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NASA Technical Reports Server (Ntrs) 19910013016: control Effort Associated with model reference adaptive control for Vibration Damping

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2017年

NASA Technical Reports Server (Ntrs) 19910013016: control Effort Associated with model reference adaptive control for Vibration Damping by NASA Technical Reports Server (Ntrs); published by

关键词： (ntrs) 19910013016: adaptive associated control control systems design control theory error analysis errors haftka, raphael t. integrals large space structures messer, richard scott model reference adaptive control nasa technical reports server (ntrs) spacecraft control vibration damping

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Computing actuator bandwidth limits for model reference adaptive control

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INTERNATIONAL JOURNAL OF control 2016年第12期89卷 2434-2452页

作者： Gruenwald, Benjamin C. Wagner, Daniel Yucelen, Tansel Muse, Jonathan A. Missouri Univ Sci & Technol Dept Mech & Aerosp Engn Rolla MO 65409 USA Air Force Res Lab Dayton OH USA

Although model reference adaptive control theory has been used in numerous applications to achieve system performance without excessive reliance on dynamical system models, the presence of actuator dynamics can seriously limit the stability and the achievable performance of adaptive controllers. In this paper, a linear matrix inequalities-based hedging approach is developed and evaluated for model reference adaptive control of uncertain dynamical systems in the presence of actuator dynamics. The hedging method modifies the ideal reference model dynamics in order to allow correct adaptation that is not affected by the presence of actuator dynamics. Specifically, we first generalise the hedging approach to cover a variety of cases in which actuator output and the control effectiveness matrix of the uncertain dynamical system are known and unknown. We then show the stability of the closed-loop dynamical system using Lyapunov-based stability analysis tools and propose a linear matrix inequality-based framework for the computation of the minimum allowable actuator bandwidth limits such that the closed-loop dynamical system remains stable. Finally, an illustrative numerical example is provided to demonstrate the efficacy of the proposed approach.

关键词： model reference adaptive control uncertain dynamical systems actuator dynamics hedging approach linear matrix inequalities

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Robust control of robotic manipulators based on left inverse system and variable structure model reference adaptive control

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INTERNATIONAL JOURNAL OF adaptive control AND SIGNAL PROCESSING 2016年第8-10期30卷 1389-1407页

作者： Dias, Samaherni Queiroz, Kurios Araujo, Aldayr Dias, Anfranserai Fed Univ Rio Grande do Norte UFRN Dept Elect Engn LACI Natal RN Brazil State Univ Feira de Santana Lab Control & Robot Syst Feira De Santana Brazil

This work proposes a control structure to be applied to robotic manipulators, which are articulated mechanical systems composed of links connected by joints. The proposed controller can be divided into two parts. The first one is a left inverse system, which is used to decouple the dynamic behavior of the joints. The second is a sliding mode controller, which is applied for each decoupled joint. It is important to note that the proposed structure, using only input/output measurements, reduces the control signal 'chattering', and it is robust to parametric uncertainties. Besides all the characteristics presented, the proposed structure simplifies the design of sliding mode controller to be applied in robotic manipulators. All these features are verified by simulations. Copyright (C) 2016 John Wiley & Sons, Ltd.

关键词： robust control model reference adaptive control variable structure control decoupling subsystems

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Efficient speed control of induction motor using RBF based model reference adaptive control method

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AUTOMATIKA 2016年第3期57卷 714-723页

作者： Kilic, Erdal Ozcalik, Hasan Riza Yilmaz, Saban Kahramanmaras Sutcu Imam Univ Fac Engn & Architecture Elect & Elect Engn Dept Kahramanmaras Turkey

This paper proposes a model reference adaptive speed controller based on artificial neural network for induction motor drives. The performance of traditional feedback controllers has been insufficient in speed control of induction motors due to nonlinear structure of the system, changing environmental conditions, and disturbance input effects. A successful speed control of induction motor requires a nonlinear control system. On the other hand, in recent years, it has been demonstrated that artificial intelligence based control methods were much more successful in the nonlinear system control applications. In this work, it has been developed an intelligent controller for induction motor speed control with combination of radial basis function type neural network (RBF) and model reference adaptive control (MRAC) strategy. RBF is utilized to adaptively compensate the unknown nonlinearity in the control system. The indirect field-oriented control (IFOC) technique and space vector pulse width modulation (SVPWM) methods which are widespread used in high performance induction motor drives has been preferred for drive method. In order to demonstrate the reliability of the control technique, the proposed adaptive controller has been tested under different operating conditions and compared performance of conventional PI controller. The results show that the proposed controller has got a clear superiority to the conventional linear controllers.

