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A new adaptive identification framework for nonlinear multi-input multi-output systems under colored noise

APPLIED MATHEMATICAL MODELLING 2022年 103卷 105-121页

作者： Li, Linwei Zhang, Huanlong Zhang, Jie Ren, Xuemei Zhengzhou Univ Light Ind Sch Elect & Informat Engn Zhengzhou 450000 Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

In this paper, an adaptive identification scheme is proposed for nonlinear multi-input multi-output systems with colored noise based on a novel parameter update law. With the help of the hierarchical principle, the identification model is decomposed into three sub-models in which the computational burden is reduced. For each sub-model, the identification algorithm is proposed to estimate the sub-model parameters. In the process of the identification algorithm design, considering the system information corrupted by the noise, an adaptive filter gain is exploited to extract helpful identification data, in which a filter is designed using the system data instead of the independent design. Based on several auxiliary filtered variables, the estimation error data are obtained, and a new parameter adaptive law with a variable learning gain is proposed according to the estimation error data. Compared with the classic parameter update law, the parameter estimation update is derived based on the estimation error information instead of other error information, such as prediction error information. Under the persistent excitation condition, all the estimated parameters converge to the true parameters. an example is used and two experiments are conducted to test the outstanding identification performance of the proposed algorithm in terms of convergence rate and identification accuracy. (c) 2021 Elsevier Inc. All rights reserved.

关键词： multi-input multi-output system Adaptive parameter estimation Identification error information Hierarchical principle

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L1 adaptive sampled-data control for uncertain multi-input multi-output systems

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AUTOMATICA 2019年 103卷 346-353页

作者： Jafarnejadsani, Hamidreza Lee, Hanmin Hovakimyan, Naira Univ Illinois Dept Mech Sci & Engn Urbana IL 61801 USA Univ Illinois Dept Aerosp Engn Urbana IL 61801 USA

This paper develops a sampled-data (SD) controller for uncertain multi-input multi-output (MIMO) systems, possibly with non-minimum phase zeros, using the L-1 adaptive control architecture. The proposed controller compensates for disturbances and uncertainties within the bandwidth of a low-pass filter. Sufficient conditions for robust stability are obtained for the closed-loop system with SD controller, where the input/output signals are held constant over a sampling period. It is shown that the hybrid closed-loop system can recover the performance of a continuous-time reference system as the sampling time of the SD controller tends to zero. Simulation examples are provided to validate the theoretical findings. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： multi-input multi-output system L-1 adaptive sampled-data control output-feedback Disturbance compensation Digital controller Non-minimum-phase system

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A generalized sensitivity analysis method based on variance and covariance decomposition of summatory functions for multi-input multi-output systems

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2021年 385卷 114009-114009页

作者： Liu, Qiming Tong, Nichen Wu, Xingfu Han, Xu Chen, Chao Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin 300401 Peoples R China Hunan Univ Coll Mech & Vehicle Engn State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Hunan Peoples R China State Key Lab Adv Power Semicond Devices Zhuzhou 412001 Hunan Peoples R China

Sensitivity analysis is a useful means to quantify the impact of a set of input variables on an output response. However, many traditional sensitivity analysis methods are applicable only to multi-input single-output (MISO) systems and are powerless for multi-input multi-output (MIMO) systems. This paper presents a global sensitivity analysis method based on variance and covariance decomposition (VCD-GSA) of summatory functions for MIMO systems. For a MIMO system with n input variables and m output responses, a set of summatory functions can be constructed by the addition and subtraction of any two output response functions. Each output response function is represented using this set of summatory functions. The variances and covariances of all the output responses are obtained by the integral calculation of the high-dimensional model representations(HDMRs) of these summatory functions. We define the total fluctuation by the sum of the variances and covariances on multiple responses, and the partial fluctuations by the sum of partial variances of a series of summatory functions. Subsequently, we define the s-order sensitivity index of the MIMO system by the ratio of the partial fluctuation on s-order function terms in HDMRs and total fluctuation. The variable sensitivity index is the sum of all of the s-order sensitivity indices, including the contribution of the input variable. The proposed VCD-GSA method is suitable for a uniform or Gaussian distribution. It is also suitable for some complex problems involving variables with correlation. Several numerical examples and engineering applications demonstrate the advantage and practicality of the proposed VCD-GSA method. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Global sensitivity analysis (GSA) High dimensional model representations (HDMRs) Summatory functions Variance and covariance decomposition (VCD) multi-input multi-output system

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A New Approach to Adaptive Control of multi-input multi-output systems Using multiple Models

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JOURNAL OF DYNAMIC systemS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME 2015年第9期137卷 091009-091009页

