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Robust Distributed Diffusion recursive least squares algorithms With Side Information for Adaptive Networks

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2019年第6期67卷 1566-1581页

作者： Yu, Yi Zhao, Haiquan de Lamare, Rodrigo C. Zakharov, Yuriy Lu, Lu Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Sichuan Peoples R China Pontificia Univ Catolica Rio de Janeiro CETUC BR-22451900 Rio de Janeiro Brazil Univ York Dept Elect Engn York YO10 5DD N Yorkshire England Sichuan Univ Sch Elect & Informat Engn Chengdu 610017 Sichuan Peoples R China

This work develops robust diffusion recursive least-squares algorithms to mitigate the performance degradation often experienced in networks of agents in the presence of impulsive noise. The first algorithm minimizes an exponentially weighted least-squares cost function subject to a time-dependent constraint on the squared norm of the intermediate update at each node. A recursive strategy for computing the constraint is proposed using side information from the neighboring nodes to further improve the robustness. We also analyze the mean-square convergence behavior of the proposed algorithm. The second proposed algorithm is a modification of the first one based on the dichotomous coordinate descent iterations. It has a performance similar to that of the former, however, its complexity is significantly lower especially when input regressors of agents have a shift structure and it is well suited to practical implementation. Simulations show the superiority of the proposed algorithms over previously reported techniques in various impulsive noise scenarios.

关键词： Distributed algorithms diffusion cooperation dichotomous coordinate-descent impulsive noises recursive least squares algorithms

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Parametric Identification and Controller Design for a Differential-Drive Mobile Robot⁎

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IFAC-PapersOnLine 2018年第15期51卷 437-442页

作者： Alves, Tiago G. Lages, Walter F. Henriques, Renato V.B. Universidade Federal do Rio Grande do Sul Porto Alegre Brazil

This paper presents a dynamic model for a differential drive mobile robot, including the actuators effects and a methodology to parameterize the model in a linear fashion, enabling the use of the recursive least squares algorithm to identify the system parameters. In addition, two control laws are designed. The inner control loop linearizes the dynamics of the mobile robot using a linearizing state feedback and then an outer control loop tracks the linear and angular velocity references by using linear controllers. Identification and control results are shown for a real robot. © 2018

关键词： Controllers Dynamic models Feedback linearization Identification (control systems) Linear control systems Machine design Mobile robots Parameter estimation State feedback Three term control systems Control laws Controller designs Differential drive Linear controllers Parametric identification PID controllers recursive least squares algorithms Robot dynamics

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Parameter Identification of Train Basic Resistance Using Multi-Innovation Theory

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IFAC-PapersOnLine 2018年第18期51卷 637-642页

作者： Liu, Xiaoyu Ning, Bin Xun, Jing Wang, Cheng Xiao, Xiao Liu, Tong State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China School of Internet of Things Engineering Jiangnan University Wuxi214122 China Traffic Control Technology CO. Ltd. Beijing Research Institute Beijing100070 China

Train basic resistance is important for the design of the automatic train operation, which influences the efficiency, punctuality, stop precision, energy consumption, and the safety of the train. The multi-innovation theory is a novel concept which can improve the accuracy of parameter estimation and be used to modify the traditional recursive least squares algorithm. In this paper, we derive the regularization form of the multi-innovation least squares algorithm and apply it to the train basic resistance parameter estimation. The simulation results based on the Yizhuang Line of Beijing Subway indicate that, compared with traditional least squares algorithm, the multi-innovation least squares algorithm can provide higher estimation accuracy and robustness, and can be used for online identification. © 2018

关键词： Parameter estimation Energy utilization Light rail transit Accuracy of parameters Automatic train operations least squares algorithm Multi innovation identification On line identification recursive identification recursive least squares algorithms Urban rail transit

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Control of Systems Exhibiting Input Multiplicities using Dual Nonlinear MPC

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IFAC-PapersOnLine 2018年第20期51卷 110-115页

作者： Kumar, Kunal Patwardhan, Sachin C. Noronha, Santosh Systems and Control Engineering Indian Institute of Technology Bombay Mumbai India Department of Chemical Engineering Indian Institute of Technology Bombay Mumbai India

