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Computing stability limits for adaptive control laws with high-order actuator dynamics

AUTOMATICA 2019年 101卷 409-416页

作者： Gruenwald, Benjamin C. Yucelen, Tansel Muse, Jonathan A. Wagner, Daniel Univ S Florida Dept Mech Engn Tampa FL USA Air Force Res Lab Aerosp Syst Directorate Wright Patterson AFB OH USA Czech Tech Univ Dept Elect Engn Prague Czech Republic

A challenge in the design of adaptive control laws for uncertain dynamical systems is to achieve system stability and a prescribed level of command following performance in the presence of actuator dynamics. It is well-known that if the actuator dynamics do not have sufficiently high bandwidth, their presence cannot be practically neglected in the design since they limit the achievable stability of adaptive control laws. In this paper, we consider the design of model reference adaptive control laws for uncertain dynamical systems in the presence of high-order actuator dynamics. Specifically, a linear matrix inequalities based hedging approach is proposed, where this approach modifies the ideal reference model dynamics to allow for correct adaptation that is not affected by the presence of actuator dynamics. The stability of the modified reference model is then computed using linear matrix inequalities, which reveals the fundamental stability interplay between the parameters of the actuator dynamics and the allowable system uncertainties. In addition, we analyze the convergence properties of the modified reference model to the ideal reference model. The presented theoretical results are finally illustrated through a numerical example. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： model reference adaptive control Uncertain dynamical systems High-order actuator dynamics System stability Command following performance Linear matrix inequalities Hedging approach

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model reference adaptive control of five-phase IPM Motors based on neural network

Model reference adaptive control of five-phase IPM Motors ba...

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IEEE International Electric Machines and Drives Conference

作者： Guo, Lusu Parsa, Leila Rensselaer Polytechnic Institute United States

ISBN: (纸本)9781457700613

This paper presents a novel model reference adaptive control (MRAC) of five-phase interior permanent magnet (IPM) motor drives. The primary controller is designed based on artificial neural network (ANN) to simulate the nonlinear characteristics of the system without knowledge of accurate motor model or parameters. The proposed motor drive decouples the torque and flux components of five-phase IPM motors by applying multiple reference frame transformation. Therefore, the motor can be easily driven below the rated speed with the maximum torque per ampere (MTPA) operation or above the rated speed with the flux weakening operation. The ANN based primary controller consists of a radial basis function (RBF) network which is trained on-line to adapt system uncertainties. The complete IPM motor drive is simulated in Matlab/Simulink environment and implemented experimentally utilizing dSPACE DS1104 DSP board on a five-phase prototype IPM motor. © 2011 IEEE.

关键词： model reference adaptive control

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model reference adaptive control for constrained linear systems

Model reference adaptive control for constrained linear syst...

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31st IASTED International Conference on modelling, Identification, and control, MIC 2011

作者： Tu, Pham Anh Uchida, Kenko Department of Electrical Engineering and Bioscience Waseda Universi Tokyo Japan

ISBN: (纸本)9780889868632

The presence of state and control constraints in physi cal systems may lead to performance deterioration and instability if not adequately accounted for in controller design processes. Therefore, a procedure of state and control constraint fulfillment for uncertain linear systems, which firstly require an adaptive controller, is proposed. model reference adaptive control (MRAC) Method is selected to design the controller, and Lyapunov stability theory is used to obtained adaptive laws. To solve the constraint fulfillment problem, a concept of maximal output admissible set [8], [11] is utilized to predict safe operation of the plant. Constraint fulfill ment procedure is described as polynomial optimization problems, and a numerical design example is carried out to support the arguments.

关键词： model reference adaptive control

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Downshift decision and process optimal control of dual clutch transmission for hybrid electric vehicles under rapid braking condition

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2019年 116卷 943-962页

作者： Zhao, Zhiguo Chen, Jiayi Li, Xueyan Lei, Dan Tongji Univ Clean Energy Automot Engn Ctr Shanghai 201804 Peoples R China Tongji Univ Sch Automot Studies Shanghai 201804 Peoples R China

