We have previously showed that it is possible to achieve parameter identification of discrete-time structured uncertainties without requiring persistency of excitation when using Concurrent Learning. Instead, granted ...
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ISBN:
(纸本)9781509020522
We have previously showed that it is possible to achieve parameter identification of discrete-time structured uncertainties without requiring persistency of excitation when using Concurrent Learning. Instead, granted a less restrictive condition compared to that of persistency of excitation is verified, exponential convergence of parameter estimates to their true values ensues. The present study applies the previously developed discrete-time Concurrent Learning adaptation law within a control loop for discrete-time adaptive control of a discrete-time single-state plant containing structured uncertainties. Provided that the same condition as for the standalone estimation is met, we can prove exponential convergence of the tracking error and parameter error to zero without necessitating persistency of excitation.
In this paper, we revisit the Proportional-Integral-Derivative (PID) controller design for torque control of robotic manipulators, for which, appropriate tuning of the said controller could prove very burdensome, espe...
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In this paper, we revisit the Proportional-Integral-Derivative (PID) controller design for torque control of robotic manipulators, for which, appropriate tuning of the said controller could prove very burdensome, especially with increasing degrees-of-freedom (DOF) and/or when designing a Multi-Input Multi-Output (MIMO) PID controller. That is, when generating and tuning matrix P-I-D gains as opposed to single values, in order to take in account possible coupling effects between involved joints. We tackle both joint space and workspace PID control tuning problems for reference tracking from an optimization standpoint. Using a previously developed stable Adaptive Particle Swarm Optimizer, we are able to automatically and systematically tune P-I-D gains, be it as single gain values or gain matrices, while optimizing a cost or fitness function. The aforesaid cost function can be arranged to feature various aggregated performance measures, `normalized' so as to overcome differences in scale if any. Taking in account some practical limitations, a 2-DOF arm is used here as a case study. Numerical simulations are provided to substantiate the adequacy of our method.
In this paper, we consider the design of linear precoding in Multiple Input Multiple Output (MIMO) Power Line Communication (PLC) systems with finite-alphabet input. First, we derive mutual information of MIMO-PLC...
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In this paper, we consider the design of linear precoding in Multiple Input Multiple Output (MIMO) Power Line Communication (PLC) systems with finite-alphabet input. First, we derive mutual information of MIMO-PLC systems with impulsive noise. Considering the non-concavity of the objective function and the low-cost requirement of PLC systems, we choose the lower bound of the mutual information as the objective function. Subsequently, we propose a novel approach to design the precoding scheme to reduce computational complexity. Specifically, our work primarily includes the following two contributions: (1) We design the right unitary matrix that is a product of two fixed unitary matrices, which only depends on the modulation mode. Hence, the results can be saved and require less computations. (2) For the power allocation matrix, we first reduce the space of power allocation using constraints of the optimal power allocation policy. Consequently, we propose a non-linear search method to obtain the optimal power allocation in small space. In regards to the computational complexity of the analysis, we conclude that the proposed precoding matrix design scheme has low complexity and is easy to implement. Moreover, the numerical results are proven to demonstrate the performance of the proposed precoding design scheme.
This article covers research into cost reduction pathways for wave energy. The primary focus is the development and test of reliable and controllable power take-off (PTO) technology for oscillating water column conver...
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ISBN:
(纸本)9781509015276
This article covers research into cost reduction pathways for wave energy. The primary focus is the development and test of reliable and controllable power take-off (PTO) technology for oscillating water column converters (OWC). This is highly relevant to the ocean energy challenge. Extensive testing via data of the NEREIDA MOWC demonstration project, a breakwater wave plant located in the Basque location of Mutriku, using Wells turbines and Doubly Fed Induction (DFIG) generators is proposed to advance wave energy converter (WEC) devices. The control tests involve the integration of the PTO technology within a new variable controller for OWC wave power generation plants. Due to the nature of the control law, it allows monitoring faults caused by parametrical errors or system disturbances. The reliability tests by means of analysing the electrical signals have been conceived as conditioning monitoring, detection of mechanical faults and hardware damaging also fulfil a secondary aim for the reduction of maintenance cost and unscheduled downtime during operation of OWC plants.
A nonlinear controller with an inherent current-limiting capability is presented in this paper for different types of dc/dc power converters (boost, buck-boost). The proposed controller is based on the idea of applyin...