关键词： Induction motor neural network model reference adaptive control vector control

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A model reference adaptive control scheme with partial-state feedback for output tracking

A model reference adaptive control scheme with partial-state...

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American control Conference

作者： Ge Song Gang Tao Department of Electrical and Computer Engineering University of Virginia Charlottesville VA 22904 USA

ISBN: (纸本)9781509045839

This paper develops a new partial-state feedback model reference adaptive control (MRAC) scheme for output tracking which combines the advantages of the existing MRAC schemes. For partial-state feedback MRAC, the plant-model matching is as achievable as full-state feedback control, while the controller structure enjoys less complexity as compared with output feedback MRAC. Partial-state feedback MRAC ensures closed-loop system stability and asymptotic output tracking. New results are presented for plant-model matching, adaptive law and stability properties. New features of partial-state feedback MRAC are addressed, including its design flexibility. Simulation results verify the effectiveness of partial-state feedback MRAC.

关键词： Feedback Output Tracking model reference adaptive control Feedback State feedback Output tracking System stability

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Robust kernel-based model reference adaptive control for unstable aircraft

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ADVANCES IN MECHANICAL ENGINEERING 2016年第11期8卷 1-12页

作者： Yang, Zhao-Xu Zhao, Guang-She Bao, Rong-Jing Rong, Hai-Jun Gao, Lei-Tao Xi An Jiao Tong Univ Sch Aerosp State Key Lab Strength & Vibrat Mech Struct 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Elect & Informat Engn Dept Automat Sci & Technol Xian Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Sch Elect & Informat Engn Xian Peoples R China

In this article, a robust kernel-based model reference adaptive control is proposed for an unstable nonlinear aircraft. The heart of the proposed kernel-based model reference adaptive control scheme comprises an offline neural identifier and an online neural controller. In the offline neural identifier, the kernel-based unified extreme learning machine algorithm is used to identify the aircraft model with the available input-output data in a finite time interval. The finite time interval is selected to avoid the response of the unstable aircraft growing unbounded. In the kernel-based unified extreme learning machine, the hidden layer feature mapping is determined by the kernel matrix. However, the unified extreme learning machine is a batch learning algorithm and is not suitable for the online control learning. To solve the problem, a recursive version of the unified extreme learning machine is developed in this study. Based on a given reference model and the identified model, the recursive version of the unified extreme learning machine algorithm is applied to construct the online control law to compensate for the changes in the aircraft dynamics or characteristics. The performance of the proposed kernel-based model reference adaptive control scheme is validated through the simulation studies of a locally nonlinear longitudinal high-performance aircraft. Simulation studies are also compared with a model reference adaptive control based on the back-propagation algorithm and a model reference adaptive control based on the basic extreme learning machine algorithm in terms of the identification and tracking abilities. The results show that the proposed kernel-based model reference adaptive control can achieve better identification and tracking performance.

关键词： Unified extreme learning machine kernel-based recursive model reference adaptive control unstable aircraft

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Cross-Coupling Synchronous Velocity control for an Uncertain model of Transformer Winding System Using model reference adaptive control Method

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AETA International Conference

作者： Van Tu Duong Jeong, Jae Hoon Jeong, Nam Soo Shin, Min Saeng Tan Tien Nguyen Byun, Gi Sig Kim, Sang Bong Pukyong Natl Univ Dept Mech Design Engn Coll Engn Busan 608739 South Korea Dongwon Inst Sci & Technol Dept Shipbldg & Ocean Engn Yangsan 626740 South Korea Pukyong Natl Univ Dept Control & Instrumentat Engn Coll Engn Busan 608737 South Korea Ho Chi Minh City Univ Technol Fac Mech Engn Ho Chi Minh Vietnam