作者： Ahmadian, Narjes Khosravi, Alireza Sarhadi, Pouria Babol Noushirvani Univ Technol Fac Comp & Elect Engn Babol Sar *** Iran Islamic Azad Univ South Tehran Branch Dept Control Engn Tehran 443511365 Iran

This paper proposes a novel method as second level adaptation using multiple models to identify and control of a class of multi-input multi-output (MIMO) systems. Different uncertain environments change the system parameters and create multiple operating conditions. These conditions are designed as multiple identification models in a model bank using adaptive laws. These models are evaluated using some estimated weighting factors based on the errors between each of the models and the actual plant. The evaluated models are effectively used in identification and control process. Bounded signals, proper closed-loop tracking performance, and rapid and accurate parameter convergence to their real values are achieved through simulation results.

关键词： multiple models multi-input multi-output system second level adaptation

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A self-tuning fuzzy controller for a class of multi-input multi-output nonlinear systems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2011年第2期24卷 238-250页

作者： Xu, Chengying Shin, Yung C. Purdue Univ Sch Mech Engn W Lafayette IN 47907 USA Univ Cent Florida Dept Mech Mat & Aerosp Engn Orlando FL 32816 USA

This paper presents a systematic design procedure of a multivariable fuzzy controller for a general multi-input multi-output (MIMO) nonlinear system with an input-output monotonic relationship or a piecewise monotonic relationship for each input-output pair. Firstly, the system is modeled as a Fuzzy Basis Function Network (FBFN) and its Relative Gain Array (RGA) is calculated based on the obtained fuzzy model. The proposed multivariable fuzzy controller is constructed with two orthogonal fuzzy control engines. The horizontal fuzzy control engine for each system input-output pair has a hierarchical structure to update the control parameters online and compensate for unknown system variations. The perpendicular fuzzy control engine is designed based on the system RGA to eliminate the multivariable interaction effect. The resultant closed-loop fuzzy control system is proved to be passive stable as long as the augmented open-loop system is input-output passive. Two sets of simulation examples demonstrate that the proposed fuzzy control strategy can be a promising way in controlling multivariable nonlinear systems with unknown system uncertainties and time-varying parameters. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Fuzzy control Self-tuning systems multi-input multi-output system Relative gain array Non-linear systems

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Fault tolerant flight control system design using a multiple model adaptive controller

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2009年第G1期223卷 39-50页

作者： Jung, B. Kim, Y. Ha, C. Seoul Natl Univ Inst Adv Aerosp Technol Sch Mech & Aerosp Engn Seoul 151742 South Korea

This article presents a fault tolerant flight control system using multiple model adaptive control (MMAC). To apply this method to the aircraft, model reference adaptive control is extended to a linear discrete-time multiple-input multiple-output system. multiple models can be determined by various flight conditions with respect to possible aircraft fault situations. In this article, the damages of control effectors are considered as aircraft faults. The aerodynamic coefficients of fault model are derived by a damaged control surface area. In the MMAC, how to select a proper fault model and how to switch into the selected model are significant issues. The conventional mode switching method, however, has a tendency to make the system fluctuate because of frequent mode switching, and therefore may deteriorate the performance of the aircraft. In this study, the modified adaptive mode switching scheme and new decision logic are proposed to improve the adaptiveness in the transient state dynamics of system. Numerical simulations are performed for a linear discrete-time aircraft model to validate the effectiveness of the proposed method.

关键词： fault tolerant flight control multiple model adaptive control switching scheme decision logic multi-input multi-output system aircraft fault modelling

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Application of Taguchi design in system identification: A simple, generally applicable and powerful method

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MEASUREMENT 2020年 151卷 106879-000页

作者： Ghazbi, Somayeh Norouzi Akbarzadeh, Alireza Akbarzadeh-T, Mohammad-R Ferdowsi Univ Mashhad Dept Mech Engn POB 91775-1119 Mashhad *** Razavi Khorasan Iran Ferdowsi Univ Mashhad Dept Elect Engn Mashhad Razavi Khorasan Iran Ferdowsi Univ Mashhad Ctr Excellence Soft Comp & Intelligent Informat P Mashhad Razavi Khorasan Iran

