In this paper, we present Asynchronous implementation of Deep Neural Network-based model reference adaptive control (DMRAC). We evaluate this new neuro-adaptivecontrol architecture through flight tests on a small qua...
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The objective of this work is to apply model reference adaptive control (MRAC) using Massachusetts Institute of Technology (MIT) rule and MRAC using Lyapunov method to control the speed of a Direct Current (DC) motor ...
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In this paper, a resetting mechanism is proposed to enhance the transient performance of model reference adaptive control. While the suggested method has a simple structure, it is capable of taking into account both t...
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In this paper, a resetting mechanism is proposed to enhance the transient performance of model reference adaptive control. While the suggested method has a simple structure, it is capable of taking into account both the desired steady-state behavior and the transient response, simultaneously. Whenever the transient specification is not satisfying, there is a jump in the controller parameters. This jump is determined by designing an optimal reset law. At the reset times, the after-reset values of parameters are calculated based on a minimization problem. The considered cost function is a mixed H-2/H criterion, which minimizes the tracking error. The optimization problem is converted to an LMI formulation, and the reset law is designed by solving this LMI at certain reset times. To verify the effectiveness of the proposed approach, simulation results are presented.
Motion control with high accuracy for each axial system is the fundamental requirement to reduce a synchronous motion error of a multi-axis system. Especially, designing a model-based controller for an uncertainty sys...
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Motion control with high accuracy for each axial system is the fundamental requirement to reduce a synchronous motion error of a multi-axis system. Especially, designing a model-based controller for an uncertainty system with unknown parameters is not easy without using system identification. To overcome the mentioned issue, this article proposes a cross-coupling synchronous velocity controller using a backstepping-based model reference adaptive control scheme in an unsymmetrical biaxial winding system called a transformer winding system. The proposed controller deals not only with the uncertainty but also with the recursive structure of the system. The backstepping technique for the recursive structural system and the model reference adaptive control method for the uncertainty of the system are designed to stabilize two axial systems with unknown parameters. An auxiliary system is added to build the proposed controller for coping with input constraints of physical actuators. To improve the proposed controller's ability to cope with external disturbances, a dead-zone modification is utilized to modify the adaptation laws to avoid the drift phenomenon. Moreover, a cross-coupling mechanism is integrated into the proposed controller to reduce the synchronous velocity error between the velocities of the biaxial winding system. The proposed controller is also transformed into discrete time to be run on a digital signal processor alone chip. The experimental results are shown to verify the high performance and efficiency of the proposed controller for practical applications.
A new, high performance, solution to the classical problem of direct model reference adaptive control for linear time-invariant systems with unknown sign of the high frequency gain is reported in the paper. The propos...
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A new, high performance, solution to the classical problem of direct model reference adaptive control for linear time-invariant systems with unknown sign of the high frequency gain is reported in the paper. The proposed algorithm directly estimates this parameter with the only required prior knowledge of a lower bound on its absolute value. To avoid the possible appearance of singularities in the controller calculation a switched projection mechanism is introduced to change, if needed, the sign of the estimate. The recently introduced dynamic regressor extension and mixing estimator is used to ensure monotonicity of the estimation error of the high frequency gain, guaranteeing that the switching appears (at most) once and avoiding the possible appearance of chattering-that may happen in classical gradient-based algorithms. Comparative simulations with the Nussbaum gain-based and gradient estimators illustrate the dramatic performance improvement of the proposed controller. (C) 2018 European control Association. Published by Elsevier Ltd. All rights reserved.
model reference adaptive control (MRAC) offers mathematical and design tools to effectively cope with many challenges of real-world control problems such as exogenous disturbances, system uncertainties and degraded mo...
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model reference adaptive control (MRAC) offers mathematical and design tools to effectively cope with many challenges of real-world control problems such as exogenous disturbances, system uncertainties and degraded modes of operations. On the other hand, when faced with human-in-the-loop settings, these controllers can lead to unstable system trajectories in certain applications. To establish an understanding of stability limitations of MRAC architectures in the presence of humans, here a mathematical framework is developed whereby an MRAC is designed in conjunction with a class of linear human models including human reaction delays. This framework is then used to reveal, through stability analysis tools, the stability limit of the MRAC-human closed-loop system and the range of model parameters respecting this limit. An illustrative numerical example of an adaptive flight control application with a Neal-Smith pilot model is presented to demonstrate the effectiveness of developed approaches.
