In this paper, we develop a decentralized tracking control (DTC) strategy to stabilize a class of continuous-time nonlinear interconnected large-scale systems in the presence of unmatched external disturbances. This s...
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ISBN:
(纸本)9798350366907;9789887581581
In this paper, we develop a decentralized tracking control (DTC) strategy to stabilize a class of continuous-time nonlinear interconnected large-scale systems in the presence of unmatched external disturbances. This strategy is derived from an online learning approach of optimal control. Initially, the DTC problem is formulated to ensure both tracking control of isolated subsystems and the overall stability of the large-scale system. By combining the tracking error with the reference trajectory, some augmented systems are constructed and then the DTC design is transformed into an optimal control problem. Considering external disturbances, we formulated it as a two-player zero-sum game. Critic neural networks are utilized to solve the Hamilton-Jacobi-Isaacs equation. This approach allows for the estimation of Nash equilibrium solution that encompasses both the optimal control law and the worst-case disturbance law. Notably, a novel gradient descent strategy with momentum is established to tune the weights of the critic neural network for approximating the cost function better. Finally, an experimental simulation is provided to verify the effectiveness of the developed DTC scheme.
Ahstract- This paper presents a comprehensive investigation into the stability analysis and design conditions for quantizers to ensure the closed-loop stability of continuous-time linear quantized systems, under the c...
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ISBN:
(数字)9798331542726
ISBN:
(纸本)9798331542733
Ahstract- This paper presents a comprehensive investigation into the stability analysis and design conditions for quantizers to ensure the closed-loop stability of continuous-time linear quantized systems, under the conditions derived by the small-gain theorem. The study begins by deriving explicit bounds on the quantizer parameters required for maintaining system stability. Building on this foundation, an optimal controller is designed using the linear quadratic regulator (LQR) framework, providing an efficient data-driven control strategy. To further enhance the system's performance, an adaptivedynamicprogramming (ADP) algorithm, referred to as the hybrid iteration (HI) method, is developed. This algorithm effectively learns the optimal control policy by leveraging the trajectories of the quantized states and inputs, thereby addressing the challenges posed by quantization constraints. The proposed HI approach combines the advantages of adaptivelearning and optimization, making it well-suited for continuous-time systems with limited information. The simulation results confirm that the ADP approach with the provided conditions not only stabilizes the quantized system but also achieves optimal control performance under the specified quantization conditions. This study offers valuable insights and a robust methodological framework for addressing stability and control challenges, with insights to be expanded to continuous-time nonlinear quantized systems, with potential applications in various engineering domains, such as networked systems, robotics and autonomous systems.
With the increasing penetration of renewable energy and electric vehicles (EVs), the behavior of EVs' charging and discharging has shown great impact on the Micro Grid power load, motivating the development of Veh...
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Path tracking control of intelligent vehicles has to deal with the difficulties of model uncertainties and nonlinearities. As a class of adaptive optimal control methods, reinforcementlearning (RL) has received incre...
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Path tracking control of intelligent vehicles has to deal with the difficulties of model uncertainties and nonlinearities. As a class of adaptive optimal control methods, reinforcementlearning (RL) has received increasing attention in solving difficult control problems. However, feature representation and online learning ability are two major problems to be solved for learning control of uncertain dynamic systems. In this article, we propose a multi-kernel online RL approach for path tracking control of intelligent vehicles. In the proposed approach, a multiple kernel feature learning framework is designed for online learning control based on dual heuristic programming (DHP) and the new online learning control algorithm is called multi-kernel DHP (MKDHP). In MKDHP, instead of the expert knowledge for selecting and fine-tuning of a suitable kernel function, only a set of basic kernel functions is required to be predefined and the multi-kernel features can be learned for value function approximation in the critic. The simulation studies on path tracking control for intelligent vehicles have been conducted under S-curve and urban road conditions. The results demonstrated that compared with other typical path tracking controllers for intelligent vehicles, such as the linear quadratic regulator (LQR), the pure pursuit controller and the ribbon-based controller, the proposed multi-kernel learning controller can achieve better performance in terms of tracking precision and smoothness.
dynamic radio resource management (RRM) in wireless networks presents significant challenges, particularly in the context of Radio Access Network (RAN) slicing. This technology, crucial for catering to varying user re...