Control of a system exhibiting input multiplicity at an optimum (singular) operating point poses a challenging control problem due to loss of invertibility and change in the sign of the steady state gain in the neighbourhood of the optimum. In this work, for controlling systems exhibiting input multiplicities, we develop a novel adaptive dual NMPC (ADNMPC) formulation based on a Wiener model which is parameterized using orthonormal basis filters (OBF). The static nonlinear output map in the Wiener model is constructed using multidimensional quadratic polynomials. The OBF poles are chosen through an off-line identification exercise and the parameters of the static nonlinear map are updated on-line using recursive least squares algorithm. Similar to Kumar et al. (2017), by introducing concept of excitation horizon, the objective function in the NMPC formulation is modified to include terms that are sensitive to the parameter covariance. The proposed formulation provides su¢cient degrees of freedom to shape the probing signals. E¢cacy of the proposed approach is demonstrated by simulating problem of controlling a continuously operated fermenter system at its optimum operating point. Analysis of the simulation results shows that the proposed ADNMPC scheme judiciously injects perturbations into the process as and when required. The probing perturbations subside when the parameter estimates stabilize. In particular, it is observed that the intensity of the perturbation increase as the excitation horizon increases. The proposed on-line model adaptation also ensures better control performance with reference to a non-adaptive NMPC controller. © 2018

关键词： Model reference adaptive control Degrees of freedom (mechanics) Parameter estimation Adaptive Control Dual control Line identifications Nonlinear MPC Orthogonal basis filters Parameter covariance Quadratic polynomial recursive least squares algorithms

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Adaptive Model Predictive Control of a Quadrotor

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IFAC-PapersOnLine 2017年第2期50卷 157-162页

作者： Chikasha, Piwai N. Dube, Chioniso University of the Witwatersrand JohannesburgWits2050 South Africa

This work explores the application of Adaptive Model Predictive Control (AMPC) to quadrotor altitude control. Model Predictive Control (MPC) is a very powerful method of Advanced Control, utilizing an implicit model to make output predictions which are in turn used in computing control action. Dynamic Matrix Control (DMC) was the first MPC technique to be implemented, and is still very common in process control especially for Chemical plants. Due to changes in operating points, the implicit model in MPC typically becomes insufficient, costing the quality of the controller. This work proposes Adaptation (based on the recursive least squares (RLS) algorithm), for online system identification to take changes in operating points into account when computing the DMC control action. Overall, Adaptive DMC is investigated as to whether Adaptive DMC is capable of improved quadrotor control or tracking. © 2017

关键词： Model predictive control Altitude control Chemical plants Matrix algebra Predictive control systems Adaptive Control Adaptive model predictive control Dynamic matrix control Forgetting factors In process control Quadrotor controls recursive least square (RLS) recursive least squares algorithms

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Biodynamic Feedthrough: Current Status and Open Issues

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IFAC-PapersOnLine 2016年第19期49卷 120-125页

作者： Venrooij, Joost Olivari, Mario Bülthoff, Heinrich H. Max Planck Institute for Biological Cybernetics Department Human Perception Cognition and Action Tübingen72076 Germany

Biodynamic feedthrough (BDFT) occurs when vehicle accelerations feed through the body of a human operator, causing involuntary limb motions, which in turn result in involuntary control inputs. Manual control of many different vehicles is known to be vulnerable to BDFT effects, such as that of helicopters, aircraft, electric wheelchairs and hydraulic excavators. This paper provides a brief review of BDFT literature, which serves as a basis for identifying the fundamental challenges that remain to be addressed in future BDFT research. One of these challenges, time-variant BDFT identification, is discussed in more detail. Currently, it is often assumed that BDFT dynamics are (quasi)linear and time-invariant. This assumption can only be justified when measuring BDFT under carefully crafted experimental conditions, which are very different from real-world situations. As BDFT dynamics depend on neuromuscular dynamics, they are typically time-varying. This paper investigates the suitability of a recently developed time-variant identification approach, based on a recursive least-squares algorithm, which has been successfully used to identify time-varying neuromuscular dynamics. © 2016

关键词： Electric machine control Cybernetics Dynamics Excavation Excavators Fighter aircraft Wheelchairs Experimental conditions Feed through Identification approach Linear and time invariants Neuromuscular dynamics Real world situations recursive least squares algorithms Time variant

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Adaptive Predictive Control Based Therapy of Bone Marrow Cancer

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IFAC-PapersOnLine 2016年第13期49卷 330-335页

作者： Silva, H.G. Lemos, J.M. Costa, B.A. INESC-ID/Instituto Superior Técnico Universidade de Lisboa Portugal