Hybrid electric vehicles (HEVs) equipped with dual clutch transmission (DCT) can benefit from engine auxiliary braking force and driving motor regenerative braking force in braking conditions. This can effectively reduce the workload of hydraulic mechanical braking systems and recovery braking energy, which will improve the fuel economy. In the downshift process, the driving motor is involved to response the change of torque/speed demand, which can further improve the shift quality of DCT without affecting the engine emissions and fuel economy. The combined braking condition of HEVs with DCT allows for the variation of power distribution between the front and rear axle, as well as the electric and hydraulic braking force. In this study, the downshift decision strategy is designed based on the two-parameter shift schedule, while considering the driver's subjective driving intention. Subsequently, the mathematical model developed to investigate the downshift process of HEVs with dual clutch transmission has been applied to skip downshift process, to analyze the control sequence and implement different control algorithms according to respective control objects. Specifically, the sliding mode variable structure optimization control algorithm is used to allow the engine speed to track the target value in the torque phase. In addition, the linear quadratic tracking controller and the model reference adaptive system are used, based on adjustable parameters, to realize the speed synchronization of the clutch driving and driven plate in the inertial phase. According to the results of electro-hydraulic braking force distribution, the braking forces of an integrated starter generator (ISG) motor and hydraulic brake system are controlled to switch to the demand level. Then, the simulation model established on a Matlab/Simulink platform is used to validate the skip downshift control strategy. Finally, the designed control strategies are verified on the test bench. The test resul

关键词： Hybrid electric vehicle with dual clutch transmission Composite braking downshift process Linear quadratic tracking control model reference adaptive control Hardware in-loop test

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ANALYSIS OF FRACTIONAL ORDER ERROR modelS IN adaptive SYSTEMS: MIXED ORDER CASES

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FRACTIONAL CALCULUS AND APPLIED ANALYSIS 2019年第4期22卷 1113-1132页

作者： Aguila-Camacho, N. Gallegos, J. Duarte-Mermoud, M. A. Univ Tecnol Metropolitana Fac Engn Dept Elect Av Jose Pedro Alessandri 1242 Santiago Chile Univ Chile Dept Elect Engn Av Tupper 2007 Santiago Chile

This paper presents the properties of two fractional order error models (FOEM) that arise in the analysis of fractional order adaptive systems (FOAS). Cases where the fractional order is the same for every equation in these two FOEM were analyzed in a previous work, obtaining useful results. However, those cases where the fractional orders are different in the equations of FOEM (mixed order cases) have not been addressed before. This paper treats the analysis of some of these mixed order cases, proving boundedness of all the signals and convergence to zero of the mean value of the squared norm of the output error. A model reference adaptive control (MRAC) scheme for an integer order plant using fractional adaptive laws for estimating the controller parameters is presented at the end of the paper, which shows the applicability and importance of the proposed results.

关键词： stability adaptive systems simulation model reference adaptive control fractional systems mixed order error models

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Interval Prediction models for Data-Driven Design of Aerial Vehicle's Robust adaptive controllers

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JOURNAL OF GUIDANCE control AND DYNAMICS 2020年第8期43卷 1512-1528页

作者： Fravolini, Mario Luca Costante, Gabriele Yucelen, Tansel Napolitano, Marcello Rosario Univ Perugia Dept Engn I-06125 Perugia Italy Univ S Florida Dept Mech Engn Tampa FL 33620 USA West Virginia Univ Dept Mech & Aerosp Engn Morgantown WV 26506 USA

An important issue in robust adaptive control is the estimation of realistic ranges for the parametric uncertainty to be used in the projection mechanism with the goal of guaranteeing the boundedness of the adaptive parameters in the presence of modeling uncertainty. To facilitate this design it is common practice to assume conservative parameters' projection bounds and to use high-gain adaptation for reducing tracking errors. However, this approach may not be suitable in practical applications because it might potentially lead to unpredictable transients and peaking phenomena. Therefore, the determination of tighter uncertainty upper bounds is a fundamental problem toward the design of safe adaptive controllers. This study shows that the problem of estimating the parametric uncertainty domain of a linearly parameterized adaptive controller can be advantageously formulated as a linear interval prediction model identification problem. On this basis a practical data-driven method to compute tight upper bounds for the uncertain parameters is proposed through the solution of a convex constrained optimization problem expressed in terms of linear matrix inequalities. The proposed method has been applied to the design of a longitudinal and a lateral directional model reference adaptive control for a fleet of YF-22 research unmanned aerial vehicles based on real flight data.