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ISBN:
(数字)9798350317640
ISBN:
(纸本)9798350317657
dynamic radio resource management (RRM) in wireless networks presents significant challenges, particularly in the context of Radio Access Network (RAN) slicing. This technology, crucial for catering to varying user requirements, often grapples with complex optimization scenarios. Existing reinforcementlearning (RL) approaches, while achieving good performance in RAN slicing, typically rely on online algorithms or behavior cloning. These methods necessitate either continuous environmental interactions or access to high-quality datasets, hindering their practical deployment. Towards addressing these limitations, this paper introduces offline RL to solving the RAN slicing problem, marking a significant shift toward more feasible and adaptive RRM methods. We demonstrate how offline RL can effectively learn near-optimal policies from sub-optimal datasets, a notable advancement over existing practices. Our research highlights the inherent flexibility of offline RL, showcasing its ability to adjust policy criteria without the need for additional environmental interactions. Furthermore, we present empirical evidence of the efficacy of offline RL in adapting to various service-level requirements, illustrating its potential in diverse RAN slicing scenarios.
In this paper, we propose an offline Deep adaptivedynamicprogramming (DADP) algorithm to improve the real-time performance of trajectory tracking control. Model Predictive Control (MPC) is an effective method for so...
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Today power converters, especially DC/DC converters, is of great importance in power electronics applications such as DC micro-grids (MGs). However, they have some limitation such as inability to handle constant power...
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ISBN:
(纸本)9781665404655
Today power converters, especially DC/DC converters, is of great importance in power electronics applications such as DC micro-grids (MGs). However, they have some limitation such as inability to handle constant power load (CPL) which results in instability problems in MGs. Thus, a controller with specific characters including, robustness and fast response to system dynamic is vital to address the unsteadiness. In this paper, an adaptive model prediction controller (AMPC) based on Deep reinforcementlearning (DRL) is developed to tackle the de-stabilization problem. In the proposed AMPC controller, the controlling signal coefficient in each variable operation point is regarded as the adjustable controller parameter and adaptively designed by the learning ability of the Deep Q- Network (DQN) strategy, leading to a robust controlling approach. We have shown that our suggested smart controller for DC/DC converters feeding CPLs is robust and fast in dynamic response.
Online video is the most popular Internet application. As the throughput would frequently change under different network conditions, it is important to adaptively select the proper bitrate and improve user's quali...
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ISBN:
(数字)9781665408769
ISBN:
(纸本)9781665408769
Online video is the most popular Internet application. As the throughput would frequently change under different network conditions, it is important to adaptively select the proper bitrate and improve user's quality of experience. In this paper, we propose a new DRL-based rate adaption algorithm for video streaming, which holistically captures user's preference of video contents, network throughput and buffer occupancy, and select the proper bitrate for video to improve the QoE. Specifically, we use 3D Convolutional neural (C3D) network to learn the spatio-temporal features, and implement the semantic analysis of videos. We also apply the Term Frequency-Inverse Document Frequency (TF-IDF) method to analyze the user's preference of different scene types, according to its viewing history. The dynamicadaptive streaming is formulated as a Markov Decision Process (MDP) problem, and use the Actor-Critic (A3C) algorithm to dynamically choose the optimal bitrate. As corroborated by simulations, our algorithm can accurately obtain the user's preference, keep the bitrate allocation consistent with the user's preference, and maintain video quality. Compared with the state-of-the-art Pensieve algorithm, our algorithm improves the average QoE by at least 12.5%. It also has a significant improvement over other baseline methods.
The enhancing size of uses on the cloud has improvised the requirement for dependable and elite execution network engineering in Datacentres. programming Defined Networking has worked on the adaptability, postponement...
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Exploiting and sharing unlicensed spectrum resources among cellular and WiFi networks is critical for the fifth-generation (5G) and beyond networks due to the severe spectrum shortage and huge traffic demands. While d...
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