control theory. However, this kind of robustness no longer has any significance for model free or data-driven control theory because the controller is designed only using I/O data of the controlled plant. In this paper, a novel robustness of the model free control or data-driven control is presented when the system is controlled by the model free adaptive control (MFAC) scheme. It is assumed that an MFAC scheme is implemented via a network control system and that data dropout occurs due to a failing sensor, actuator or network failure, resulting in what it is called intermittent MFAC. The stability of such a MFAC scheme is analyzed by the statistical approach. It is shown that the MFAC system is still stable when data dropout occurs, and the data dropout impacts the convergence speed. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： model free adaptive control Data dropouts Robustness Intermittent measurement

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model free adaptive control Algorithm based on GRU network 33

Model Free Adaptive Control Algorithm based on GRU network

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33rd Chinese control and Decision Conference (CCDC)

作者： Sun, Jinggao Chen, Xianfeng Su, Guanghao Pan, Hongguang East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China Xian Univ Sci & Technol Sch Elect & Control Engn Xian 710049 Peoples R China

ISBN: (纸本)9781665440899

The application of traditional adaptive control algorithm usually depends on the precise mathematical model of process, but it is difficult to establish a mathematical model for a dynamic process. The existing model free adaptive (MFA) control algorithm structure is based on Back Propagation(BP) Neural Network, which does not fully take into account the continuous characteristic relationship between the input timing error sequences. According to this actual problem, an improved MFA control algorithm based on Gated Recurrent Unit (GRU) network is proposed in this paper, compared with BP network, GRU network has more advantages in processing timing series. It fully considers the information of error sequence, and more appropriate manipulated variable could be generated automatically to satisfy the demand of an open-loop stable and controllable single-variable, multivariable or large delay process system without the need for complicated manual adjustments, quantitative knowledge of the process or the identifier of the controlled system and learning process. Simulation results demonstrate that the GRU-MFA learning algorithm performs better in terms of stability, response speed and adaptability than the BP-MFA algorithm without human intervention.

关键词： model free adaptive control Timing error sequences Gated recurrent unit Learning algorithm

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model free adaptive control for the Temperature Adjustment of UGI Coal Gasification Process in Synthetic Ammonia Industry 9

Model Free Adaptive Control for the Temperature Adjustment o...

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9th IEEE Data Driven control and Learning Systems Conference (DDCLS)

作者： Liu, Shida Sun, Jiao Ji, Honghai Hou, Zhongsheng Fan, Lingling North China Univ Technol Sch Elect & Control Engn Beijing 100093 Peoples R China Qingdao Univ Sch Automat Qingdao 266071 Peoples R China Beijing Informat Sci & Technol Univ Sch Automat Beijing 100192 Peoples R China

ISBN: (纸本)9781728159225

In this manuscript, a data-driven model free adaptive control (MFAC) method is introduced for a UGI gasifier. During the UGI gasification process, the temperature of crude gas inside the UGI gasifier is very important. However, the accurate multi-input and multi-output mathematical model describing the dynamics of the crude gas temperature cannot be created due to the complexity of the gasification systems. The main feature of MFAC method is that the controller design depends only on the input and the output measurement data of the controlled plant. Specifically, the MFAC controller is designed via a novel dynamic linearization technique with a time varying parameter termed Poseudo-Jacobian Matrix (PGM), which contains the coupling information of each output variable. Further, simulation results show that MFAC has a very reliable tracking ability for the temperature adjustment of the gasification process.

关键词： data-driven control UGI gasifier model free adaptive control temperature control

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model free adaptive control Algorithm based on ReOSELM for Autonomous Driving Vehicles 40

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

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40th Chinese control Conference (CCC)

作者： Zhang, Xiaofei Ma, Hongbin Wang, Zhichao Fan, Mingyu Zhao, Bolin Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Beijing Inst Technol State Key Lab Intelligent Control & Decis Complex Beijing 100081 Peoples R China Meituan Grp Beijing 100102 Peoples R China Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China

ISBN: (纸本)9789881563804

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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model free adaptive control based on FRM with an approach to reduce the control activity

Model Free Adaptive Control based on FRM with an approach to...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Kadri, Muhammad Bilal Hussain, Sarfraz PNEC Natl Univ Sci & Technol Elect & Power Engn Dept Karachi Pakistan

ISBN: (纸本)9781424465880

model free adaptive control (MFAC) has been used to reject the sensor noise measuring the controlled output. The MFAC is trained using feedback error learning law. The measurement noise makes the control error (used by feedback error learning) very noisy which results in increased control activity as well as corruption of the past learning. The MFAC is modeled as a Fuzzy Relational model (FRM). FRM can be trained to represent the sensor noise occurring at the plant output in the fuzzy control signal. This representation of the sensor noise can be effectively utilized to reduce the control activity;hence increasing the life span of an actuator and contributing towards enhanced control performance. The control activity can be reduced by using conditional defuzzification which defuzzifies the fuzzy control signal by taking into consideration the uncertainty representation. By employing conditional defuzzification the controller will not respond to the noisy measurement of the plant output. The superiority of the conditional defuzzification used in conjunction with MFAC is demonstrated by the fact that with increased sensor noise the control activity reduces while maintaining the same control performance.