关键词： model reference adaptive control UAV Flight Data Elevator Deflection Ailerons True Airspeed Convex Optimization Computing Aerospace System Sideslip Angle

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model reference Neural-Fuzzy adaptive control of the Concentration in a Chemical Reactor (CSTR)

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IFAC-PapersOnLine 2016年第29期49卷 158-162页

作者： Bahita, M. Belarbi, K. Faculty of Process engineering University of Constantine 3 Constantine25000 Algeria Constantine 3 University Ecole Nationale Polytechnique de Constantine Constantine25000 Algeria

In this work, we consider the application of a special type of neural networks based on radial basis functions (RBF) which belongs to a class of associative memory neural networks. The RBF neural network is used in a novel approach as a controller in a model reference adaptive control (MRAC) architecture. The global control scheme is based on a one step ahead fuzzy model and the objective is to control a Continuous Stared Tank Reactor (CSTR) complex nonlinear system. © 2016

关键词： model reference adaptive control Associative processing Associative storage Identification (control systems) Memory architecture Radial basis function networks Associative memory Associative memory neural networks Complex nonlinear system CSTR Fuzzy modeling MRAC Radial Basis Function(RBF) RBF Neural Network

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Nose Landing Gear Simple adaptive Shimmy Suppression System

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JOURNAL OF GUIDANCE control AND DYNAMICS 2020年第7期43卷 1298-1312页

作者： Orlando, Calogero Kore Univ Enna Fac Engn & Architecture Aerosp Syst I-94100 Enna Italy

The occurrence of shimmy vibration in aircraft nose landing gear can cause an increase of fatigue stress on the nose landing gear mechanical elements that can eventually lead to high wearing of components as well as to severe damage of the leg, giving raise to safety issues. The use of the Modified Simple adaptive controller is investigated for the realization of an adaptive shimmy suppression system. The nonlinear governing equations that model the shimmy vibration behavior of an aircraft nose landing gear are first linearized in order to synthesize the adaptive controller, while nonlinear terms are then taken into account as disturbances. The almost strict passivity of the plant is obtained by using a parallel feedforward compensator. The parallel feedforward compensator is designed as the inverse of a stabilizing controller while the Modified Simple adaptive controller is tuned by considering the minimization of some dynamic performance parameters as design requirements. The influence of the advancing speed on the controlled plant response is studied by simulating scenarios of aeronautical interests.

关键词： Nose Landing Gear model reference adaptive control Full State Feedback Flight control System control Moment Gyroscope Mathematical models State Dependent Riccati Equation Singular Value Decomposition Single Input Single Output Numerical Simulation

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A NOTE ON THE model reference adaptive control OF LINEAR PARABOLIC SYSTEMS WITH CONSTANT COEFFICIENTS

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Journal of Systems Science & Complexity 2011年第6期24卷 1110-1117页

作者： Chaohua JIA School of Mathematics and Systems Science Beihang University Beijing 100191 China LMIB of the Ministry of Education Beijing 100191 China

The problem of constructing a model reference adaptive control law for an uncertain 1- dimensional parabolic system with one constant coefficient is considered in this paper. adaptive control law are obtained by Lyapunov redesign method. The energy method for parabolic systems and the Agmon＇s inequality are applied in the analysis, which leads to a stronger result than that of Hong and Bentsman （Automatica, 1994）.

关键词： adaptive law distributed parameter systems model reference adaptive control parabolic system.

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Robust adaptive control of a Multirotor with an Unknown Suspended Payload

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IFAC-PapersOnLine 2020年第2期53卷 9432-9439页

作者： A.P. Erasmus H.W. Jordaan Electrical and Electronic Engineering Department Stellenbosch University South Africa

This paper addresses the problem of a multirotor carrying an unknown suspended payload, allowed to swing in one axis. The payload is unknown in the sense that its parameters, such as the payload mass and cable length, are unknown and its state, the swing angles, are not available for measurement. The suspended payload alters the flight dynamics of the vehicle considerably and the flight controllers need to minimize this effect. A robust model reference adaptive control technique is designed and implemented to minimize the effect of the suspended payload on the vehicle while allowing the controller to adapt to account for the unknown payload. The controller is modified to reject external disturbances and to be robust in the presence of sensor noise and drift. In simulation, this technique proves to dampen the oscillations caused by the payload. A quadrotor was built to practically demonstrate the effectiveness of the controller. The PX4 flight control stack is considered for the firmware of the vehicle. The model reference adaptive controller was implemented and succeeded to dampen the oscillations caused by the payload in a practical flight.

关键词： Learning adaptation in autonomous vehicles Multirotor UAV Suspended payload model reference adaptive control Robust

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