This paper is a development of the authors' recently published idea on a simple and generally applicable system identification algorithm based on Taguchi method. In the previous paper, the effectiveness of the algorithm in identifying nonlinear systems was shown. This paper presents the modified algorithm with the capability of identifying multi-input multi-output (MIMO) systems, and systems with unknown parameters of quite different value-ranges. Regarding the former, a contribution analysis is introduced and utilized to determine that the model between which of the input-output pairs should be identified first. The results proved that this idea significantly results in less time-cost. Moreover, the study develops the identification algorithm by adding a step of breaking down the identification process to multiple steps, each identifying a group of parameters that are in about the same value-range. Utilizing Taguchi method that includes human intelligence in the loop, the presented method effectively saved the process time and led to a more accurate estimation. In the method presented, the algorithm identifies a group of parameters at each step starting with the most contributor ones rather than identifying all of them at once. To group parameters, a contribution analysis is employed. To prove the effectiveness of the method, without loss of generality, the algorithm is applied on a multi-input single-output system. The experimental test-setup used in this study is a custom-made linear series elastic actuator that is designed for lower limb exoskeletons in Ferdowsi University of Mashhad (FUM)-Robotic Laboratory. (C) 2019 Published by Elsevier Ltd.

关键词： system identification Taguchi multi-input multi-output system MIMO Nonlinear systems Non-parametric system

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5Almost disturbance decoupling control of mimo nonlinear system subject to feedback linearization and a feedforward neural network: Application to half-car active suspension system

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INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY 2010年第4期11卷 581-592页

作者： Li, T. H. S. Huang, C. J. Chen, C. C. Natl Chiyi Univ Dept Elect Engn Chiayi 60004 Taiwan Natl Cheng Kung Univ Dept Elect Engn Tainan 70101 Taiwan

A novel tracking and almost disturbance decoupling problem of multi-input, multi-output (MIMO) nonlinear systems based on feedback linearization and a multi-layered feedforward neural network approach has been proposed. The feedback linearization and neural network controller guarantees exponentially global uniform ultimate bounded stability and almost disturbance decoupling performance without using any learning or adaptive algorithms. The new approach renders the system to be stable with the almost disturbance decoupling property at each step when selecting weights to enhance the performance if the proposed sufficient conditions are maintained. One example, which cannot be solved by the existing approach of the almost disturbance decoupling problem because it requires the sufficient conditions that the nonlinearities that multiply the disturbances satisfy structural triangular conditions, is proposed to exploit the fact that the tracking and the almost disturbance decoupling performances are easily achieved by the proposed approach. In order to demonstrate the practical applicability, a famous half-car active suspension system is investigated.

关键词： Feedforward neural network multi-input multi-output system Almost disturbance decoupling Half-car active suspension system Feedback linearization approach

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Operator-based robust nonlinear control system design for MIMO nonlinear plants with unknown coupling effects

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INTERNATIONAL JOURNAL OF CONTROL 2010年第9期83卷 1939-1946页

作者： Deng, Mingcong Bi, Shuhui Okayama Univ Grad Sch Nat Sci & Technol Okayama 7008530 Japan

In this article, operator-based robust nonlinear control system design for multi-input multi-output (MIMO) plants with unknown coupling effects is considered. That is, by using operator-based robust nonlinear control design, coupling effects existing in the MIMO nonlinear plants can be decoupled based on a feedback design and robust right coprime factorisation approach, the coupling effects caused by controllers and plant outputs can be stabilised by using definition of Lipschitz operator and contraction mapping theorem, and output tracking performance can be realised by a tracking design scheme. Finally, a simulation example about temperature control process of 3-input/3-output aluminum plate is given to support the theoretical analysis.

关键词： multi-input multi-output system unknown coupling effects operator-based robust right coprime factorisation Lipschitz operator contraction mapping theorem

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FEEDFORWARD NEURAL NETWORK AND FEEDBACK LINEARIZATION CONTROL DESIGN OF BILSAT-1 SATELLITE system

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2012年第10A期8卷 6921-6943页

作者： Chien, Ting-Li WuFeng Univ Dept Elect Engn Minhsiung 62153 Chiayi County Taiwan

The paper presents a novel feedforward neural network and feedback linearization control of BILSAT-1 satellite system for the almost disturbance decoupling performance. The proposed controller guarantees exponentially global uniform ultimate bounded stability and the almost disturbance decoupling performance without using any learning or adaptive algorithms. The proposed approach provides the architecture of the neural network and the weights among the layers in order to guarantee stability of the system. Moreover, the new approach renders the system to be stable with the almost disturbance decoupling property at each step of selecting weights to enhance the performance if the proposed sufficient conditions are maintained. One example, which cannot be solved by the first paper on the almost disturbance decoupling problem, is proposed in this paper to exploit the fact that the tracking and the almost disturbance decoupling performances are easily achieved by the proposed approach.

关键词： BILSAT-1 satellite system Feedforward neural network Almost disturbance decoupling multi-input multi-output system Feedback linearization approach Composite Lyapunov approach

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