关键词： model free adaptive control fuzzy relational models conditional defuzzification

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model free adaptive control for Automatic Car Parking Systems 11

Model Free Adaptive Control for Automatic Car Parking System...

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11th World Congress on Intelligent control and Automation

作者： Dong Hang-rui Jin Shang-tai Hou Zhong-sheng Beijing Jiaotong Univ Sch Elect & Informat Engn Adv Control Syst Lab Beijing 100044 Peoples R China

ISBN: (纸本)9781479958252

In this paper, a model free adaptive control (MFAC) scheme is proposed for automatic car parking systems. The scheme consists of a control algorithm, a parameter estimation algorithm and a parameter reset algorithm. The design of the proposed scheme only uses the input and output (I/O) data, and does not involve any model information of the controlled car. Therefore, the MFAC based automatic parking system is applicable for different kinds of car. The simulation comparisons among MFAC scheme and PID control scheme are given for different kinds of car with different parking speed. The simulation results show that the proposed MFAC scheme has smaller tracking errors in the orientation angle of the car, the x axis and y axis.

关键词： model free adaptive control Automatic parking PID

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model free adaptive control scheme based on Elman neural network prediction 39

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

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39th Chinese control Conference (CCC)

作者： Liao Yong-wen Ma Yi-ming Chen Bao-wei Li Geng-da Zeng De-liang North China Elect Power Univ Sch Control & Comp Engn Beijing 102200 Peoples R China Guodian New Energy Technol Res Inst Co Ltd Beijing 102200 Peoples R China

ISBN: (纸本)9789881563903

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 under Sigmoid Function and Newton Method for Estimation of the Pseudo-Partial-Derivative in Nonlinear Systems 12

Model Free Adaptive Control under Sigmoid Function and Newto...

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12th IEEE/IAS International Conference on Industry Applications (INDUSCON)

作者： Durandal, Edwin C. Rosa, Guilherme A. Araujo, Rejane de B. Coelho, Antonio A. R. Univ Fed Santa Catarina Dept Automat & Syst BR-88040900 Florianopolis SC Brazil

ISBN: (纸本)9781509051274

The model free Learning adaptive control (MFLAC) is based on the pseudo-partial-derivative (PPD) calculated from the input and output signals of the system to be controlled and also using a fixed penalty factor that weights the control energy to provide a good behavior to the feedback process. This paper presents two new adaptation algorithms to adjust the PPD, based on the Sigmoid Function (SF) and another on the Newton Method (NM). With these proposals, alternative control designs are obtained for the on-line tuning of the PPD, ensuring good stability and robustness for the closed-loop system. Simulations on a nonlinear coupled liquid-tank plant and a laboratory apparatus on a damped pendulum show the efficiency and superiority of the proposed adaptive controllers when using estimation mechanisms to adjust these parameter over the standard MFLAC. Performance indices are used to show the improvement of the behavior of the proposal control algorithms.

关键词： model free adaptive control pseudo-partial-derivative gradient method sigmoid function newton method non-linear system autotuning

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model free adaptive control for Organic Rankine Cycle Processes 6

Model Free Adaptive Control for Organic Rankine Cycle Proces...

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2017 IEEE 6th Data Driven control and Learning Systems Conference (DDCLS’17)

作者： Mingming Lin Ronghu Chi Xiaohong Yin Ximei Liu School of Automation & Electronic Engineering Qingdao University of Science & Technology

ISBN: (纸本)9781509054626

In this paper, a data-driven model free adaptive control(MFAC) scheme, based on a novel transformation and linearization of the evaporator model, is developed for superheating regulation in an organic Rankine cycle(ORC) process. The designing procedures of the proposed approach are presented. The main feature of the method is that the controller design depends only on the measured input pump rotating speed and output superheating. Simulation results demonstrate the effectiveness of the proposed controller.

关键词： Data-driven model free adaptive control Organic Rankine Cycle Superheating

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