Little has been done in the study of these intriguing questions, and I do not wish to give the impression that any extensive set of ideas exists that could be called a "theory." What is quite surprising, as ...
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Little has been done in the study of these intriguing questions, and I do not wish to give the impression that any extensive set of ideas exists that could be called a "theory." What is quite surprising, as far as the histories of science and philosophy are concerned, is that the major impetus for the fantastic growth of interest in brain processes, both psychological and physiological, has come from a device, a machine, the digital computer. In dealing with a human being and a human society, we enjoy the luxury of being irrational, illogical, inconsistent, and incomplete, and yet of coping. In operating a computer, we must meet the rigorous requirements for detailed instructions and absolute precision. If we understood the ability of the human mind to make effective decisions when confronted by complexity, uncertainty, and irrationality then we could use computers a million times more effectively than we do. Recognition of this fact has been a motivation for the spurt of research in the field of neurophysiology.
The extensive exploitation of traditional energy sources has significantly intensified pressure on environmental protection, making carbon emission reduction a critical strategy to mitigate global warming. Integrated ...
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The extensive exploitation of traditional energy sources has significantly intensified pressure on environmental protection, making carbon emission reduction a critical strategy to mitigate global warming. Integrated energy systems (IES), based on multi-energy coupling and complementarity, offer a promising approach to enhance renewable energy consumption, improve energy efficiency, and enable low-carbon operation. However, uncertainties in renewable energy generation and user demand present challenges for optimizing IES operation and scheduling. To address these, power-to-gas (P2G) technology, acting as both a renewable energy consumption and CO2 absorption facility, is incorporated into the IES to facilitate electricity-heat-gas cogeneration. Additionally, a carbon emission factor is integrated into the optimization objective, providing a comprehensive assessment of the system's economic and environmental benefits. A multi-objective optimization model is developed, with constraints on energy balance, equipment capacity, and energy storage or release. dynamic programming (DP) is applied to solve the model and obtain real-time system outputs for the next 24 h, balancing economic and environmental goals. Sensitivity analyses of energy storage capacity and carbon tax price are performed to explore their impacts on system scheduling optimization.
Incorporating a number of the author's recent ideas and examples, dynamic programming: Foundations and Principles, Second Edition presents a comprehensive and rigorous treatment of dynamic programming. The author ...
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ISBN:
(数字)9781420014631
ISBN:
(纸本)9780824740993
Incorporating a number of the author's recent ideas and examples, dynamic programming: Foundations and Principles, Second Edition presents a comprehensive and rigorous treatment of dynamic programming. The author emphasizes the crucial role that modeling plays in understanding this area. He also shows how Dijkstra's algorithm is an excellent exampl
作者：
Cabello, Julia GarciaCarbo-Garcia, S.Univ Granada
Andalusian Res Inst Data Sci & Computat Intellige Dept Appl Math Granada Spain Univ Granada
Andalusian Res Inst Data Sci & Computat Intellige Dept Comp Sci & Artificial Intelligence Granada Spain
In recent years, the architecture and structure of Deep Neural Networks (DNNs) have become progressively more complex in order to respond to the increasing complexity of real problems. A strategy to deal with this com...
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ISBN:
(纸本)9783031820724;9783031820731
In recent years, the architecture and structure of Deep Neural Networks (DNNs) have become progressively more complex in order to respond to the increasing complexity of real problems. A strategy to deal with this complexity when it affects training would be to partition DNN training in some way: for example, by distributing it among different components of a computer network. For this, training (which is in essence the minimization of the loss function) should be performed through separated "smaller pieces". This paper offers an alternative to the gradient-based DNN training from a dynamic programming (DP) point of view (DP is an optimisation methodology supported by the division of a complex problem into many problems of lower complexity). To do so, conditions which enable the DNN minimization algorithm to be solved under a DP perspective are studied here. In this line, in this work is proved that any artificial neural network ANN (and thus also DNNs) with monotonic activation is separable. Furthermore, whenever ANNs are considered as a dynamical system in the form of a network (known as coupled cell networks CCNs), we show that the transmission function is a separable function assuming that the activation is non-decreasing.
This article investigates the H-infinity optimal output-feedback control problem of heterogeneous multiagent systems. First, a hierarchical control scheme is designed to reduce the algorithm's complexity and the e...
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This article investigates the H-infinity optimal output-feedback control problem of heterogeneous multiagent systems. First, a hierarchical control scheme is designed to reduce the algorithm's complexity and the expected global performance constraints can be ensured by designing compensation input indicator. Second, a relaxation parameter is introduced to derive the optimal solution under output feedback. Additionally, a policy iteration algorithm and vectorization method are presented to determine local and collaborative control gains. This relaxation parameter serves to ease the design conditions for performance indicator. In addition, adaptive dynamic programming (ADP) is introduced and reversible datasets are designed to obtain optimal parameters with unknown drift dynamics. This design achieves model-free control of optimal output feedback for heterogeneous multiagent systems. Finally, the effectiveness of the control schemes is validated using F-16 aircraft and 4-wheel autonomous vehicles as examples.
With the increasing trend of smart electronic devices, interlinked industries face various challenges in meeting market demand. The demand for customized small-batch and multi-variety products with agility in customer...
