In this work, we propose a new sliding mode controller based on a referencemodel for controlling data transmission rates in a connection-oriented communication network. In the proposed approach, we build a model of t...
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In this work, we propose a new sliding mode controller based on a referencemodel for controlling data transmission rates in a connection-oriented communication network. In the proposed approach, we build a model of the network, which is controlled by a linear quadratic (LQ)optimal controller. Then, the sliding variable of the real network is forced to follow the reference sliding variable generated by this model. This method enables us to preserve the favorable properties of the optimal controller, such as reducing and smoothing out the initial flow rates. Moreover, once the referencemodel attains the vicinity of the desired state, the designed sliding mode controller is able to react more rapidly to changes in the available bandwidth, ensuring better robustness. Due to this fact, the considered controller can ensure full bottleneck link bandwidth utilization with smaller memory buffers than theLQoptimal one. This important advantage is shown both analytically, and in computer simulations
The Proton Exchange Membrane Fuel Cell (PEMFC) is one of the most important power supplies. Maintaining a constant voltage in PEM fuel cells has always attracted the attention of many researchers and many articles hav...
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The Proton Exchange Membrane Fuel Cell (PEMFC) is one of the most important power supplies. Maintaining a constant voltage in PEM fuel cells has always attracted the attention of many researchers and many articles have been published on this issue. This paper presents a transfer function model of a PEM fuel cell. Subsequently, model reference control (MRC) strategy is proposed to fix fuel cell voltage in presence of noise and disturbance. The model and the controller are implemented in the MATLAB and SIMULINK environment and results are compared with a PID controller.
This paper proposes a modelreference based adaptive sliding mode controller for Maglev system. An adaptive sliding control scheme is designed to achieve the robustness without knowing the limit of upper bound of pert...
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ISBN:
(纸本)9781509044269
This paper proposes a modelreference based adaptive sliding mode controller for Maglev system. An adaptive sliding control scheme is designed to achieve the robustness without knowing the limit of upper bound of perturbation. With the use of adaptation law, the unknown bound of perturbation has been successfully adapted to design the adaptive sliding mode control scheme. An additional degree of freedom is achieved with the use of additional integral term in sliding surface improving steady state error. The mathematical analysis and simulation results have been given to shows the effectiveness of proposed theory.
The purpose of this work is to design a Sliding-mode control based in modelreference (SMC-MR) applied to a nonlinear ball and plate system with time delay. This approach consists in a controller that acts on a simple...
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ISBN:
(纸本)9781538603987
The purpose of this work is to design a Sliding-mode control based in modelreference (SMC-MR) applied to a nonlinear ball and plate system with time delay. This approach consists in a controller that acts on a simple estimated model (similar to the real process), which is called a referencemodel, and taking as reference the output of that system, a second controller is designed to act on the real process. Then, the controller will cause the real system to follow the referencemodel and be sufficiently robust in the presence of time delay and modeling errors. A performance analysis of the controller is done by simulation considering the characteristics of a real plant.
In this conference paper, a referencemodel sliding mode controller is developed. The switching variable of the actual system is made to track the one outputted by the plant dynamical model. This mathematical model, i...
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ISBN:
(纸本)9781728198095
In this conference paper, a referencemodel sliding mode controller is developed. The switching variable of the actual system is made to track the one outputted by the plant dynamical model. This mathematical model, in turn, is steered by a sub-optimal control law. This control law is a constant output signal during the beginning v time steps. The value and duration of this signal are determined by minimizing a "traditional" quadratic control quality criterion. An inventory management system is presented as an example application of the presented control methodology. It is demonstrated, that the new control method has advantages over a similar one, in which the referencemodel is controlled by an optimal controller. These advantages are: requiring smaller warehouse capacity and providing newer (i.e. spending less time in the warehouse) stock to the consumer.
In this paper, we consider data driven control of Hammerstein systems. For such systems a common control structure is a transfer function followed by a static output nonlinearity that tries to cancel the input nonline...
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In this paper, we consider data driven control of Hammerstein systems. For such systems a common control structure is a transfer function followed by a static output nonlinearity that tries to cancel the input nonlinearity of the system, which is modeled as a polynomial or piece-wise linear function. The linear part of the controller is used to achieve desired disturbance rejection and tracking properties. To design a linear part of the controller, we propose a weighted average risk criterion with the risk being the average of the squared L 2 tracking error. Here the average is with respect to the observations used in the controller and the weighting is with respect to how important it is to have good control for different impulse responses. This criterion corresponds to the average risk criterion leading to the Bayes estimator and we therefore call this approach Bayes control. By parametrizing the weighting function and estimating the corresponding hyperparameters we tune the weighting function to the information regarding the true impulse response contained in the data set available to the user for the control design. The numerical results show that the proposed methods result in stable controllers with performance comparable to the optimal controller, designed using the true input nonlinearity and true plant.