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ISBN:
(纸本)9781467383479
A nonlinear controller with an inherent current-limiting capability is presented in this paper for different types of dc/dc power converters (boost, buck-boost). The proposed controller is based on the idea of applying a dynamic virtual resistance in series with the inductor of the converter, which varies according to a nonlinear dynamical system. It is shown that the proposed approach acts independently from the converter parameters (inductance, capacitance) or the load and has a generic structure that can be used to achieve different regulation scenarios, e.g. voltage, current or power regulation. Based on the nonlinear model of the boost and the buck-boost converter, it is analytically proven that the inductor current remains always bounded below a given maximum value using input-to-state stability theory under a suitable choice of the controller parameters. Hence, the proposed control strategy offers an inherent protection property since the power of the converter is limited below a given value during transients or unrealistic power demands. Simulation results for both types of dc/dc converters are presented to verify the desired controller performance.
The origin of artificial intelligence is investigated, based on which the concepts of hybrid intelligence and parallel intelligence are presented. The paradigm shift in Intelligence indicates the ''new normal&...
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It is well known that in the case of the wastewater treatment bioprocesses, including anaerobic digestion process, usually, the complete knowledge of inputs is not available and therefore the implementation of the con...
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ISBN:
(纸本)9781509027217
It is well known that in the case of the wastewater treatment bioprocesses, including anaerobic digestion process, usually, the complete knowledge of inputs is not available and therefore the implementation of the control laws becomes a difficult problem. Therefore, in the sequel, for the above mentioned process two observer structures, able to estimate the unknown concentration of the input will be presented and analyzed. The paper deals with a comparative analysis of two types of observers: a stochastic one - Extended Kalman Filter and a deterministic one - Sliding Mode Observer. The estimation methods are analyzed in realistic frame taking into consideration the presence of the measurement noise but especially the usual variations in the case of an anaerobic digestion process, such as the variation of the maximum growth rate of the microorganisms. The effectiveness of the proposed observers has been validated by numerical simulations.
With the demands for shrinkage in feature sizes becoming more than ever, the extreme ultraviolet (EUV) lithography is the route pursued by the industry's leading authorities. Leaving the strive for achieving a hig...
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ISBN:
(纸本)9781509007561
With the demands for shrinkage in feature sizes becoming more than ever, the extreme ultraviolet (EUV) lithography is the route pursued by the industry's leading authorities. Leaving the strive for achieving a high source power aside, we hereby consider one of the most prominent disrupters of imaging quality: heating induced deformation of EUV reticles. To diminish its detrimental effect, a model-based prediction scheme is ideally used for steering certain actuators in an EUV tool. To enable such a solution in practice, computationally fast models are required. Along this direction, we use the POD and DEIM reduction methods on finite-element-based thermal and mechanical equations that relate to a recent geometry where cooling channels are placed nearby the reticle. Several sensor measurements approximating the initial thermal state of this model are treated as parameters, and two parameter-oriented reduction methods are designed which we refer to as local reduction (LR) and decomposed reduction (DR), respectively. The former builds local simplified models based on clustering of the parameters, whereas the latter expands the terms in the model so that the reduction is independent of the parameters. The results indicate that only two sensors are sufficient for an accurate characterization of the initial condition. Furthermore, among the evaluated methods, similar accuracies were observed for various tested scenarios, and therefore, it is concluded that the LR is more suitable for this application due to its less intricate structure.
Aims. Due to the fundamental importance of vortices on the photosphere, in this work we aim to fully automate the process of intensity vortex identification to facilitate a more robust statistical analysis of their pr...
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Constraint removal accelerates model predictive control by detecting inactive constraints at the yet unknown optimal solution and removing them from the online optimization problem. We show in this paper that the numb...
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Constraint removal accelerates model predictive control by detecting inactive constraints at the yet unknown optimal solution and removing them from the online optimization problem. We show in this paper that the number of removed constraints can be increased further by generalizing previously used inactivity criteria. The proposed generalization does not depend on information available at previous time steps, and consequently can also be applied at the initial state. In addition, we provide a detailed analysis of the computational complexity of the proposed variant and of existing constraint removal methods, applied to both active-set (AS) and interior-point (IP) solvers. Finally, we compare the different constraint removal variants in numerical experiments to corroborate the complexity analysis carried out, showing the greatest benefits of the proposed variant, especially with IP solvers.
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