This work presents a coalitional model predictive controller for collaborative vehicle platoons. The overall system is modeled as a string of locally controlled vehicles that can share data through a wireless communic...
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This work presents a coalitional model predictive controller for collaborative vehicle platoons. The overall system is modeled as a string of locally controlled vehicles that can share data through a wireless communication network. The vehicles can dynamically form disjoint groups that coordinate their actions, i.e., the so-called coalitions . The control goals are keeping a desired reference distance between all vehicles while allowing for occasional switching of the communication topology. Likewise, the presented controller promotes a string-stable evolution of the platoon system. Numerical results are provided to illustrate the proposed approach.
Dear Editor,This letter is concerned with developing meta-learning models for fast,stable,and effective few-shot learning across tasks over a few training ***,deep and reinforcement learning(RL)is widely used in auton...
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Dear Editor,This letter is concerned with developing meta-learning models for fast,stable,and effective few-shot learning across tasks over a few training ***,deep and reinforcement learning(RL)is widely used in autonomous intelligent systems(e.g.,target recognition[1],path planning[2],and robot control[3],[4]).
Computer aided control in biomedical applications is gaining more and more popularity due to numerous research studies that have proven the efficiency of automatic control over manual dosing, which is highly susceptib...
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ISBN:
(数字)9798350382655
ISBN:
(纸本)9798350382662
Computer aided control in biomedical applications is gaining more and more popularity due to numerous research studies that have proven the efficiency of automatic control over manual dosing, which is highly susceptible to human errors. Optimal drug dosing is best achieved using automatic control, which triggers important benefits in terms of both costs and patient side-effects. However, mathematical models for patients are highly susceptible to large modeling uncertainty. A predictive control algorithm is designed in this paper for optimal multidrug control of hemodynamic variables. Improved closed loop performance is obtained compared to similar control strategies, for
$\pm 30\%$
modeling uncertainty. The simulation results demonstrate that predictive control is a feasible solution for optimal drug dosing. An analysis of the closed loop performance for significant patient variability shows that controllers tuned using a nominal patient model often fail to achieve desired robustness. To limit the effect of modeling uncertainty, the prediction model should be updated using an online identification tool to extract patient features.
This paper addresses the path planning problem for unmanned aerial vehicle (UAV), where a UAV serves as a messenger to periodically traverse over road-constrained ground vehicles (GVs) to relay information. The GVs ma...
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In this work we make use of knowledge from clinical practice to deliver a trusted environment for feedback control of co-administration of two drugs to induce depth of hypnosis during general anesthesia. The clinical ...
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In this work we make use of knowledge from clinical practice to deliver a trusted environment for feedback control of co-administration of two drugs to induce depth of hypnosis during general anesthesia. The clinical data we have provides valuable insight into the ratio used to determine the Propofol and Remifentanil infusion rates by means of specifying their corresponding effect site concentrations. A feedback closed loop with a fixed parameter PID controller tuned on population dynamics is used. To account the strong variations in gain of the system (patient) we propose an online identification of the dose-effect response from clinical data available during clinical protocol. Theoretical analysis is provided to justify the approach and simulations with real data from patient are given to support the theoretical insight. Limitations of this approach are discussed as to justify why ratio based co-administration is used in practice solely during the induction phase of general anesthesia.
Conventional programming of explicit control code is unsuitable for flexible and collaborative production systems. A model-based approach, which focuses on defining capabilities of a system, instead of specifying how ...
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Worldwide competition and diverse demand of customers pose great challenges to manufacturing enterprises. How to organize production to achieve high productivity and low cost becomes their primary task. In the mean ti...
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Worldwide competition and diverse demand of customers pose great challenges to manufacturing enterprises. How to organize production to achieve high productivity and low cost becomes their primary task. In the mean time, the rapid pace of technology innovation has contributed to the development of new types of flexible automation. Hence, increasing manufacturing enterprises convert to multi-product and small-batch production, a manufacturing strategy that brings increased output, reduced costs, and quick response to the market. A distinctive feature of small-batch production is that the system operates mainly in the transient states. Transient states may have a significant impact on manufacturing systems. It is therefore necessary to estimate the dynamic performance of systems. As the assembly system is a typical class of production systems, in this paper, we focus on the problem of dynamic performance prediction of the assembly systems that produce small batches of different types of products. And the system is assumed to be characterized with Bernoulli reliability machines, finite buffers, and changeovers. A mathematical model based on Markovian analysis is first derived and then, the analytical formulas for performance evaluation of three-machine assembly systems are given. Moreover, a novel approach based on decomposition and aggregation is proposed to predict dynamic performance of large-scale assembly systems that consist of multiple component lines and additional processing machines located downstream of the assemble machine. The proposed approach is validated to be highly accurate and computationally efficient when compared to Monte Carlo simulation.
During the coal seam drilling process, the drill string is subject to compressive deformation, compounded by unpredictable variations in formation hardness and borehole wall friction, leading to challenges in maintain...
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The HVAC system is one of the major power consuming equipment in buildings. Considering the effective setpoint temperature of each zone can increase efficiency in managing power usage in buildings, leading to the oper...
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ISBN:
(数字)9798350381559
ISBN:
(纸本)9798350381566
The HVAC system is one of the major power consuming equipment in buildings. Considering the effective setpoint temperature of each zone can increase efficiency in managing power usage in buildings, leading to the operating cost reduction. This paper presents the design of supervisory control (SC) for multi-zone HVAC systems that aims to minimize the total operating cost (TOC) and the thermal comfort cost (TCC). To minimize the TOC and TCC, both objectives are normalized and combined as a quadratic programming (QP) problem which can be efficiently solved. To achieve the design objective, two methods of SC are developed for multi-zone, namely, centralized SC and decentralized SC. We apply standard QP and sparse QP solvers using interior point method. The numerical results reveal that centralized SC performs better according to a tradeoff curve which TOC and TCC are lower. While both solvers give the same results, sparse solver can provide the solutions faster than interior point method. This paper demonstrates that centralized control is appropriate for implementation in multi-zones buildings and the sparse solver proves to be more suitable in the context of sparse QP problem.
This article considers the problem of prescribed-time stabilization for a class of uncertain high-order nonlinear systems (i.e., systems in the p-normal form) with a pre-specified asymmetric output constraint. A core ...
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