The utilization of floating offshore wind turbines is regarded as a promising solution for offshore renewables, wherein blade pitch control plays a crucial role in both operation and maintenance. Among the various bla...
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The utilization of floating offshore wind turbines is regarded as a promising solution for offshore renewables, wherein blade pitch control plays a crucial role in both operation and maintenance. Among the various blade pitch control methods, strategies that involve more states pertaining to the control objectives demonstrate superior tracking performance. However, these approaches necessitate additional linearization and parameter tuning efforts, particularly when addressing varying operating conditions in real-world unsteady environments. In this study, a decoupled model reference adaptive control framework with inner control laws for ideal responses for both collective and individual blade pitch is employed, in the context of floating offshore wind turbine control problems during operation beyond rated conditions. According to the simulations conducted on a benchmark wind turbine simulator under a series of turbulent wind speeds, the proposed integrated controller achieved reductions in power fluctuation and damage equivalent load in blades compared to corresponding baseline controllers, at the expense of acceptable deterioration in tower base loads and blade actuator activities. The proposed scheme demonstrates its adaptability in serving the entire operating range, mitigating the need for parameter tuning due to variations in wind speed, and exhibits potential to involve more control purposes.
The design of a fractional order model reference adaptive control for anesthesia based on a fractional order model is proposed in the paper. This model gets around many difficulties in controller designs based on the ...
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The design of a fractional order model reference adaptive control for anesthesia based on a fractional order model is proposed in the paper. This model gets around many difficulties in controller designs based on the pharmacokinetic/pharmacodynamic model, commonly used for anesthesia for theses purposes, and allows to design a simple adaptivecontroller with stability and positivity of the system ensured via Lyapunov analysis. Also, the convergence of the tracking error to zero is established by applying an extension of the Barbalat lemma, proven in the paper. Simulations illustrate the effectiveness and robustness of the proposed control.
This paper is devoted to the design and analysis of a predictor-based model reference adaptive control. Stable adaptive laws are derived using Lyapunov framework. The proposed architecture is compared with the now-cla...
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This paper is devoted to the design and analysis of a predictor-based model reference adaptive control. Stable adaptive laws are derived using Lyapunov framework. The proposed architecture is compared with the now-classical model reference adaptive control. A simulation example is presented in which numerical evidence indicates that the proposed controller yields improved transient characteristics.
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.
This paper presents a predictive optimization-based model reference adaptive control (MRAC) approach for dynamic positioning (DP) of a fully actuated underwater vehicle subject to dynamic uncertainties and actuator sa...
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This paper presents a predictive optimization-based model reference adaptive control (MRAC) approach for dynamic positioning (DP) of a fully actuated underwater vehicle subject to dynamic uncertainties and actuator saturation. Compared with conventional linear referencemodel-based approaches, this proposed MRAC controller utilizes an optimized referencemodel composed of the closed-loop approximate vehicle model under a nonlinear model predictive controller, in which both the state and input constraints are considered. An adaptive dynamic inversion controller is designed to track the reference trajectory in the presence of dynamic uncertainties, and a single hidden layer neural network is incorporated to compensate for the mismatch of the actual and approximate models and ensure the convergence of tracking errors. The effectiveness of the proposed DP approach is validated by comparative simulations performed with a remotely operated vehicle.
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.
A nonlinear adaptivecontrol strategy based on radial. basis function networks and principal component analysis is presented. The proposed method is well suited for low dimensional nonlinear systems that are difficult...
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A nonlinear adaptivecontrol strategy based on radial. basis function networks and principal component analysis is presented. The proposed method is well suited for low dimensional nonlinear systems that are difficult to model and control via conventional means. The effective system dimension is reduced by applying nonlinear principal component analysis to state variable data obtained from open-loop tests. This allows the radial basis functions to be placed in a lower dimensional space than the original state space. The total number of basis functions is specified a priori, and an algorithm which adjusts the location of the basis function centers to surround the current operating point is presented. The basis function weights are adapted on-line such that the plant output asymptotically tracks a linear referencemodel. A highly nonlinear polymerization reactor is used to compare the nonlinear adaptivecontroller to a linear state feedback controller that utilizes the same amount of plant information. (C) 2000 Elsevier Science Ltd. All rights reserved.
A non-quadratic Lyapunov function and new adaptive laws for model reference adaptive control are presented. These adaptive laws use a new signal. the cubr of the system output error signal e(3), unlike the ordinary sy...
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A non-quadratic Lyapunov function and new adaptive laws for model reference adaptive control are presented. These adaptive laws use a new signal. the cubr of the system output error signal e(3), unlike the ordinary system output error e. Their performance in an adaptive system is promising. Simulation results show that these adaptive laws yield improved performance, along with the assurance of Lyapunov stability.
Existence of unknown time-delay in the systems is a drastic restriction that it can menace the stability criteria and even deteriorate the performance system. This undesired case would be more intensified if that the ...
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Existence of unknown time-delay in the systems is a drastic restriction that it can menace the stability criteria and even deteriorate the performance system. This undesired case would be more intensified if that the uncertain input nonlinearity effects are also considered. To handle the input nonlinearities effects (results in dead-zone and/or hysteresis phenomena) and also unknown time-delay in the chaotic systems, this paper presents an observer-based model reference adaptive control (MRAC) scheme for a class of unknown time-delay chaotic systems with disturbances. This new method is a delay-independent variable-structure control method which is integrated with an observer system. The main task of the proposed approach is to accomplish a perfect tracking procedure such that unknown parameters are adapted via output estimation error. Furthermore, stability of the closed-loop system is achieved by means of the Lyapunov stability theory. Finally, the proposed methods are applied to some famous chaotic systems to verify the effectiveness of the proposed methods.
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