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With the increasing trend of smart electronic devices, interlinked industries face various challenges in meeting market demand. The demand for customized small-batch and multi-variety products with agility in customer expectations makes the scheduling problem more complex. Batch-processing machine (BPM) scheduling refers to managing and organizing the execution of a group of tasks or jobs on a machine. BPM scheduling is a complex optimization problem critical in semiconductor production systems industries. A single BPM scheduling problem, considering multiple jobs with different sizes, release times, processing times, and due dates to minimize total earliness and tardiness, is studied in this paper. A mixed integer programming model is formulated to express the problem, including the related constraints. The self-adaptive hybrid differential evolution and tabu search (SDETS) algorithm with dynamic programming is proposed to solve the BPM-scheduling problem. The novel SDETS algorithm is embedded with four additional features: a) dynamic programming-based batch formation;b) right-left-shifting rules to identify the starting time of each batch;c) DE-self-adaptive mutation strategy to determine the job sequence and trade-off between exploration and exploitation;d) introduction of tabu-search to enhance the convergence rate. A comprehensive parametric tuning of the algorithms is conducted to optimize the performance and enhance the suitability for the specific problem set case instances. The findings suggest that the proposed algorithm surpasses the performance of the compared algorithms. Moreover, the SDETS method exhibits high convergence to find more precise and globally optimal solutions for large-scale problem instances, further emphasizing its practical applicability.
The stability and performance of ac-dc systems in grid modernization heavily rely on the rectification mode of grid-connected voltage-source converters (GC-VSCs). Being considered as the heart of the system, its impac...
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The stability and performance of ac-dc systems in grid modernization heavily rely on the rectification mode of grid-connected voltage-source converters (GC-VSCs). Being considered as the heart of the system, its impact is significant. The current-controlled GC-VSC based on the cascade control using a pulsewidth modulation approach is commonly deployed in the smart grid paradigm. This article discusses how the dynamics induced by that type of GC-VSC control structure can be regarded as singularly perturbed systems in modern ac-dc grids. As a result, it proposes a novel optimal control strategy for the voltage control problem with uncertain dynamics using reinforcement learning (RL) via the adaptive (or approximate) dynamic programming method and the singular perturbation theory (SPT). First, by means of SPT, the original optimal control problem is decomposed into two optimal problems with respect to an unknown slow time-scale subsystem and a known fast time-scale subsystem. Second, for the slow subsystem with unmeasurable states, an output-feedback-based off-policy RL algorithm with a guaranteed convergence is given in order to learn the optimal controller in terms of measurement data. Third, a composite controller is established in terms of the obtained fast-slow controllers;its optimality and closed-loop stability are rigorously proved. Unlike the direct full-order design, not only does the proposed decomposition composite design framework bypass the numerical stiffness, but it also alleviates the high dimensionality in the control synthesis. Comparative experiments using testing based on power hardware-in-the-loop simulations and rapid control prototyping methodology reveal the superiority and effectiveness of the proposed method.
This article investigates the event-triggered cooperative adaptive optimal output regulation problem for unknown multiagent systems (MASs) under switching network. To address communication disruptions between subsyste...
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This article investigates the event-triggered cooperative adaptive optimal output regulation problem for unknown multiagent systems (MASs) under switching network. To address communication disruptions between subsystems and the leader, a distributed observer is provided to estimate the reference signals. Without using system dynamics, an event-triggered mechanism is established to reduce computation and communication costs. Then, event-triggered adaptive optimal controllers are developed by using the available input/state data. By exploiting the Lyapunov stability theory and the method of input-to-state stability (ISS), rigorous stability analysis is conducted, and conditions for MASs to achieve the leader-to-formation stability (LFS) are provided. Additionally, the sensitivity of the suboptimality index to system parameters is analyzed. Finally, an application to cooperative adaptive cruise control (CACC) is presented to validate the proposed approach.
Infinite-horizon optimal control of constrained piecewise affine (PWA) systems has been approximately addressed by hybrid model predictive control (MPC), which, however, has computational limitations, both in offline ...
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Infinite-horizon optimal control of constrained piecewise affine (PWA) systems has been approximately addressed by hybrid model predictive control (MPC), which, however, has computational limitations, both in offline design and online implementation. In this article, we consider an alternative approach based on approximate dynamic programming (ADP), an important class of methods in reinforcement learning. We accommodate nonconvex union-of-polyhedra state constraints and linear input constraints into ADP by designing PWA penalty functions. PWA function approximation is used, which allows for a mixed-integer encoding to implement ADP. The main advantage of the proposed ADP method is its online computational efficiency. Particularly, we propose two control policies, which lead to solving a smaller-scale mixed-integer linear program than conventional hybrid MPC, or a single convex quadratic program, depending on whether the policy is implicitly determined online or explicitly computed offline. We characterize the stability and safety properties of the closed-loop systems, as well as the suboptimality of the proposed policies, by quantifying the approximation errors of value functions and policies. We also develop an offline mixed-integer-linear-programming-based method to certify the reliability of the proposed method. Simulation results on an inverted pendulum with elastic walls and on an adaptive cruise control problem validate the control performance in terms of constraint satisfaction and CPU time.
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