ISBN: (纸本)9783319272474;9783319272450

This paper proposes cross-coupling synchronous velocity control based on model reference adaptive control (MRAC) method for an uncertain model of transformer winding system with two non-symmetrical axial systems such as a winding spindle system and a nozzle feed drive system. Since it is difficult to achieve the physical parameters in modeling of the transformer winding system, MRAC is used to ensure the stability of the transformer winding system. In order to minimize the synchronous velocity error between two axial systems, a cross-coupling synchronous control is employed. Accordingly, the velocity error of the winding spindle system is reflected to the nozzle feed drive system and vice versa. Simulation and experimental results show that the proposed controller which is applied to the transformer winding system with uncertain parameters can reduce the synchronous velocity error between two axial systems.

关键词： model reference adaptive control Cross-coupling Synchronous speed control Winding system

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Basis Integral Concurrent Learning model reference adaptive control

Basis Integral Concurrent Learning Model Reference Adaptive ...

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European control Conference (ECC)

作者： Cho, Namhoon Kim, Youdan Seoul Natl Univ Inst Adv Aerosp Technol Dept Mech & Aerosp Engn Seoul 151744 South Korea

ISBN: (纸本)9781509025916

A modified concurrent learning model reference adaptive control method is proposed to guarantee the global convergence of parameter estimation error without assuming perfect knowledge on the state derivative. The value of the basis function vector is stored and manipulated in the original concurrent learning model reference adaptive control scheme. But in the proposed method, the time integral of the basis function vector is used instead of the basis itself to construct the history-based adaptation signal. By this modification, a smoother or an observer to get the state derivative estimate is not required in the proposed method unlike the original scheme. Therefore, the implementation of the proposed method is simpler than the previous one.

关键词： model reference adaptive control

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Fractional model reference adaptive control for Electro-Hydraulic Servo System 7

Fractional Model Reference Adaptive Control for Electro-Hydr...

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IEEE Chinese Guidance, Navigation and control Conference (CGNCC)

作者： Zhao, Yifei Jiao, Zongxia Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

ISBN: (纸本)9781467383189

The precise position control for electro-hydraulic servo system (EHSS) is a key technical problem for modern industry control. This paper presents a model reference adaptive control (MRAC) strategy combining fractional order calculus theory and neural network to solve the high nonlinearity and parameter uncertainties in the EHSS. The reference model in the MRAC strategy is a fractional second-order model and the controller with adaptive law is designed by a Wiener recurrent neural network (WRNN). Simulations indicate the validity and superiority of the proposed fractional MRAC strategy.

关键词： model reference adaptive control

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Predictor-Based model reference adaptive control of an Unmanned Underwater Vehicle 14

Predictor-Based Model Reference Adaptive Control of an Unman...

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14th International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Makavita, C. D. Nguyen, H. D. Jayasinghe, S. G. Ranmuthugala, D. Univ Tasmania Australian Maritime Coll Hobart Tas 7250 Australia

ISBN: (纸本)9781509035496

Unmanned Underwater Vehicles (UUVs) are being deployed in advanced applications that require precise manoeuvring close to complex underwater structures such as oilrigs and subsea installations or moving objects such as ships and submarines. The effect of vehicle's hydrodynamic parameter variations is significant in such scenarios and in extreme conditions the UUV may experience loss of control. In addition, external disturbances and actuator failures degrade the performance of the UUV. adaptive control has been identified as a promising solution that can improve the performance in such situations. However, adaptive control is not widely used in UUVs mainly due to the trade-off between fast learning and smooth control signals. The latter can be guaranteed at low learning rates but require additional input to improve learning. The Predictor model reference adaptive control (PMRAC) is one such method that uses a prediction error to improve learning. In this paper, the performance of PMRAC in UUV applications is investigated and compared to standard model reference adaptive control (MRAC) at low learning rates under normal operational conditions, partial actuator failure, and under the influence of external disturbances. Simulation results show that PMRAC significantly reduces the tracking error compared to MRAC. In addition, PMRAC is less affected and recovers quickly from actuator failure and external disturbances, while generating smooth control signals with less oscillation compared to MRAC.

关键词： actuator failure external disturbances model reference adaptive control state predictor underwater vehicles

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