the Model Free Adaptive control (MFAC) control laws are attractive for applications involving complex plants. this is mainly due to the fact that no process modeling is required, which becomes more and more cumbersome...
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ISBN:
(纸本)9798350364309;9798350364293
the Model Free Adaptive control (MFAC) control laws are attractive for applications involving complex plants. this is mainly due to the fact that no process modeling is required, which becomes more and more cumbersome, but only a dynamic linearization through the concept of pseudo partial derivatives (PPD), which it introduces. Using PPD, the control law uses only the input and output data of the process to understand and control it. systems in the aerospace category are increasingly complex, using a multitude of sensors, information that can be used very easily for applications involving MFAC. the paper proposes a test of an MFAC law conjugated with Sliding Mode control (SMC), to test this type of law in comparison withthe classic MFAC-CFDL variant.
this paper proposes a fault tolerant control strategy for discrete-time descriptor systems using a reconfiguration-based technique. the proposed approach is designed to achieve faulttolerance capabilities without modi...
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ISBN:
(纸本)9798350364309;9798350364293
this paper proposes a fault tolerant control strategy for discrete-time descriptor systems using a reconfiguration-based technique. the proposed approach is designed to achieve faulttolerance capabilities without modifying the nominal controller of the descriptor system but adding a reconfiguration block including a virtual sensor and a virtual actuator. the implementation of the virtual actuator and virtual sensor is based on the system input-to-state stability criterion through linear matrix inequalities. Finally, the effectiveness of the proposed technique is illustrated on an academic simulation example.
this paper delves into optimal adaptive control for a plug flow reactor (PFR) partial differential equations (PDEs) model using the integral reinforcement learning (IRL) technique. Initially, it introduces a policy it...
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ISBN:
(纸本)9798350364309;9798350364293
this paper delves into optimal adaptive control for a plug flow reactor (PFR) partial differential equations (PDEs) model using the integral reinforcement learning (IRL) technique. Initially, it introduces a policy iterative algorithm designed to learn the solution of the corresponding matrix Riccati differential equation in real time. Notably, this method operates independently of explicit insight into the internal dynamics of the PFR process. Moreover, this paper establishes the convergence of the algorithm, contingent upon the initial action being stabilizing. Furthermore, an alternative algorithm is introduced to enhance the practical implementation of the IRL approach. the paper substantiates its findings through numerical simulations, demonstrating the efficacy of the developed algorithm.
the paper aims at bringing enhancements for the control of thermo-energetic processes by proposing anticipative action in order to reduce the perturbation effects in the process. Our contribution is organized in three...
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ISBN:
(纸本)9798350364309;9798350364293
the paper aims at bringing enhancements for the control of thermo-energetic processes by proposing anticipative action in order to reduce the perturbation effects in the process. Our contribution is organized in three connected sections. After a short introduction, in the first section, the dynamic model of the transfer energy between agent and product is estimated based on thermal balance equations, to explore the static and dynamic evolution of the process and to design the controlsystem algorithms. In the second section, the nominal digital system is designed based on dynamic model and the imposed performances for the close loop system are validated through simulation. In the final section, the cascade and feedforward structures are designed respectively. the performances based on anticipative action, guarantee the invariance of the system, being validated in a simulation environment. the simulation results confirm the effectiveness of this research and the possibility of transferring these results towards the industrial heat processes. Finally, the conclusions and perspectives are given.
Model predictive control has emerged as a prominent technique in control engineering due to its ability to handle constraints on bothcontrol signals and system states. this capability makes model predictive control a...
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ISBN:
(纸本)9798350364309;9798350364293
Model predictive control has emerged as a prominent technique in control engineering due to its ability to handle constraints on bothcontrol signals and system states. this capability makes model predictive control a powerful tool, particularly for complex systems with operational limitations. However, a major challenge associated with model predictive control is the "curse of dimensionality" arising from the constrained optimization problem solved at each time step. this problem becomes computationally expensive as the system dimension increases. this study proposes an accelerated model predictive control algorithm that addresses the curse of dimensionality. We achieve this by solving an equivalent suboptimal model predictive control problem within a reduceddimensional subspace. the subspace is efficiently calculated using singular value decomposition of the Hessian matrix associated withthe quadratic cost function. An adaptation law dynamically determines the subspace size, balancing accuracy and computational efficiency of the model predictive controlcontroller.
this paper presents an algorithmic approach for segmenting news broadcasts produced by television stations. the proposed approach uses advanced signal processing techniques to extract and classify audio segments from ...