In this paper we propose a new modelreference approach to reaching law based sliding mode control of discrete-time systems. The new approach significantly improves perfor-mance of the controlled plant by eliminating ...
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In this paper we propose a new modelreference approach to reaching law based sliding mode control of discrete-time systems. The new approach significantly improves perfor-mance of the controlled plant by eliminating the persistent effect of past uncertainties on its sliding motion. It involves obtaining a desired state trajectory with the use of a referencemodel of the system and the reaching law approach, and then driving the state of the original plant alongside that trajectory. As a result, sliding motion of the system is not distorted by past uncertainties, and state trajectory more precisely follows the desired one. Furthermore, the proposed modelreference approach allows one to apply strategies with arbitrary relative degree sliding variables to the referencemodel and successfully carry over the properties of these strategies to the original plant, even in the presence of non-matched uncertainties. This is an important advantage since such strategies without the referencemodel, despite their robustness with respect to matched disturbance, cannot typically be used when matching conditions are not satisfied. In particular, we have considered two reaching laws for the proposed modelreference scheme, using relative degree one and two sliding variables, respectively. In both cases we have shown that system sliding motion is only affected by the single most recent disturbance, which results in increased robustness of the plant when compared to traditional reaching law approach. (c) 2020 Elsevier Ltd. All rights reserved.
In this study, a modelreference adaptive state-dependent Riccati equation (MRASDRE) is proposed as a maximum power point tracking (MPPT) controller to extract the maximum power from a photovoltaic (PV) array mounted ...
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In this study, a modelreference adaptive state-dependent Riccati equation (MRASDRE) is proposed as a maximum power point tracking (MPPT) controller to extract the maximum power from a photovoltaic (PV) array mounted on an orbiting satellite. In the structure of the proposed MPPT, a fuzzy logic controller (FLC) is used to estimate the reference voltage of the PV array at the maximum power point (MPP), and an adaptive mechanism is used to compensate for the difference between the PV system and the referencemodel. The modelreference generates the duty cycle for damping the oscillations of the DC-DC converter and tracking the MPP. A Lyapunov stability theory is used to obtain an adaption law to tune the gains of the adaptive mechanism. Hence, the stability of the closed system can be guaranteed, and the maximum power point (MPP) can be captured. To evaluate the performance of the proposed MPPT used in an orbiting satellite, the outer space environment is simulated. Also, an electrical power supply (EPS) is proposed to manage the generation and consumption of satellites. The efficiency of the proposed MPPT is verified in the simulation tests.
Our study introduces a new modelreference based approach to the design of sliding mode controller for discrete-time dynamical systems subject to external disturbances. We propose to begin the control design with gene...
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Our study introduces a new modelreference based approach to the design of sliding mode controller for discrete-time dynamical systems subject to external disturbances. We propose to begin the control design with generation of the reference trajectory for the system using its mathematical model and a hyperbolic tangent based sliding mode reaching law. Next, for the real disturbed plant, we propose a reaching function, which follows the reference trajectory in each step. Further, we prove that this approach ensures existence of quasi-sliding motion according to the definition of Gao et al. Moreover, the proposed controller offers a significant reduction of the width of the achieved quasi-sliding mode band in comparison to other sliding mode methodologies, which results in an improvement of the system's robustness. The properties of our control scheme are finally illustrated with a simulation example. Copyright (C) 2020 The Authors.
In control applications where finding a model of the plant is the most costly and time consuming task, Virtual reference Feedback Tuning (VRFT) represents a valid - purely data-driven - alternative for the design of m...
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In control applications where finding a model of the plant is the most costly and time consuming task, Virtual reference Feedback Tuning (VRFT) represents a valid - purely data-driven - alternative for the design of model reference controllers. However, the selection of a proper referencemodel within a model-free setting is known to be a critical task, with this model typically playing the role of a hyper-parameter. In this work, we extend the VRFT methodology to compute both a proper referencemodel and the corresponding optimal controller parameters from data by means of Particle Swarm optimization. The effectiveness of the proposed approach is illustrated on a benchmark simulation example.
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