This paper starts by reviewing the mathematical model for tumor growth as well as the pharmacokinetics and pharmacodynamics models of the drug, so that the therapy can be as close as possible to reality. A Nonlinear Model Predictive Control algorithm (NMPC) is used to find the optimal drug dose, in order to reduce the bone marrow tumor density. The recursive least squares algorithm is used to learn the parameters of the tumor growth model, in order to obtain an adaptive NMPC strategy. This control strategy is applied to a bone microenvironment model to schedule a therapy for reducing tumor density. © 2016

关键词： Model predictive control Adaptive control systems Bone Controlled drug delivery Diseases Drug dosage Nonlinear systems Predictive control systems Tumors Adaptive predictive control Biomedical systems Bone marrow Non linear model Nonlinear model predictive control Pharmacokinetics and pharmacodynamics recursive least squares algorithms Tumor growth modeling

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Nonlinear Time-varying Systems Identification Based on Multi-dimensional Taylor Network and Variable Forgetting Factor recursive least squares Algorithm

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IFAC-PapersOnLine 2015年第11 11期48卷 1103-1107页

作者： Zhang, Jiao-Jun Yan, Hong-Sen School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

A novel method for nonlinear time-varying systems identification based on multi-dimensional Taylor network and variable forgetting factor recursive least squares algorithm is proposed. In this paper, the connection weight coefficients of multi-dimensional Taylor network are regarded as time-varying parameters, which are trained by the variable forgetting factor recursive least squares algorithm, to reflect the input-output change of nonlinear time-varying systems. Simulation results show that the method proposed in this paper is valid. © 2015

关键词： Time varying networks Identification (control systems) least squares approximations Time varying systems Connection weights Input output Multi dimensional Nonlinear time varying systems recursive least squares algorithms Time varying parameter Variable forgetting factors

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Semi-active suspension control with LPV mass adaptation

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IFAC-PapersOnLine 2015年第26期48卷 67-72页

作者： Tudón-Martínez, Juan C. Hernández-Alcántara, Diana Morales-Menendez, Ruben School of Engineering and Technology Universidad de Monterrey Av. I. Morones Prieto 4500 Pte. San Pedro Garza García N.L.66238 Mexico School of Engineering and Sciences Tecnologico de Monterrey Av. E. Garza Sada 2501 Monterrey N.L.64849 Mexico

A novel control system for an automotive semi-active suspension based on a Quarter of Vehicle (QoV) model is proposed. In typical conditions the mass of the vehicle body varies due to several variables, such as the number of passengers, the vehicle load, etc. These variations can lead to undesirable responses even if the controller was adequately tuned using a nominal model. In order to improve this situation a controller which actively adapts to vehicle body mass variations can be used. In this article a control scheme based on the Linear Parameter- Varying (LPV) framework is proposed. This approach considers the nonlinear behavior of the damper and variations on the sprung mass as scheduling parameters. The vehicle body mass is online estimated using the recursive least squares algorithm. Simulations of a QoV model of a pickup truck which includes an experimental magneto-rheological damper model were carried out in order to evaluate the proposed scheme. Results in the frequency and time domain show the effectiveness of the proposed controller compared to an uncontrolled damper and a standard LPV controller without mass adaptation. The comfort is improved over 25% using the proposed scheme when the vehicle body mass changes. © ) 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Suspensions (components) Active suspension systems Automobile bodies Automobile suspensions Controllers Magnetic levitation vehicles Trucks Automotive semi active suspensions Frequency and time domains Linear parameter varying LPV control Magneto rheological dampers recursive least squares algorithms Semi active suspension controls Vehicle mass

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Decomposition-Based recursive least squares Algorithm for Wiener Nonlinear Feedback FIR-MA Systems Using the Filtering Theory

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2014年第11期33卷 3649-3662页

作者： Wang, Ziyun Wang, Yan Ji, Zhicheng Jiangnan Univ Minist Educ Key Lab Adv Proc Control Light Ind Wuxi 214122 Peoples R China

A decomposition-based recursive least squares algorithm is developed for Wiener nonlinear systems described by finite impulse response moving average models. After transferring a finite impulse response moving average (FIR-MA) model to a controlled autoregressive model, we compute the parameters by combining the decomposition principle and the least squares method and using the filtering idea. The simulation results validate the proposed algorithm.

关键词： System identification Wiener model recursive least squares algorithms Filtering theory Key-term decomposition

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