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ISBN:
(纸本)9798350364309;9798350364293
this paper presents an algorithmic approach for segmenting news broadcasts produced by television stations. the proposed approach uses advanced signal processing techniques to extract and classify audio segments from Romanian TV news broadcasts. By using derivative of 1-dimensional Gaussian filter and binary classification models, this paper aims to automatically identify and classify news segments into categories such as studio news, field news, commercials and music breaks. the primary objective of this paper is to develop a segmentation methodology optimized for reduced computational memory and quick processing. By leveraging efficient computational techniques, the news segmentation can be performed in a timely manner without compromising accuracy. this optimization is crucial for real-time or near-real-time applications within TV networks, facilitating tasks such as content monitoring and analysis.
the paper develops a comprehensive framework for analyzing the invariance of polyhedral sets with respect to the trajectories of nonlinear continuous-time dynamic systems. the fundamental contribution consists in stat...
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ISBN:
(纸本)9798350364309;9798350364293
the paper develops a comprehensive framework for analyzing the invariance of polyhedral sets with respect to the trajectories of nonlinear continuous-time dynamic systems. the fundamental contribution consists in stating and proving a general theorem, which takes into account nonlinear time-varying systems and, respectively, polyhedral sets with arbitrary time-dependence. To the intrinsic mathematical value of this result, the work adds the significance of some problems with specific contexts, which can be derived as particular cases of our general setting. At the same time, it is shown that some of these particular cases are correlated with specific invariance properties, which were studied individually, through separate researches. the specificity of the properties refers both to characteristics of dynamics and to subclasses of polyhedral sets.
Cloud computing can be an useful tool when dealing with spatially distributed resources, which need to be simultaneously managed. In this work, a simulation environment is developed using OpenStack to apply a Distribu...
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ISBN:
(纸本)9798350364309;9798350364293
Cloud computing can be an useful tool when dealing with spatially distributed resources, which need to be simultaneously managed. In this work, a simulation environment is developed using OpenStack to apply a Distributed Model Predictive control strategy for a platooning application consisting of multiple autonomous mobile robots. the idea is to develop the entire algorithm by taking advantage of the possibility of creating virtual machines. thus, each autonomous mobile robot is treated as an individual agent and is modeled and controlled on a virtual machine, whereas the communication between the agents is performed in the cloud, using the TCP/IP protocol standard. the simulation results illustrate that the platooning application deployed in OpenStack outperforms the simulation performed on a single computer.
this paper presents the performance analysis and the most significant design decisions taken to improve the runtime performance of a quality-diversity volume rendering framework, focusing on using parallel and distrib...
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ISBN:
(纸本)9798350364309;9798350364293
this paper presents the performance analysis and the most significant design decisions taken to improve the runtime performance of a quality-diversity volume rendering framework, focusing on using parallel and distributed computing to optimize specific stages within the framework pipeline. After considering the benefits and trade-offs of several aspects like minimization of execution time, portability and hardware independence, we developed a hybrid CPU-GPU parallel computing model that increased the overall efficiency. Using this model, we achieved significant improvement in runtime speed for critical components of the framework compared to the sequential implementation. Additionally, this model can be applied in a wide range of scenarios, where the performance improvements provided by GPU processing can be complemented by CPU parallelization, with better runtime results.
the Artificial Pancreas Problem (APP) offers a potential framework for control Engineering studies, specifically in the field of continuous monitoring and actuation to control glucose levels. the models that give a sa...
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ISBN:
(纸本)9798350364309;9798350364293
the Artificial Pancreas Problem (APP) offers a potential framework for control Engineering studies, specifically in the field of continuous monitoring and actuation to control glucose levels. the models that give a satisfactory level of accuracy are nonlinear by nature however, the standard approach in linear control is to find a linear representation of the model. the current paper proposes a comparison between standard linearization and linearization via the Koopman Operator for an input-affine nonlinear model from insulin intake to glucose level. Each model also has an additive disturbance component. To account for it, the current paper proposes a method of modeling the disturbance based on Gauss Processes. For a meaningful comparison between the considered linear matrix inequality-based controllers (LMI) and linear-quadratic regulators (LQR), the paper introduces the term Glucose Absolute Error (GAE) as an error index adapted for the Insulin-Glucose system.
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