Designing an adequate controller for a plant with an arbitrary relative degree is still an active area of research. In this paper, a discrete variable structure model reference adaptive control using only input-output...
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Designing an adequate controller for a plant with an arbitrary relative degree is still an active area of research. In this paper, a discrete variable structure model reference adaptive control using only input-output measurements (DVS-MRAC-IO) for not strictly positive real systems with a relative degree of two is proposed. In order to show the effectiveness of the proposed controller, a detailed stability analysis is studied using Lyapunov theory. Further, a straightforward generalization of DVS-MRAC-IO for systems with arbitrary relative degree is presented. Numerical results are used to show the effectiveness of the proposed methods.
This paper is concerned with the problem of H-infinity state tracking model reference adaptive control for switched systems by the multiple Lyapunov functions method. Neither the measurability of the system state nor ...
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This paper is concerned with the problem of H-infinity state tracking model reference adaptive control for switched systems by the multiple Lyapunov functions method. Neither the measurability of the system state nor the solvability of the H-infinity state tracking model reference adaptive control for each individual subsystem is required. First, to improve the transient performance of switched systems, the closed-loop referencemodel is introduced to switched systems. Second, the H-infinity state tracking model reference adaptive control problem for switched systems is solved by designing adaptivecontrollers for subsystems and a switching law. Then, a solvability condition of the H-infinity state tracking model reference adaptive control problem for switched systems is developed. (C) 2017 Elsevier Ltd. All rights reserved.
This study demonstrates the utilization of model reference adaptive control (MRAC) for closedl-oop fractional-order PID (FOPID) control of a magnetic levitation (ML) system. Design specifications of ML transportation ...
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This study demonstrates the utilization of model reference adaptive control (MRAC) for closedl-oop fractional-order PID (FOPID) control of a magnetic levitation (ML) system. Design specifications of ML transportation systems require robust performance in the presence of environmental disturbances. Numerical and experimental results demonstrate that incorporation of MRAC and FOPID control can improve the disturbance rejection control performance of ML systems. The proposed multiloop MRAC-FOPID control structure is composed of two hierarchical loops which are working in conjunction to improve robust control performance of the system in case of disturbances and faults. In this multiloop approach, an inner loop performs a regular closed-loop FOPID control, and the outer loop performs MRAC based on Massachusetts Institute of Technology (MIT) rule. These loops are integrated by means of the input-shaping technique and therefore no modification of any parameter of the existing closed-loop control system is necessary. This property provides a straightforward design solution that allows for independent design of each loop. To implement FOPID control of the ML system, a retuning technique is used which allows transforming an existing PID control loop into an FOPID control loop. This paper presents the simulation and experimental results and discusses possible contributions of multiloop MRAC-FOPID structure to disturbance rejection control of the ML system.
In structural control, appropriate control parameters and a stable closed-loop mechanism are required for a controller to achieve optimal performance, particularly in the presence of uncertain structural parameters un...
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In structural control, appropriate control parameters and a stable closed-loop mechanism are required for a controller to achieve optimal performance, particularly in the presence of uncertain structural parameters under external excitation. In this study, an optimum model reference adaptive control (OMRAC) algorithm combining the Linear quadratic regulator method and model reference adaptive control based on Lyapunov stability is proposed. The OMRAC algorithm is implemented and applied to the response control of a base-isolated structure equipped with magneto-rheological dampers under earthquake excitations. The results from a series of numerical simulations that consider the effects of uncertainty within structural parameters are reported. The performance of the OMRAC algorithm is compared with that of other control algorithms in terms of effectiveness and stability. The results suggest that the proposed OMRAC method can successfully compensate for uncertainties in structural parameters, leading the controlled structure to adaptively track the optimal response of the referencemodel.
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